Download M C A

MICHAEL
CHRISTOPHER
ANDERSON
+44 (0) 1223 355294
[email protected]
MRC Cognition and Brain Sciences Unit
15 Chaucer Road, Cambridge
CB2 7EF England
United Kingdom
Senior scientist and programme leader at the MRC Cognition and Brain Sciences Unit in
Cambridge, studying the cognitive and neural mechanisms underlying forgetting in long-term
memory.
Professional Positions
2009 -
Senior Scientist and Programme Leader, MRC Cognition and Brain Sciences
Unit, Cambridge UK
2007-2009
Chair in Cognitive Neuroscience, University of St. Andrews, Scotland
2008
Professor of Cognitive Neuroscience. Department of Psychology,
University of Oregon
2001-2007
Associate Professor of Cognitive Neuroscience. Department of Psychology,
University of Oregon
2002-2003
Visiting Scholar, Department of Psychology, Stanford University
1995-2001
Assistant Professor of Cognitive Neuroscience. Department of Psychology,
University of Oregon
1994-1995.
McDonnell-Pew Postdoctoral Fellow in Cognitive Neuroscience. Department
of Psychology, University of California, Berkeley.
1991-1992.
Consultant, Rand Corporation. Santa Monica, California.
1987-1988.
Associate Scientist. Rockwell International Laboratory for Artificial
Intelligence. Palo Alto, California.
M. Anderson
Vita
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Academic History
1994-1995
Postdoctoral fellow, Department of Psychology, University of California,
Berkeley
1988-1994
Ph.D., Psychology, University of California, Los Angeles, June 1994.
Dissertation: Inhibitory Control and the Mechanisms of Episodic Forgetting.
Advisors: Robert Bjork, Keith Holyoak
1982-1986
B.A., Cognitive Science, University of Rochester, May 1986. Honors
in Cognitive Science. Advisor: Michael Tanenhaus
Fellowships, Scholarships, and Internships
Fellow of the Association for Psychological Science
2009-Present
Fellow of the Psychonomics Society
2013-Present
Posner / Boies Fellowship.
2004
Visiting Scholar Fellowship, British Psychological Society.
1997
McDonnell-Pew Summer Institute in Cognitive Neuroscience, Dartmouth, NH.
1996
McDonnell-Pew post-doctoral fellowship in cognitive neuroscience, (Berkeley). 1994-1995
University of California, graduate research award.
1992-1993
Summer Internship, RAND corporation.
1991
Regent's Fellowship, University of California, Los Angeles.
1988-1989
Alumni Regional Scholarship, University of Rochester.
1982-1986
Professional Memberships
American Association for the Advancement of Science; American Psychological Association;
Association for Psychological Science; Cognitive Neuroscience Society; Memory Disorders
Research Society; Psychonomic Society; Society for Neuroscience. British
Neuropsychological Society.
Professional Leadership and Service
PROFESSIONAL SOCIETIES
Governing Board, Psychonomics Society
2009--2014
M. Anderson
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CONSULTING EDITORSHIPS:
Memory and Cognition,
Perspectives on Psychological Science,
Journal of Experimental Psychology: Learning, Memory & Cognition
Psychological Science
Cognitive Neuroscience
AD HOC REFEREEING:
Acta Psychological
Alcoholism
Applied Cognitive Psychology
Behavioral and Brain Sciences
Behavior Research and Therapy
Behavioral Neuroscience
Brain Stimulation
British Journal of Psychology
Cerebral Cortex
Cognition
Cognitive, Affective, and Behavioral Neuroscience
Cognition and Emotion
Cognitive Processes
Cognitive Psychology
Canadian Journal of Experimental Psychology
Developmental Science
Experimental Psychology
Hippocampus
Journal of Cognitive Neuroscience
Journal of Abnormal Psychology
Journal of Experimental Psychology: Applied
Journal of Experimental Psychology: Human Perception and Performance
Journal of Experimental Psychology: Learning, Memory and Cognition
Journal of Experiment Psychology: General
Journal of Neuroscience
Journal of Personality and Social Psychology
Journal of Memory and Language
Journal of Neuroscience
Memory and Cognition
Memory
Nature
Neuroimage
Neuropsychology
Neuropsychologia
PNAS
Psychology and Aging
2002 - 2004
2005 - 2007
2006 – 2012
2014 –Present
2015--Present
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Psychological Bulletin
Psychonomic Bulletin and Review
Psychological Review
Psychological Science
Quarterly Journal of Experimental Psychology
Science
Trends in Cognitive Science
DEPARTMENTAL SERVICE:
Unit Management Committee, Cognition and Brain Sciences Unit
University of Cambridge Psychology Research Ethics Committee
Knowledge Transfer Committee, Cognition and Brain Sciences Unit
fMRI Scientific Committee, University of St. Andrews
Executive Committee, Lewis Center for Neuroimaging.
Colloquium Committee, University of Oregon
Attneave Memorial Lecture Committee, University of Oregon
Graduate Admissions Committee Chair, University of Oregon
Participant Panel Review Group, MRC CBU
Chaucer Club Organizing Committee
Teaching Experience
UNIVERSITY OF ST. ANDREWS (2 years):
Introduction to Cognitive Neuroscience
Psychology in Everyday Life
UNIVERSITY OF OREGON (12 years):
Learning and Memory
Amnesia and the Medial-Temporal Lobe Memory System
Mind and Brain
Cognitive Science Laboratory
Memory and Frontal Lobe Function
Advanced Cognitive Neuroscience
Issues in Psychology: Cognitive Psychology
Introduction to Experimental Psychology
UNIVERSITY OF CALIFORNIA, LOS ANGELES (5 years):
Introduction to Cognitive Psychology (teaching assistant)
Cognitive Psychology Laboratory (teaching assistant)
Cognitive Science Laboratory (teaching assistant)
Basic Research Methods in Psychology Laboratory (teaching assistant)
2014-present
2012-present
2014-present
2007-2009
2002-2004
2005-2007
2005-2007
2009-2010
2010-2011
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UNIVERSITY OF ROCHESTER (1 year):
Introduction to Cognitive Science (teaching assistant)
Advisees and Postdoctoral Fellows
MASTERS AND PH.D. STUDENTS
Geeta Shivde
Theodore Bell
Julia Rheinholz
Lisa Stryker
Leilani Goodmon
Benjamin Levy
Justin Hulbert
M.A., Ph.D.
M.A., Ph.D.
M.A.
M.A.
M.A., Ph.D
M.A., Ph.D.
M.A., Ph.D
Yuhua Guo
Shanti Shanker
M.A., Ph.D.
M.A., Ph.D.
Berit Brumerloh
M.A.
Tanya Wen
Ph.D.
POST-DOCTORAL FELLOWS
Sarah Johnson
Roland Benoit
Pierre Gagnepain
Maria Wimber
Ana Catarino
Taylor Schmitz
Jonathan Fawcett
(graduated, UO, associate professor)
(graduated, UO, post-doc)
(graduated, UO, industry)
(graduated, UO, industry)
(graduated, USF, assistant professor)
(graduated UO, asstnt professor, UCSF)
(graduated, University of
Cambridge, post-doc, Princeton)
(current, University of Cambridge).
(current, University of Bangor, cosupervised with Oliver Turnbull)
(current, University of Cambridge
supervised with Trevor Robbins)
(upcoming, University of Cambridge)
(UO, assistant professor)
(Cambridge, post-doc, Harvard)
(Cambridge, PI, INSERM)
(Cambridge, lecturer, University of
Birmingham)
(Cambridge, current)
(Cambridge, current)
(Cambridge, current)
Grants
Michael C. Anderson, P.I.
Inhibitory Control and Frontal Lobe Function.
McDonnell-Pew Individual Investigator Small Grant in Cognitive Neuroscience.
1995-1998
($60,000)
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Michael C. Anderson, P.I.
Inhibitory Control as an Approach to Motivated Forgetting.
University of Oregon, Summer Research Award. Office of Research and Faculty
Development. 1999
($4000)
Michael C. Anderson, P.I.
Inhibitory Control and the Mechanisms of Forgetting.
National Institute of Mental Health.
2000-2005
($750,000)
Michael C. Anderson, Co-Investigator
Acquisition of a 1.5 Tesla (T) Magnetic Resonance Imaging (MRI) System for Research and
Research Training in Cognitive Neuroscience
National Science Foundation.
2000-2003
($929,890)
Michael C. Anderson, Co-Investigator
Acquisition of a Magnetic Resonance Imaging (MRI) System for Research on the Neural Basis
of Human Cognition.
Department of Defense Telemedicine and Advanced Technology Division.
2000-present ($3,000,000)
Michael C. Anderson, Subcontract PI (Mara Mather, PI)
The Impact of Emotion Regulation on Cognition in Age
National Institute of Aging.
12/01/03-11/30/08
($1,084,048)
Michael C. Anderson, P.I.
Suppressing Unwanted Memories: Neural Systems of Intrusion Control
Oregon Health Sciences University School of Medicine, Medical Research Council.
1/05 –12/31/06
($30, 000)
Michael C. Anderson, Training Faculty
Systems Physiology Training Grant
National Institute of General Medical Sciences
Training Grant, 7/05-6/10
($2,100,000).
Michael C. Anderson, P.I.
Neural Mechanisms Underlying the Control of Reflexive Orienting in Human Memory
National Science Foundation
April 1st, 2007 – March 31st, 2012 ($500,000)
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Michael C. Anderson, P.I.
Gating Conscious Memories Through Hippocampal Modulation.
Mind Science Foundation, Tom Slick Award
June 15th, 2010-June 14th, 2011. ($15,000)
Michael C. Anderson, co-investigator
Acquisition of a 7-t Magnetic Resonance Imaging (MRI) System for Research on Dementia
Medical Research Council
Infrastructure Grant, 3/2014 (21,00,000 pounds)
Michael C. Anderson, PI
Broken resilience: How stroke affects brain mechanisms for controlling unwanted thoughts
and undermines survivors’ peace of mind.
Stroke Association, 3/2015 (209,895 pounds)
Media Coverage
See http://www.memorycontrol.net/ for PDFs or links to articles, video, and radio
GENERAL RESEARCH PROGRAMME
Scientific American Mind. Article on my work entitled “Trying to Forget” by Ingrid
Wickelgren in a special issue on “Adaptive Forgetting” January 1st, 2012
National Public Radio. Special episode of “On Point” dedicated to adaptive
forgetting, on which I was one of three guests (hour long programme). January
2012
FLEGAL & ANDERSON (2009). GOLF PAPER
BBC1 Television
BBC1 radio
Live at five, BBC Radio
BBC World Service
Kingdom News Radio
Midday Report (South Africa)
Radio 4, BBC
Citytalk, with Gary Quinn
TalkSport radio
Scotsman
Daily Telegraph
LiveScience
Daily Mail
ANDERSON ET AL. (2004). SCIENCE
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National and Local Press.
Newsweek article
New York Times
CNN (Dr. Sanjay Gupta)
NPR (San Francisco Affiliate)
Washington Post
San Francisco Chronicle
Science News
Oregonian
Register-Guard (AP Wire).
National Review
Dana Foundation Newsletter
KEZI TV (Water cooler segment)
KUGN, KLCC, KLCC Sunday at Noon call in show
KPNW News, Boston and San Francisco TV News
International Press.
BBC News
The Independent (London)
The Guardian (London)
Le Monde (Paris)
The Japan Times
Hindustan Times (India)
The Toronto Globe and Mail
China Daily
El Pais (A Spanish Newspaper)
Galileu (A Brazilian Newspaper)
The Scottsman News
Ottawa Citizen
ABC News Radio (Australian Broadcasting Company)
Special Categories: Discover Magazine; Movie: Eternal Sunshine of the Spotless
Mind: Invited to promote movie.
Professional Organizations.
APA Monitor. Article covering my work on memory suppression.
ANDERSON & GREEN (2001). NATURE
US News and World Report article
CNN
ABC News
Los Angeles Times
Washington Post
The Dailey Telegraph
Le Monde
Oregonian
Register-Guard
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NPR
KLCC
Science News
The New Scientist
Lancet
ABC News (Australian Broadcasting System)
German, Brazilian and Argentinean Newspapers
Books
See http://www.psypress.com/books/details/9781848721845/ to browse the book.
Baddeley, A., Eysenck, M.W., & Anderson, M.C. (2009). Memory. Psychology Press.
Translated into Spanish, Portuguese, Italian, Russian, Hungarian
Baddeley, A., Eysenck, M.W., & Anderson, M.C. (2014). Memory, 2nd edition. Psychology
Press.
Publications
Publications can be downloaded at http://www.memorycontrol.net/publications.htm
Anderson, M.C., Bjork, R.A., & Bjork, E.L. (1994). Remembering can cause forgetting:
Retrieval dynamics in long-term memory. Journal of Experimental Psychology: Learning,
Memory and Cognition, 20, 1063-1087.
Anderson, M.C., & Bjork, R.A. (1994). Mechanisms of inhibition in long-term memory: A
new taxonomy. In D. Dagenbach & T. Carr (Eds.), Inhibitory Processes in Attention,
Memory and Language (pp 265-326). San Diego: Academic Press.
Anderson, M.C., & Spellman, B.A. (1995). On the status of inhibitory mechanisms in
cognition: Memory retrieval as a model case. Psychological Review, 102, 68-100.
Anderson, M.C., & Neely, J. (1996). Interference and inhibition in memory retrieval. In
E.L. Bjork and R.A. Bjork (Eds.), Memory. A volume in the Handbook of Perception and
Cognition (pp 237-313) New York: Academic Press.
Bjork, E.L., Bjork, R.A., & Anderson, M.C. (1997). Varieties of goal directed forgetting. In
J.M. Golding and C.M. McCleod (Eds), Directed Forgetting: Interdisciplinary Approaches.
Lawrence Erlbaum Associates.
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Anderson, M.C., & McCulloch, K.C. (1999). Integration as a general boundary condition on
retrieval-induced forgetting. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 25, 608-629.
Anderson, M.C., Bjork, E.L., & Bjork, R.A. (2000). Retrieval-induced forgetting: Evidence
for a recall-specific mechanism. Psychonomic Bulletin and Review, 7, 522-530.
Anderson, M.C., Green, C., & McCulloch, K.C. (2000). Similarity and inhibition in longterm memory: Evidence for a two-factor model. Journal of Experimental Psychology:
Learning, Memory and Cognition, 26, 1141-1159.
Anderson, M.C. (2001). Active Forgetting: Evidence for functional inhibition as a source of
memory failure. Journal of Aggression, Maltreatment, and Trauma, 4(2). Monograph 8.
Reprinted in J. Freyd and A. DePrince (Eds.), Trauma and Cognitive Science: A Meeting of
Minds, Science, and Human Experience.
Shivde, G., & Anderson, M.C. (2001). The role of inhibition in meaning selection: Insights
from retrieval-induced forgetting. In D. Gorfein (Ed.), On the Consequences of Meaning
Selection: Perspectives on Resolving Lexical Ambiguity. Washington: APA Books.
Anderson, M.C., & Bell, T. (2001). Forgetting our facts: The role of inhibitory processes in
the loss of propositional knowledge. Journal of Experimental Psychology: General, 130.
544-570.
Anderson, M.C. & Green, C. (2001). Suppressing unwanted memories by executive control.
Nature, v410, n 6826, 131-134.
Levy, B. & Anderson, M.C. (2002). Inhibitory processes and the control of memory
retrieval. Trends in Cognitive Science, 6, 299-305.
Anderson, M.C., & Levy, B. (2002). Repression can and should be studied empirically.
Trends in Cognitive Science, 6, 502-503.
Anderson, M.C. (2003). Rethinking interference theory: Executive control and the
mechanisms of forgetting. Invited featured paper, Journal of Memory and Language, 49,
415-445.
Johnson, S.K., & Anderson, M.C. (2004). The role of inhibitory control in forgetting
semantic knowledge. Psychological Science. 15, 448-453.
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Anderson, M.C., Ochsner, K., Kuhl, B., Cooper, J., Robertson, E., Gabrieli, S.W., Glover,
G., & Gabrieli, J.D.E. (2004). Neural systems underlying the suppression of unwanted
memories. Science, V 303, 232-235.
Anderson, M.C. (2005). The role of inhibitory control in forgetting unwanted memories:
A consideration of three methods. In C. MacLeod & B. Uttl (Eds.), Dynamic Cognitive
Processes (pp. 159-190). Tokyo: Springer-Verlag.
Anderson, M.C. (2006). Repression: A cognitive neuroscience approach. In M. Mancia
(Ed.). Neuroscience and Psychoanalysis (pp. 327-350). Milan: Spring.
Anderson, M.C., & Levy, B.J. (2006). Encouraging the nascent cognitive neuroscience of
repression. Behavioral and Brain Sciences, 29, 511-513.
Levy, B.J., McVeigh, N., Marful, A., & Anderson, M.C. (2007). Inhibiting your native
language: The role of retrieval-induced forgetting during second-language acquisition.
Psychological Science, 18, 29-34.
Anderson, M.C. ( 2007). Inhibition in long-term memory. In Y. Dudai, R. Roediger, E.
Tulving, and S. Fitzpatrick (Eds.), The Science of Memory: Concepts. New York: Oxford
University Press.
Anderson, M.C. & Levy, B.J. (2007). Theoretical issues in inhibition: insights from research
on human memory. In D. Gorfein & C. MacLeod (Eds.), The Role of Inhibitory Processes in
Cognition.
Hulbert, J., & Anderson, M.C. (2008). The role of inhibition in learning. In Benjamin, A. S.,
de Belle, S., Etnyre, B., & Polk, T. (2008). Human Learning: Biology, Brain, and
Neuroscience, Holland: Elsevier. 7-20.
Levy, B.J., & Anderson, M.C. (2008). Individual differences in suppressing unwanted
memories: the executive deficit hypothesis. Acta Psychologica, 127, 623-635.
Flegal, K., & Anderson, M.C. (2008). Overthinking skilled motor performance: Or why
those who teach, can’t do. Psychonomic Bulletin and Review, 15, 927-932.
Anderson, M.C., & Weaver, C. (2009). Inhibitory control over action and memory. In L.
Squire (Ed.). The Encyclopedia of Neuroscience. Elsevier.
Levy, B.J., & Anderson, M.C. (2009). The control of mnemonic awareness. Encyclopedia
of Consciousness. William P. Banks (Ed.). Elsevier.
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Garcia-Bajos, E., Migueles, M., & Anderson, M.C. (2009). Script knowledge
modulates retrieval-induced forgetting for eyewitness events. Memory, 17, 92-103
Anderson, M.C., & Levy, B (2009). Suppressing unwanted memories. Current Directions
in Psychological Science. 18 (4) 189-194.
Paz-Alonso, P.M., Ghetti, S., Matlen, B.J., Anderson, M.C., & Bunge, S.A. (2009)
Memory suppression is an active process that improves over childhood. Frontiers in
Neuroscience, 3, 1-6
Anderson, M.C., & Levy, B.J. (2010). On the relation between inhibition and interference in
cognition. In A. Benjamin (Ed). Successful Remembering and Successful Forgetting: Essays
in Honor of Robert A. Bjork, American Psychological Association.
Anderson, M.C., Reinholz, J., & Kuhl, B. & Mayr, U. (2011). Intentional suppression of
unwanted memories grows more difficult as we age. Psychology and Aging. 26, 397-405.
Goodmon, L., & Anderson, M.C. (2011). Semantic integration as a boundary condition on
inhibitory processes in episodic retrieval. Journal of Experimental Psychology: Learning,
Memory and Cognition.
Anderson, M.C., & Huddleston, E. (2011). Towards a Cognitive and Neurobiological
Model of Motivated Forgetting. In Belli, R. F. (Ed.), True and false recovered
memories: Toward a reconciliation of the debate. Vol. 58: Nebraska Symposium on
Motivation. New York: Springer.
Kuhl, B., & Anderson, M.C. (2011). More is not always better: Paradoxical effects of
repetition on semantic accessibility. Psychonomic Bulletin and Review.
Shivde, G.S., & Anderson, M.C. (2011). On the existence of semantic working memory:
Evidence for direct semantic maintenance. Journal of Experimental Psychology: Learning,
Memory and Cognition.
Hulbert, M.C., Shivde, G.S., & Anderson, M.C. (2012). Evidence against associative
blocking as a cause of cue-independent retrieval-induced forgetting.
Experimental
Psychology.
Huddleston, E., & Anderson, M.C. (2012). Reassessing critiques of the independent probe
method for studying inhibition. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 38, 1408-1418
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Benoit, R., & Anderson, M.C. (2012). Opposing mechanisms support the voluntary
forgetting of unwanted memories. Neuron, 76, 450-460.
Levy, B.J., & Anderson, M.C. (2012). Purging of memories from conscious awareness
tracked in the human brain. Journal of Neuroscience. 32, 16785-16794.
Weller, P. D., Anderson, M. C., Gómez-Ariza, C. J., & Bajo, M. T. (2013). On the status of
cue independence as a criterion for memory inhibition: Evidence against the covert blocking
hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(4),
1232.
Bergström, Z. M., Anderson, M. C., Buda, M., Simons, J. S., & Richardson-Klavehn, A.
(2013). Intentional retrieval suppression can conceal guilty knowledge in ERP memory
detection tests. Biological Psychology, 94(1), 1–11.
Kevin van Schie , Elke Geraerts & Michael C. Anderson (2013): Emotional and
nonemotional memories are suppressible under direct suppression instructions, Cognition &
Emotion, 1122-1131.
Paz-Alonso, P. M., Bunge, S. A., Anderson, M. C., & Ghetti, S. (2013). Strength of coupling
within a mnemonic control network differentiates those who can and cannot suppress memory
retrieval. The Journal of Neuroscience, 33(11), 5017-5026.
Rae, C. L., Hughes, L. E., Weaver, C., Anderson, M. C., & Rowe, J. B. (2014). Selection and
stopping in voluntary action: A meta-analysis and combined fMRI study. Neuroimage, 86,
381-391.
Gagnepain, P., Henson, R. N., & Anderson, M. C. (2014). Suppressing unwanted memories
reduces their unconscious influence via targeted cortical inhibition. Proceedings of the
National Academy of Sciences, 111(13), E1310-E1319.
Küpper, C. S., Benoit, R. G., Dalgleish, T., & Anderson, M. C. (2014). Direct suppression as
a mechanism for controlling unpleasant memories in daily life. Journal of Experimental
Psychology: General, 143(4), 1443-1449.
Anderson, M. C., & Hanslmayr, S. (2014). Neural mechanisms of motivated forgetting.
Trends in cognitive sciences, 18(6), 279-292.
Schilling, C. J., Storm, B. C., & Anderson, M. C. (2014). Examining the costs and benefits of
inhibition in memory retrieval. Cognition, 133(2), 358-370.
Fawcett, J. M., Benoit, R. G., Gagnepain, P., Salman, A., Bartholdy, S., Bradley, C., Chan,
D.K.Y., Roche, A., Brewin, C.R., & Anderson, M. C. (2014). The origins of repetitive
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thought in rumination: Separating cognitive style from deficits in inhibitory control over
memory. Journal of Behavior Therapy and Experimental Psychiatry.
Benoit, R. G., Hulbert, J. C., Huddleston, E., & Anderson, M. C. (2014). Adaptive top–down
suppression of hippocampal activity and the purging of intrusive memories from
consciousness. Journal of Cognitive Neuroscience, 27, 96-111
Rae, C.L., Hughes, L.E., Anderson, M.C., & Rowe, J.B. (2014). The prefrontal cortex
achieves inhibitory control by facilitating subcortical motor pathway connectivity, Journal of
Neuroscience, 35, 786-794.
Murray, B.D., Anderson, M.C., & Kensinger, E.A. (2015). Older adults can suppress
unwanted memories when given an appropriate strategy. Psychology and Aging
Wimber, M., Alink, R., Charest, I, Kriegskorte, N., & Anderson, M.C. (2015). Retrieval
induces adaptive forgetting of competing memories via cortical pattern suppression. Nature
Neuroscience.
Catarino, A., Kuepper, C., Werner-Seidler, A., Dalgleish, T., & Anderson, M.C. (2015).
Failing to forget: Inhibitory control deficits compromise memory suppression in posttraumatic stress disorder. Psychological Science.
Manuscripts in Progress
Hulbert, J.C., Henson, R.N., & Anderson, M.C. (under revision). Inducing amnesia through
cognitive control.
Strebb, M., Mecklinger, A., Anderson, M.C., Lass-Hennemann, J., & Michael, T.
(submitted). Forget about it: Memory control ability predicts reduced PTSD symptoms after
an analogue trauma
Yang, T., Lei, X., & Anderson, M.C. (submitted). Decreased inhibitory control of negative
information in directed forgetting. Biological Psychology.
Greve, A., Cooper, E., Anderson, M.C. & Henson, R.N. (submitted). Does prediction error
drive one-shot declarative learning?
van Schie, K., & Anderson, M.C. (in preparation). Fatigue compromises the control of
intrusive memories.
Gagnepain, P., Hulbert, J.C., & Anderson, M.C. (in preparation). Parallel regulation of
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memory and emotion supports the suppression of intrusive memories
Weller, P., Bajo, T., & Anderson, M.C. (in preparation). Controlling mnemonic distraction
improves perceptual focus.
Benoit, R., Davies, D., & Anderson, M.C. (in preparation). Forgetting the future:
Attenuating real-life fears by suppressing prospective simulations
Hulbert, J.C., Anderson, M.C., & Kuhl, B.A. (in preparation). What doesn’t kill you makes
you stronger: enhanced forgetting of unwanted memories with traumatic experience.
Hulbert, J.C., Bross, K., & Anderson, M.C. (in preparation). Inhibitory control contributes
to retroactive interference.
Hotta, C., Kawaguchi, J., & Anderson, M.C. (in preparation). Intentional suppression, not
distraction, leads to long-term forgetting.
Schmitz, T., Correira, M., Ferreira, C., Prescott, A., & Anderson, M.C. (in preparation).
GABA-ergic inhibition underlies inhibitory control over thought.
Beckinschtein, P., Weisstraub, N., Gallo. F., Renner, M., & Anderson, M.C. (in preparation).
A species-general, retrieval-specific mechanism of adaptive forgetting.
Levy, B.J. Gagenpain, P., Abdullaev, Y., Posner, M.I., & Anderson, M.C. (in preparation). A
common neural system mediates reflexive attentional capture by both percepts and unwanted
memories
Keynotes, Invited Addresses, Symposia, Colloquia
INVITED ADDRESSES AND KEYNOTES
• Keynote Speaker, Japanese Psychological Association, Nagoya Japan, September,
2015
•
British Academy / British Psychological Society Invited Lecture, British
Academy, London, September, 2015.
•
Keynote Speaker, Memory and motivation: A reappraisal of the recovered/false
memory debate: the 50th annual Nebraska Symposium on Motivation. Cognitive
and Neural Systems Underlying the Control of Intrusive Memories.University of
Nebraska, Lincoln, April, 2010.
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•
Congreso de la Sociedad Española de Psicología Experimental, Keynote address.
Cognitive and Neural Systems Underlying the Control of Intrusive Memories. San
Sebastian, Spain, April, 2008.
•
World Congress on Cognitive Behavioral Therapy. Keynote Address. Cognitive
and Neural Systems Underlying the Control of Intrusive Memories: Implications
for Theories of Thought Suppression. Barcelona, July, 2007.
•
North Carolina Cognition Conference. Keynote Address. Suppressing unwanted
memories: Cognitive and neural systems. February, 2006.
•
Kyoto University, Invited talk, Individual differences in the suppression of
unwanted memories. Inhibitory Processes in the Mind. January, 2006.
•
Japanese Society of Cognitive Psychology, Kanazawa, Japan. Keynote address.
Suppressing unwanted memories: cognitive and neural mechanisms. May, 2005.
•
Tagung Experimentell Arbeitender Psychologen, Regensburg Germany.
Suppressing unwanted memories: Cognitive and neural systems. April, 2005.
•
Southeastern Psychology Association, Nashville, Tennessee. Suppressing
unwanted memories: Cognitive and neural mechanisms. April, 2005.
•
Western Psychological Association, Portland, Oregon. Suppressing unwanted
memories: Cognitive and neural mechanisms. April, 2005
•
The Place of Inhibitory Processes in Cognition. Arlington Texas. Suppressing
unwanted memories: Cognitive and neural mechanisms. March, 2005.
•
International Festival of Science, Genoa Italy. Suppressing unwanted memories:
Cognitive and neural mechanisms. November, 2004.
•
American Psychological Association, Honolulu, Hawaii. Suppressing unwanted
memories: Cognitive and neural mechanisms. July, 2004.
•
Fifth Tsukuba International Conference on Memory: Dynamic Cognitive
Processes, Tsukuba, Japan. Cognitive and neural mechanisms underlying the
suppression of unwanted memories. March, 2004.
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•
International Workshop on Constructive Memory, Sophia, Bulgaria. Inhibitory
control and the regulation of awareness. July, 2003.
•
Banff Annual Seminar in Cognitive Science, Banff, Canada. Suppressing
unwanted memories by executive control. May, 2003.
•
American Psychological Society, Atlanta Georgia.
Suppressing unwanted
memories: Cognitive and neural mechanisms. May, 2003.
•
Armadillo Conference. San Antonio, Texas. Suppressing unwanted memories by
executive control. April, 2002.
•
Cognitive Aging Conference, Georgia Tech, Atlanta. On measuring inhibitory
deficits: Insights from the study of inhibitory processes in human memory. May,
2002.
•
American Psychological Association, San Francisco, CA. Suppressing unwanted
memories by executive control. August, 2001.
•
International Conference on Memory, Valencia, Spain. Keynote address.
Suppressing unwanted memories by executive control. July, 2001.
•
British Psychological Society, Bristol, England. Retrieval Processes and the
mechanisms of episodic forgetting. September, 1997
SYMPOSIA, INVITED TALKS, & WORKSHOPS
• Organizer of symposium on Interference and Inhibitory Control functions of the
Prefrontal Cortex for the Annual Conference of the Memory Disorders Research
Society, San Francisco, California. November, 1997.
•
Organizer of symposium on Inhibitory Processes in Memory Retrieval for the
Annual Conference of the Society for Applied Research in Memory and
Cognition. Boulder Colorado. July, 1999.
•
Invited speaker at Trauma and Cognitive Science, Eugene, Oregon. Active
forgetting: Functional inhibition as a source of memory failure. July, 1998.
•
Invited speaker at the “Presidential symposium on inhibition and recovery in
human and animal memory,” at the Western Psychological Association
conference, Los Angeles. Inhibitory processes in the management and control of
human memory. April, 1999.
M. Anderson
Vita
18
•
Invited speaker at “Inhibitory Processes in Lexical Ambiguity,” Arlington Texas.
Inhibitory processes in lexical ambiguity resolution: A retrieval-induced forgetting
approach. March, 2000.
•
Invited speaker at symposium on “Disruptions in Memory for Trauma” at the
annual conference of the Western Psychological Association, Portland, Oregon.
Active Forgetting. April, 2000.
•
Organizer of a symposium on the Executive Control of Memory for the Annual
Conference of The memory disorders research society, Boston MA. October,
2001.
•
Organizer of a symposium on Inhibitory Processes in Memory for the International
Conference on Memory. Valencia, Spain. July, 2001.
•
Co-organizer (with Liz Phelps) of a symposium on Memory disruption:
suppression and reconsolidation for the Memory Disorders Research Society.
October, 2004.
•
Co-Organizer (with Joe LeDoux) of the symposium on Memory Regulation:
Inhibition, Extinction, and Reconsolidation for Cognitive Neuroscience Society.
April, 2005.
•
Co-Organizer (with Lili Sahakyan) of the symposium Inhibitory Processes in
Human Memory for the International Conference on Memory, Sydney, Australia,
July 2006
•
Invited speaker at the symposium The hidden side of cognition: Laboratory studies
revealing overlooked aspects of everyday experience, for the Western
Psychological Association, May, 2007, Vancouver, British Columbia.
•
Organizer of the symposium Cognitive and Neural Mechanisms Underlying the
Suppression of Intrusive Memories for the American Psychological Society,
Washington D.C., May 2007
•
Invited speaker at panel debate on When is thought suppression unhealthy? World
Congress on Cognitive Behavioral Therapy, Barcelona Spain, July 2007
•
Organizer of the symposium Mechanisms underlying the suppression of unwanted
memories and their clinical implications for the 36th annual meeting of the British
M. Anderson
Vita
19
Association for Behavioural and Cognitive Psychotherapies. Edinburgh, Scotland,
July, 2008.
•
Invited Keynote Speaker, SINAPSE induction meeting, the Byrn, Scotland.
•
Invited Speaker, Successful remembering and successful forgetting: A Festschrift
in Honor or Robert Bjork. January, 2009, Los Angeles.
•
Co-organizer with Heather Berlin, Neuroscience meets Psychoanalysis: The neural
basis of suppression, repression and dissociation. Annual meeting of the
Association for the Scientific Study of Consciousness, Toronto, Canada, June,
2010
•
Co-organizer with Heather Berlin, Neuroscience meets Psychoanalysis: The neural
basis of suppression, repression and dissociation. Towards a Science of
Consciousness, Tucson, Arizona, April, 2010.
•
Invited Speaker at the symposium When top down rules brain function, for the
British Neuropsychological Society. September, 2010, London.
•
Invited Speaker at the symposium “Memory and the Brain” at the University
Medical Center at the University of Groningen. May, 2011.
•
Invited Speaker at the workshop “To Head or to Heed? Cross-Disciplinary
Approaches to Impulsivity and the Inhibition of Thought and Action,” University
of Amsterdam. June, 2011.
•
Invited Speaker at “Prefrontal Inhibition of Memory: Too much and too little”
International Conference on Memory, York, England, August, 2011
•
Invited Speaker at “Executive Control of Attention, Thought, and Action”
Experimental Psychology Society, London, England, January, 2014
•
Co-organizer with David Badre, “Interactions between the prefrontal cortex and
the medial-temporal lobes supporting the control of memory retrieval”. Memory
Disorders Research Society, Austin Texas, September, 2014
•
Co-organizer with David Badre, “Interactions between the prefrontal cortex and
the medial-temporal lobes supporting the control of memory retrieval”. Cognitive
Neuroscience Society, San Francisco, March, 2015
M. Anderson
Vita
20
•
Invited Speaker at “Studies of instructed memory suppression in concealed
information tests, with autonomic, behavioral, fMRI, and brain wave responses.”
Association for Psychological Science, May 2015.
•
Invited Speaker at “Adaptive Minds: Neural and Environmental Constraints on
Learning and Memory” a meeting of the Sino-German International Research
Training Group (IRTG). Lake Bostalsee, Germany, October, 2015.
DEPARTMENTAL COLLOQUIA
• Department of Psychology, Pomona College, 1993
• Department of Psychology, University of North Carolina, Greensboro, 1995
• Department of Psychology, University of Pennsylvania. 1995
• Department of Psychology, Columbia University, 1995
• Department of Psychology, New York University, 1995
• Department of Psychology, University of California, San Diego, 1995
• Department of Psychology, University of Oregon, Eugene, 1995
• Department of Psychology, University of California, Berkeley,1995
• Center for Neuroscience, University of California, Davis,1995
• Cognition and Brain Sciences Unit, Cambridge University, September 1997
• Department of Psychology, Cognitive Area, Stanford University, 1997
• Department of Psychology, University of Iowa, November 2001
• Department of Psychology, University of Pennsylvania, January 2002
• Eunice Kennedy Shriver Center, Boston, MA, May 2002
• Department of Psychology, Stanford University, Oct, 2002
• Department of Psychology, UC Santa Cruz, January, 2003
• Department of Psychology, UC Davis, May, 2003
• Department of Psychology, Princeton University, May 2003
• Department of Psychology, UC Berkeley, September, 2003
• Department of Psychology, Washington University in St. Louis, October, 2003
• Department of Psychology, UCLA, May, 2004
• Department of Psychology, University of Nagoya, Nagoya, Japan, 2005
• Department of Psychology, University of Granada, Spain, December, 2006
• Department of Psychology, St. Andrews University, Scotland, April, 2007
• Department of Psychology, Warwick University, England, January, 2008
• Department of Psychology, Brown University, March 2008
• Department of Psychology, Goldsmith’s College, London, April, 2008
• Department of Psychology, York University, May 2008
• MRC Cognition and Brain Sciences Unit, Cambridge England, July, 2008
• Department of Psychology, University of Bangor, August, 2008
• Department of Psychology, University of Dundee, October, 2008
M. Anderson
Vita
21
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Department of Psychology, Northwestern University, November, 2008
Cognitive Neurology and Alzheimer's Disease Center, Chicago, November 2008
Department of Psychology, University of Granada, Spain, December, 2008
Department of Psychology, University College, London, January, 2009
Department of Neurology and Stereotactic Surgery, University of Magdeburg,
Germany, Feb, 2009
MRC Cognition and Brain Sciences Unit, Cambridge England, Jan, 2010
Department of Neurology, Addenbrookes Hospital, Cambridge England, Oct, 2010
Department of Psychology, SUNY Buffalo, Buffalo, New York, Nov, 2010
Department of Psychology, Lund University, Lund, Sweden, June 2011
Department of Psychology, University College, London, Feb, 2012
Center for Cognitive and Neural Systems, Edinburgh, Scotland, March, 2012
Department of Psychology, University of Hertfordshire, March, 2012
Department of Psychology, University of Konstanz, Konstanz, Germany, April,
2012
Department of Psychology, University of Essex, March, 2014
Department of Psychology, University of Granada, April, 2014
Department of Psychology, UCLA, November, 2014
Department of Psychology, University of Saarland, Germany, December, 2014
Department of Psychology, University of Trier, Germany, April, 2015
Published Abstracts and Presentations
Anderson, M.C. (2014). A supramodal inhibitory control process supports the inhibition of
memories and actions.
Rae, C., Hughes, L.E., Weaver, C., Anderson, M.C., * Rowe, J.B. (2012). Selection and
inhibition: two sides of voluntary action. Human Brain Mapping.
Anderson, M.C. (2011). Towards a neurocognitive model of memory suppression. Talk
presented at the 5th International Conference on Memory, York, England, August, 2011
Benoit, R., & Anderson, M.C. (2011). Two mechanisms of voluntary memory suppression.
Talk presented at the 5th International Conference on Memory, York, England, August, 2011
Levy, B.J., & Anderson, M.C. (2011). Failed memory suppression reflects attentional capture
within memory. Talk presented at the 5th International Conference on Memory, York,
England, August, 2011Levy, B.J. Depression
M. Anderson
Vita
22
Levy, B.J., Hertel, P., Joormann, J., Anderson, M.C., Hamilton, P., Wagner, A., & Gotlib, I.
(2011).
Depression and the cognitive and neural consequences of intentional memory
suppression. Talk presented at the 5th International Conference on Memory, York, England,
August, 2011.
Gagnepain, P., & Anderson, M.C. (2011) I remember, therefore I forget: Bringing memory
inhibition to life using the SenseCam. Talk presented at the 5th International Conference on
Memory, York, England, August, 2011
Hulbert, J.C., & Anderson, M.C. (2011). The ups and downs of hippocampal modulation:
Mnemonic consequences of memory control. Talk presented at the 5th International
Conference on Memory, York, England, August, 2011
Anderson, MC. (2010, October). Hippocampal modulation as a mechanism of memory
control. Annual Meeting of the Memory Disorders Research Society, Chicago, Illinois.
Bergstrom, Z., Anderson, M.C., Buda, M., Simons, J., & Richardson-Klavehn, A. (2010).
Concealing guilty knowledge by retrieval suppression in an ERP memory detection test.
Human Brain Mapping, Barcelona Spain.
Hulbert, J.C. & Anderson, M.C. (2010). Impairing recognition memory via hippocampal
modulation. Poster presentation at Recognition memory mechanisms: From proteins to
paitents, Bristol, England.
Bergstrom, Z., Anderson, M.C., Buda, M., Simons, J., & Richardson-Klavehn, A. (2010).
Concealing guilty knowledge by retrieval suppression in an ERP memory detection test.
Poster presentation at Recognition memory mechanisms: From proteins to paitents, Bristol,
England.
Anderson, M.C. (2010, January). Hippocampal modulation as a mechanism of memory
control. Cambridge Neurosciences, Cambridge England.
Weaver, C., & Anderson, M.C. (2010). Inhibitory aftereffects dissociate different types
of motor stopping. Poster presented at Cambridge Neurosciences. Cambridge, England.
Hulbert, J.C., & Anderson, M.C. (2010). Opening the Amnesic Window through
Hippocampal Modulation. Poster presented at Cambridge Neurosciences. Cambridge,
England.
Paz-Alonso, P.M., Ghetti, S., Anderson, M.C., & Bunge, S.A. (2010, March). Memory
suppression develops over childhood. Poster to be presented at the The Frontal Lobes, 20th
Annual Rotman Research Institute Conference, Rottman Institute, Toronto, Canada.
M. Anderson
Vita
23
Levy, B.J., & Anderson, M.C. (2009, October). Multiple routes to achieving inhibitory
control over memory. Poster presented at the 39th annual meeting of the Society for
Neuroscience, Chicago, IL.
Clark, C.M., Scott, N.W., Levy, B.J., & Anderson, M.C. (2009, September). Measuring the
intrusion of unwanted memories and the regulation of mnemonic awareness. Poster presented
at the annual meeting of the British Association for Cognitive Neuroscience, London, UK.
Berstrom, Z., Anderson, M.C., & Richardson-Klavehn, A. (2009, September). Concealing
guilty knowledge by retrieval suppression in an ERP memory detection test. Talk presented at
the annual meeting of the British Association for Cognitive Neuroscience, London, UK.
Levy, B.J., & Anderson, M.C. (2009, August). Executive control and the regulation of
intrusive memories. Paper presented at the 6th annual meeting of the Bay Area Memory
Meeting, UCSF, CA.
Weaver, C., & Anderson, M.C. (2009). All stopping is not the same: the role of inhibition in
action control. Presentation at the Annual Meeting of the Psychonomic Society. Boston, MA.
Weaver, C., Amiruddin, A., and Anderson, M.C. (2009). Is inhibition involved in motor
stopping? Poster presentation at the British Association for Cognitive Neuroscience
Conference. London, England.
Weaver, C., & Anderson, M.C. (2009). Do we inhibit responses to stop them? Assessing
the role of inhibition in motor stopping tasks. Poster at the 31st Annual Conference of the
Cognitive Science Society. Amsterdam, The Netherlands.
Weaver, C., & Anderson, M.C. (2009). Is all stopping the same? Assessing inhibition in
action control. Association for Psychological Science Convention. San Francisco, CA.
Weaver, C., & Anderson, M.C. (2009). Is inhibition involved in the stop-signal and go/nogo
tasks? Poster at the Annual Meeting of the Cognitive Neuroscience Society. San Francisco,
CA.
Huddleston, E. & Anderson, M.C. (2009, September). Impairment of visual memories caused
by suppression of memory retrieval. Poster presented at the Annual Meeting of the British
Association for Cognitive Neuroscience. London, U.K.
Huddleston, E. & Anderson, M.C. (2009, August). A novel paradigm for exploring the neural
mechanisms of visual memory suppression. Poster presented at the 31st Annual Conference of
the Cognitive Science Society. Amsterdam, The Netherlands.
M. Anderson
Vita
24
Huddleston, E. & Anderson, M.C. (2009, May). Retrieval suppression causes forgetting of
visual memories. Poster presented at 21st Annual Convention of the American Psychological
Society. San Francisco, CA.
Huddleston, E. & Anderson, M.C. (2009, March). Suppressing visual memories by executive
control. Poster presented at 16th Annual Meeting of the Cognitive Neuroscience Society, San
Franscico, CA.
Hulbert, J.C., & Anderson M.C. (2009). Inducing amnesia through cognitive control: The
hippocampal modulation (H.M.) paradigm. Poster presentation at the Annual Meeting of the
Cognitive Science Society, Amsterdam, Netherlands.
Hulbert, J.C., & Anderson M.C. (2009). Intentional suppression and the amnesic penumbra.
Poster presentation at the Association for Psychological Science 21st Annual Convention, San
Francisco, California.
Hulbert, J.C., & Anderson M.C. (2009). Resolving retrieval conflict through inhibition. Poster
presentation at the 16th Annual Cognitive Neuroscience Society Meeting, San Francisco,
California.
Hulbert, J.C., & Anderson M.C. (2008). Inducing amnesia through executive control. Poster
presentation at the Neuroscience & Cognitive Control (NeuroCog) Meeting, Ghent, Belgium.
Hulbert, J.C. & Anderson, M.C. (2008). Cue-independent impairment in retrieval-induced
forgetting reflects inhibition, not associative interference. Talk presented by second author at
the 49th Annual Meeting of the Psychonomic Society, Chicago, Illinois.
Paz-Alonso, P.M., Ghetti, S., Matlen, B.J., Anderson, M.C., & Bunge, S.A. (2008, May).
Neurocognitive Development of Memory Suppression. Poster to be presented at the 15th
annual meeting of the Cognitive Neuroscience Society. San Francisco.
Weaver, C., & Anderson, M.C. (2008). Bridging the gap between inhibition of thoughts
and actions. Talk at the 2008 SINAPSE Induction. Glenesk, Scotland.
Anderson, M.C. (2008). Cognitive and neural systems underlying the suppression of
unwanted memories. Talk at the 2008 SINAPSE Induction. Glenesk, Scotland.
Levy, B.J., & Anderson, M.C. (November, 2007). Inhibitory control of intrusive memories:
Neural correlates of successful and failed suppression. Society for Neuroscience Abstracts.
Paz-Alonso, P.M., Ghetti, S., Souza, M.J., Anderson, M.C., Bunge, S.A. (November, 2007).
M. Anderson
Vita
25
Neurodevelopmental Correlates
Neuroscience Abstracts.
of
Suppressing
Unwanted
Memories.
Society
for
Levy, B.J., & Anderson, M.C. (November, 2007). Inhibiting intrusive memories: Neural
mechanisms of successful and failed control over the retrieval of unwanted memories. Paper
to be presented at the annual meeting of the Psychonomics Society, Long-Beach, CA.
Hulbert, J.C., Huddleston, E.J., Anderson, M.C. (2007, May). Suppressing retrieval of an
unwanted memory also disrupts new episodic encoding for events close in time. Poster
presented at the 14th annual meeting of the Cognitive Neuroscience Society, New York, NY.
Levy, B.J., & Anderson, M.C. (2007, May). When unwanted memories intrude: Reactive
control over stopping retrieval. Poster presented at the 14th annual meeting of the Cognitive
Neuroscience Society, New York, NY.
Paz-Alonso, P.M., Ghetti, S., Matlen, B.J., Anderson, M.C., & Bunge, S.A. (2007, May).
Suppressing unwanted memories: A developmental study. Poster presented at the 14th annual
meeting of the Cognitive Neuroscience Society, New York, NY.
Weaver, L.C., & Anderson, M.C. (2007, May). Isolating Inhibition in the Stopping of Motor
Responses. Poster presented at the 14th annual meeting of the Cognitive Neuroscience
Society, New York, NY.
Bell, T., & Anderson (2006, November). Working memory capacity predicts individual
differences in the ability to suppress unwanted memories. Paper presented at the annual
meeting of the Psychonomics Society, Houston, Texas.
Anderson, M.C. (2006, November). On the existence of a working memory system for the
active maintenance of semantics. Invited talk given at Rice University, department of
psychology.
Anderson, M.C. (2006, July). Individual differences in the ability to control unwanted
memories. Talk given at the International Conference on Memory, Sydney, Australia.
Goodmon, L., & Anderson, M.C. (2006, July). Protecting against inhibition: The influence of
episodic and semantic integration on retrieval-induced forgetting. Talk given at the
International Conference on Memory, Sydney, Australia.
Levy, B.J. & Anderson, M.C. (2006, July). Proactive and Reactive Control of Memory
Suppression. Talk given at the International Conference on Memory, Sydney, Australia.
M. Anderson
Vita
26
Hulbert, J., Huddleston, E., & Anderson, M.C. (2006). The hippocampal modulation (HM)
effect: Behavioral induction of amnesia. Poster presented at the 18th annual meeting of the
American Psychological Society, New York, New York.
Goodmon, L., & Anderson, M.C. (November, 2005). Protecting against inhibition: The
influence of episodic and semantic integration on retrieval-induced forgetting. Paper
presented at the annual meeting of the Psychonomics Society, Toronto, Canada.
Anderson, M.C. (April, 2005). Executive control and the regulation of declarative memory.
Talk presented at the annual meeting of the Cognitive Neuroscience Society. New York, New
York.
Goodmon, L., & Anderson, M.C. (March, 2005). The influence of pre-existing memories on
retrieval-induced forgetting. Poster presented at the conference “The place of inhibitory
processes in cognition,” Arlington Texas.
Levy, B.J., & Anderson, M.C. (March, 2005). The role of inhibition in second language
acquisition. Poster presented at the conference “The place of inhibitory processes in
cognition,” Arlington Texas.
Anderson, M.C. (November, 2004).. Inhibitory control and the suppression of unpleasant
events. Abstracts of the Psychonomics Society, Volume 9.
Tabor, J. A. & Anderson, M.C. (2004). The role of inhibition in retroactive interference.
Poster presented at the 16th annual meeting of the American Psychological Society, Chicago,
IL.
Levy, B. & Anderson, M.C. (2004). Inhibitory control of semantic retrieval. Poster presented
at the 16th annual meeting of the American Psychological Society, Chicago, IL.
Johnson, S. K. Levy, B., & Anderson, M. C. (2004). A good night’s sleep moderates the
inhibitory control of memory. Poster presented at the 16th annual meeting of the American
Psychological Society, Chicago, IL.
Anderson, M.C., Ochsner, K., Gabrieli, J., Kuhl, B., Cooper, J., Robertson, E., & Glover, G.
(November, 2003). Neural systems underlying the suppression of unwanted memories.
Abstracts of the Psychonomics Society, Volume 8.
Anderson, M.C., Ochsner, K., Gabrieli, J., Kuhl, B., Cooper, J., Robertson, E., & Glover, G.
(November, 2003). Neural systems underlying the suppression of unwanted memories.
Society for Neuroscience Abstracts.
M. Anderson
Vita
27
Anderson, M.C. (August, 2003). Neural systems underlying the suppression of unwanted
memories. Cognitive Science Association for Interdisciplinary Learning. Hood River,
Oregon.
Levy, B., & Anderson, M.C. (2002). Inhibitory Control over semantic memory retrieval
Poster presented at the 15th annual meeting of the American Psychological Society, New
Orleans, LA.
Johnson, S., Levy, B., & Anderson, M.C. (2002). Retrieval Induced forgetting in semantic
memory. Poster presented at the 15th annual meeting of the American Psychological Society,
Atlanta, GA.
Tabor, J., & Anderson, M.C. (2002). Inhibitory Processes in Retroactive Interference. Poster
presented at the 15th annual meeting of the American Psychological Society, Atlanta, GA.
Levy, B., & Anderson, M.C. (2002). Executive control and the mechanisms of encoding.
Abstracts of the Psychonomic Society, Volume 7
Kuhl, B., & Anderson, M.C. (2002). Inhibitory processes in semantic satiation. Poster
presented at the 82nd annual meeting of the Western Psychological Association, Irvine, CA.
Kuhl, B., & Anderson, M.C. (2002). Inhibitory mechanisms underlying semantic satiation.
Poster presented at the 14th annual meeting of the American Psychological Society, New
Orleans, LA.
Levy, B., & Anderson, M.C. (2002). The role of inhibitory control during encoding. Poster
presented at the 14th annual meeting of the American Psychological Society, New Orleans,
LA.
Johnson, S., & Anderson, M.C. (2002). Retrieval Induced forgetting in semantic memory.
Poster presented at the 14th annual meeting of the American Psychological Society, New
Orleans, LA.
Anderson, M.C., & Shivde, G. (November, 2001). Maintaining meaning: Evidence for
semantic working memory. Abstracts of the Psychonomic Society, Volume 6
Anderson, M.C. (October, 2001). Stopping recollection: Inhibitory control processes in
declarative memory retrieval. Paper given at the annual meeting of the Memory Disorders
Research Society, Boston, MA.
M. Anderson
Vita
28
Anderson, M.C. (July, 2001). Important lessons in the study of inhibitory processes. Paper
presented at a symposium on Inhibitory Processes in Memory, International conference on
memory, Valencia Spain.
Anderson, M.C. (July, 2001). Inhibitory Control and the Regulation of Awareness. Paper
presented at the annual meeting of the Cognitive Science Association for Interdisciplinary
Learning, Hood River, Oregon.
Levy, B., & Anderson, M.C. (June, 2001).
Inhibitory processes in learning new
associations. Poster presented at the annual meeting of the Cognitive Science Association for
Interdisciplinary Learning, Hood River, Oregon.
Levy, B., & Anderson, M.C. (June, 2001). Regulation of conscious awareness: further
evidence for a direct suppression mechanism. Poster to be presented at the xth annual
meeting of the American Psychological Society, Toronto, CA.
Levy, B., & Anderson, M.C. (June, 2001). Regulation of conscious awareness: further
evidence for a direct suppression mechanism. Poster presented at the 3rd annual meeting of
the NOWCAM, Vancouver, CA.
Anderson, M.C. (May, 2001).
Inhibitory processes and the regulation of phenomenal
Awareness. Paper to be presented at “The Contents of Consciousness: perception, attention,
and phenomenology”, the annual meeting of the Association for the scientific study of
consciousness. Duke University.
Miyamoto, A., & Anderson (May 2001). Quantifying the Inhibitory Regulation of Awareness.
Poster to be presented at “The Contents of Consciousness: perception, attention, and
phenomenology”, the annual meeting of the Association for the scientific study of
consciousness. Duke University.
Anderson, M.C., & Green, C. (November, 2000). Inhibitory control and the cognitive
foundations of repression. Abstracts of the Psychonomic Society, Volume 5.
Anderson, M.C., & Shivde, G. (November, 1999). The functional role of inhibitory processes
in retrieval: Evidence from a parametric study of retrieval-induced forgetting. Abstracts of
the Psychonomic Society, Volume 4
Anderson, M.C., & Bell, T. (November, 1999). Getting our facts straight: Evidence
for
inhibitory processes that resolve interference during propositional retrieval. Abstracts of the
Psychonomic Society, Volume 4
M. Anderson
Vita
29
Anderson, M.C. (July, 1999). Commentary on Inhibitory Process Research. Commentary to
be given at the Society for Applied Research in Memory and Cognition, Boulder, CO.
Shivde, G.& Anderson, M.C. (1999). Inhibition of alternative meanings of homographs
following extended retrieval practice. Poster presented at the 11th Annual Conference of the
American Psychological Society, Washington D.C., June, 1999.
Bell, T., & Anderson, M.C. (May, 1998). Retrieval induced forgetting in memory for facts.
Poster presented at the 10th Annual Conference of the American Psychological Society,
Washington DC, May, 1998.
Shivde, G., & Anderson, M.C. (May, 1998). Inhibition in episodic memory: Evidence for a
retrieval-specific mechanism. Poster presented at the 10th Annual Conference of the
American Psychological Society, Washington DC, May, 1998.
Wilson, A.J., & Anderson, M.C. (May, 1998). Dividing attention impairs episodic memory
recall. Poster presented at the 10th Annual Conference of the American Psychological
Society, Washington DC, May, 1998.
Anderson, M.C. (November, 1997). The inhibitory control of retrieval from long-term
memory: Intact and Impaired function. Paper presented at the annual meeting of the Memory
Disorders Research Society, San Francisco, California.
Anderson, M.C., De Kok, D, & Child, C. (1997, November). Retrieval-induced forgetting on
a test of recognition memory. Abstracts of the Psychonomic Society, 2, Volume 2.
Shivde, G., Anderson, M.C., & Fisher, R. (1997, July). The effect of a parametric
manipulation of retrieval practice on alternative meanings of ambiguous words. Poster
presented at the annual meeting of the Cognitive Science Association for Interdisciplinary
Learning, Hood River, OR.
McCulloch, K.C., & Anderson, M.C. (1996, November). Mnemonic strategies and retrievalinduced forgetting. Poster presented at the 37th Annual meeting of the Psychonomic Society,
Chicago, Illinois.
Bjork, E.L., Bjork, R.A., & Anderson, M.C. (1996, July). Intentional and unintentional
causes of forgetting. Paper presented at the 2nd International Conference on Memory,
Padova, Italy.
Anderson, M.C., Abousleman, T., & Burke, D. (1993, November). Aging and retrievalinduced forgetting. Poster presented at the 34th Annual Meeting of the Psychonomic Society,
Washington D.C.
M. Anderson
Vita
30
Anderson, M.C., Bjork, E.L., & Bjork, R.A. (1993, November). Strength is not enough:
Evidence against blocking models of retrieval inhibition. Paper presented at the 34th Annual
Meeting of the Psychonomic Society, Washington D.C.
Wharton, C.M., Lange, T., Anderson, M.C., Wickens, T., & Schindler, A. (1993, November).
Analogical reminding in story comprehension. Poster presented at the 34th Annual Meeting
of the Psychonomic Society, Washington D.C.
Anderson, M.C., Bjork, R.A., & Bjork, E.L. (1993, June). Retrieval-practice inhibits strong,
but not weak competitors, regardless of practiced-item strength. Poster presented at the Fifth
Annual Convention of the American Psychological Society, Chicago, Illinois.
Anderson, M.C. (1991, July). Does genuine inhibition occur in long-term memory? Paper
presented at the International Conference on Memory, Lancaster, England.
Anderson, M.C., & Spellman, B.A. (June, 1991). Retrieval practice inhibits similar
memories, regardless of whether common cues are shared. Poster presented at the Third
Annual Convention of the American Psychological Society, Washington, DC.
Anderson, M.C., & Bjork, R.A. (November, 1990). Category-specific retrieval inhibition in
long-term memory. Paper presented at the 31st Annual Meeting of the Psychonomic Society,
New Orleans, LA.
Feature Review
Neural mechanisms of motivated
forgetting
Michael C. Anderson1,2 and Simon Hanslmayr3,4
1
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
3
School of Psychology, University of Birmingham, Birmingham, UK
4
Department of Psychology – Zukunftskolleg, University of Konstanz, Konstanz, Germany
2
Not all memories are equally welcome in awareness.
People limit the time they spend thinking about unpleasant experiences, a process that begins during encoding,
but that continues when cues later remind someone of
the memory. Here, we review the emerging behavioural
and neuroimaging evidence that suppressing awareness
of an unwelcome memory, at encoding or retrieval, is
achieved by inhibitory control processes mediated by
the lateral prefrontal cortex. These mechanisms interact
with neural structures that represent experiences in
memory, disrupting traces that support retention. Thus,
mechanisms engaged to regulate momentary awareness introduce lasting biases in which experiences remain accessible. We argue that theories of forgetting
that neglect the motivated control of awareness omit a
powerful force shaping the retention of our past.
A neglected force that shapes retention
Over the past century, memory research has focused on
passive factors that make us forget. Forgetting has been
proposed to result from the decay of memories over time,
the accumulation of similar interfering experiences in
memory, and changes in physical context that make it
harder to recall the past [1]. This historical emphasis on
passive factors fits the common assumption that forgetting
is a negative outcome and, thus, any process underlying it
must happen involuntarily. Although forgetting is often
negative, this emphasis neglects a fundamental feature of
human existence: not all experiences are pleasant. When
reminded of negative events, we are not well disposed
towards them and we deliberately limit their tenure in
awareness. This process is familiar to most people; a
reminder evokes a brief flash of memory and feeling,
abruptly followed by efforts to exclude the unwanted memory from awareness. We do this to preserve our emotional
state, to protect our sense of self, and sometimes simply to
concentrate on what needs to be done. Therefore, any
scientific theory of forgetting must include an account of
the considerable motivational forces that shape retention.
Here, we review the growing research on neural mechanisms underlying motivated forgetting. The term
Corresponding author: Anderson, M.C. ([email protected]).
1364-6613/
! 2014 The Authors. Published by Elsevier Ltd. This is an open access article under
the CC BY license (http://creativecommons.org/licenses/by/3.0/). http://dx.doi.org/
10.1016/j.tics.2014.03.002
‘motivated forgetting’ here refers to increased forgetting
arising from active processes that down-prioritise unwanted experiences in service of creating or sustaining
an emotional or cognitive state. For example, to sustain
positive emotions or concentration, belief in some state of
affairs, confidence, or optimism, it may be necessary to
reduce accessibility of experiences that undermine those
states. Here, we focus on neural evidence for the role of
inhibitory control processes in the voluntary interruption
of mnemonic processing. A core claim is that these inhibitory control processes, widely studied in psychology
and cognitive neuroscience, can be targeted flexibly at
different stages of mnemonic processing and at different
types of representation to modulate the state of traces in
memory.
In support of this view, we review evidence that inhibition can be engaged either during memory encoding or
retrieval to limit retention of unwanted memories. Stopping encoding may disrupt the consolidation of traces
already formed, and also prevent further reflection on
the experience that would enhance its longevity. By contrast, stopping retrieval disrupts the automatic progression from cues to an associated memory, the persisting
effects of which influence whether the experience remains
accessible. Both encoding and retrieval stopping terminate
an unfolding mnemonic process so that an experience can
be excluded from conscious awareness. Through these
efforts to terminate awareness, attentional control interacts with traces in episodic memory to shape what we do
and do not remember of our past.
Inhibitory control at encoding
An effective way of keeping an unwanted memory from
being retrieved in the future is to disrupt and truncate its
encoding. These processes are investigated with directed
forgetting paradigms, in which participants receive a cue
to forget information that they just acquired [2]. Hundreds
of studies conducted over the past 50 years reveal that
humans can readily implement such forgetting instructions, demonstrating that motivation indeed shapes encoding. Inhibition has been proposed to have a role in stopping
encoding processes in these procedures, although passive
factors also are likely to have a role (e.g., [3]). We focus here
on evidence indicating a distinct contribution of inhibitory
control in actively limiting the encoding of unwanted
experience. This evidence has been collected with the
Trends in Cognitive Sciences, June 2014, Vol. 18, No. 6
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Feature Review
Glossary
Accessibility versus availability: : a theoretical distinction on why memory
retrieval can fail. We may fail to retrieve memories because we do not access a
stored memory (i.e., accessibility) or because the memory is not available
anymore in the system (i.e., availability).
Brain oscillations: : regular fluctuations visible in the EEG and/or magnetoencephalogram (MEG), most likely reflecting summated excitatory and inhibitory
postsynaptic potentials. Brain oscillations occur at different distinct frequencies (up to 150 Hz) and have an important role in synchronising neural
assemblies [104] and shaping synaptic plasticity [35].
Cue independence: : the tendency for suppression-induced forgetting to
generalise to novel test cues other than the one originally used as a cue during
retrieval suppression.
Direct suppression: : a method of limiting awareness of an unwanted memory
when a reminder appears in which a person disengages the retrieval process to
either prevent the memory for coming to mind, or to limit its time in
awareness. Inhibition is thought to be a key process in overriding the natural
operation of the retrieval mechanism.
Effective connectivity analysis: : a form of connectivity analysis that allows one
to infer not only that neural activity in two distinct regions is related (statistically),
but also the directional nature of that relation. Effective connectivity analyses,
such as dynamic causal modelling, permit causal inferences about the influence
of one brain region on another in conditions of interest.
Episodic context: : the spatiotemporal environment in which a stimulus is
encountered. The representation of this context and its association to a stimulus
form a fundamental feature of episodic memory of the stimulus. Context can also
refer to internal states that get associated to a stimulus (e.g., mood or incidental
thoughts), which is sometimes referred to as ‘mental context’.
Event-related potential (ERP): : a time-varying brain signal with positive and
negative deflections (so-called ‘components’), obtained by averaging over
several EEG segments corresponding to a task or stimulus.
Fading affect bias: : the documented tendency for negative emotions
associated with personal experiences to decline more quickly over time
compared with positive emotions.
Inhibitory control: : a control process that downregulates activity of interfering
or otherwise unwanted representations in the service of a current task or goal,
reducing their influence on cognition and behaviour.
Late positive component (LPC): : a positive ERP component related to episodic
retrieval. During a retrieval task, the LPC emerges approximately 400–800 ms
after stimulus onset, is maximal over parietal recording sites, and is assumed
to reflect retrieval of contextual details of the study episode (i.e., recollection
[105]).
Long-range synchrony: : synchronisation between distant cell populations
separated by several centimetres (e.g., frontal and parietal). Long-range
synchrony is usually estimated based on the co-variation of oscillatory phase
between two recording cites.
Mnemic neglect: : the tendency for people to have a higher rate of forgetting
for negative feedback about themselves and their performance, than for
neutral or positive feedback, even when encoding time is matched.
N2: : a negative ERP component related to cognitive control, and often
associated with motor response inhibition. The N2 refers to enhanced
frontocentral negativity typically approximately 150–400 ms.
Repetition priming: : improved performance in processing a stimulus arising
from prior exposure to the stimulus.
Repetition suppression: : the finding that repetitions of a stimulus elicit less
neural activity in areas involved in processing the stimulus, compared with
nonrepeated stimuli, taken to be a marker of memory for the stimulus.
Repetitive transcranial magnetic stimulation (rTMS): : a technique commonly
used to stimulate a specific brain area by applying a time-varying magnetic
field that induces electric current flow in the brain.
Selective rehearsal: : a passive, noninhibitory account used to explain the
reduced memory performance for to-be-forgotten items, relative to to-beremembered items.
Socially shared retrieval-induced forgetting: : when a person is recounting an
experience shared by listeners, the tendency for the listeners to later forget (at
a higher rate) details not recounted by the speaker. The higher rate of
forgetting is thought to arise from listeners covertly retrieving the experience
as it is being recounted and, consequently, inducing retrieval-induced
forgetting on nonretrieved knowledge.
Suppression-induced forgetting: : in the TNT procedure, impaired recall of nothink items, compared with baseline memories that are neither retrieved nor
suppressed.
Think/no-think procedure (TNT): : the main procedure used to study retrieval
suppression, whereby people are repeatedly prompted with cues to memories
and asked to either retrieve (think) the memory, or to stop its retrieval (nothink), with the result that suppressed items are more poorly recalled on later
tests.
Thought substitution: : a method of preventing retrieval of an unwanted
memory when a reminder appears in which a person generates alternative
thoughts associated to the reminder to occupy awareness.
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Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
item-method [4] and list-method [5] directed forgetting
procedures (Box 1).
Item-method directed forgetting
Item-method directed forgetting has a long tradition in
cognitive psychology [4]. This effect is robust, as reflected
by the range of conditions under which it has been
reported, including both explicit and implicit memory tests
[6,7]. Item-method directed forgetting usually has been
explained in terms of selective rehearsal (see Glossary)
according to which to-be-forgotten items are spared from
further processing and are subject to passive forgetting,
whereas to-be-remembered items are actively rehearsed
[2]. Interestingly, the occurrence of item-method directed
forgetting in recognition tests has been used as an argument for passive, noninhibitory explanations, because
some have argued that inhibition should only temporarily
reduce the accessibility of the affected items and, therefore,
it should be possible to release these items from inhibition
later [2].
Although selective rehearsal is a common interpretation of item-method directed forgetting [2], recent behavioural and neural evidence indicates that inhibitory
control over episodic encoding may have a bigger role than
has been acknowledged. For example, the selective rehearsal account emphasises processes acting on to-be-remembered items, which are rehearsed more extensively
and elaborately when the cue to remember is given. Therefore, the system should experience more cognitive load in
the remember compared to the ‘forget’ condition, in which
people can simply drop the to-be-forgotten item from working memory. This prediction was tested in several experiments in which participants performed a secondary task
after the remember and/or forget cue was given [8,9].
However, contrary to the selective rehearsal account, the
forget condition was more effortful than the remember
condition, as reflected by slower reaction times to perform
the secondary task during execution of the forget instruction. Moreover, stopping a motor response after the cue is
more successful in the forget compared with the remember
condition [9], suggesting that forget cues trigger similar
inhibitory mechanisms to those engaged when stopping a
motor action [10]. However, further clarification of this
possibility is needed [9]. These results clearly imply that an
active process contributes to item-method directed forgetting [11], and raise the possibility that it is inhibitory
in nature. This possibility is consistent with evidence that
directed forgetting cues lead to the removal of items from
working memory and not merely to passive decay [12,13].
Several recent functional (f)MRI studies support the
hypothesis that item-method directed forgetting engages
an active process that inhibits ongoing encoding [14–18].
These studies consistently indicate that attempting to
forget a recent item engages prefrontal and parietal
regions, suggesting that forgetting is effortful, consistent
with behavioural findings [15,16,18]. The right superior
and middle frontal gyrus (approximately BA 9/10), and the
right inferior parietal lobe (approximately BA 40) are
consistently more active during intentional forgetting
(to-be-forgotten items that are actually forgotten) compared with incidental forgetting (to-be-remembered items
Feature Review
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
Box 1. Item and list-method directed forgetting
(B)
Item-method
Encoding
Encoding
(list 1)
Test
Leaf
Arm
Old/
new?
F
Leaf
Arm
Leaf
Old/
new?
R
...
Beard
(C)
List-method
F
Arm
Beard
Old/
new?
Cue
...
R
Encoding
(list 2)
Behavioural results
Item-method
(i)
Test
Knee
Winter
List-method
(ii)
Gold
Memory
(A)
instruction. At the end, a recall test occurs. Figure IC compares the typical
behavioural results obtained in item-method (Figure ICi) and list-method
(Figure ICii) directed forgetting. Figure ICii shows the twofold effect of
the forget cue on the recall test, with forgetting of list-1 items and
enhancement of list-2 items. In both paradigms, participants do not
know in advance whether they should forget or remember the respective
item. Thus, the control processes mediating these effects must act on
memory representations and not the initial perception of an event.
Memory
In studies of directed forgetting, two procedures are generally used: the
item-method and the list-method (see [1]). These paradigms are
illustrated in Figure I. As illustrated in Figure IA, in item-method directed
forgetting, participants study items one at a time, and each item is
followed by a forget (F) or remember (R) instruction. Later, memory for
all items is tested. As shown in Figure IB, in list-method directed
forgetting, a entire list of items is first studied, followed by a F or R
instruction. A second list is then studied, usually followed by a R
...
List 1
List 2
TRENDS in Cognitive Sciences
Figure I. The item and list-methods for studying directed forgetting, along with the typical pattern of findings (for real examples, see [8] and [5,25], respectively).
that are forgotten (Figure 1A) [15,16,18]. Although these
findings suggest that intentional forgetting recruits additional processes beyond those associated with incidental
forgetting, these activations do not specify the nature of
those processes. For example, activations during forget
trials might reflect engagement of the default mode network, which is characterised by positive blood oxygenation
level-dependent (BOLD) correlations between superior
prefrontal and parietal cortex during rest [19]. Thus, these
(A)
(B)
Item-method DF
forge!ng success
findings may simply reflect a greater incidence of passive
rest during forget trials compared with remember trials.
However, speaking against this view, connectivity analyses show that activity in the right dorsolateral prefrontal
cortex (DLPFC) during forget trials predicts decreased
activity in the left hippocampus, especially during successful intentional forgetting [18]. This latter result is incompatible with the default mode network hypothesis, which
predicts the opposite (positive) connectivity pattern
fMRI
(i)
EEG
(C)
rTMS
DLPFC
(∼BA 9)
40
DLPFC (fMRI)
% Difference
(ii)
Key:
30
DLPFC
Control
20
10
0
Forge!ng
Enhancement
Neural sync. (EEG)
TRENDS in Cognitive Sciences
Figure 1. Neural correlates of directed forgetting (DF). (A) An activation map of a recent item-method directed forgetting functional (f)MRI study [18]. Red areas illustrate
significant voxels (P <0.005) indicating greater activity for to-be-forgotten items that are actually forgotten compared with to-be-remembered items that are remembered.
(B,C) The results of a multimodal list-method DF experiment [32]. (B) Forget instructions were associated with increased blood oxygenation level-dependent (BOLD) signal
in the left dorsolateral prefrontal cortex (DLPFC) and reduced alpha/beta long-range synchrony [11–18 Hz (i)], which were negatively correlated on a single trial level (ii). (C)
Stimulating the DLPFC with repetitive transcranial magnetic stimulation (rTMS; 1 Hz) selectively increased list-1 forgetting, without affecting list-2 enhancement. Adapted,
with permission, from [32] (B,C). Abbreviation: EEG, electroencephalogram.
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Feature Review
between DLPFC and the medial temporal lobe (MTL) [20].
Rather, negative connectivity between right DLPFC and
hippocampus suggests that the right prefrontal cortex
exerts inhibitory control over the encoding activity in
the MTL [21], similar to that observed during retrieval
(see ‘Neural Basis of Retrieval Suppression’). One plausible hypothesis is that the active forgetting mechanism
implicated by behavioural studies [15,16,18] may reflect
the action of this frontohippocampal modulatory system.
The neural correlates of item-method directed forgetting
have also been studied with intracranial event-related
potentials, providing information about the temporal dynamics of the forgetting mechanism within the MTL [22].
This study found that forget cues that cause later forgetting
elicited decreased negativity in the anterior hippocampus
compared with remember cues that led to forgetting. Notably, enhanced negativity in the hippocampus at approximately 500 ms is usually related to successful encoding.
These authors further found that forget cues triggered
sustained positivity in the rhinal cortex, an interfacing
structure between the cortex and hippocampus. Together
with scalp event-related potential (ERP) studies, showing
sustained prefrontal positivity after the forget cue [23],
localised to the right DLPFC [24], these studies converge
with fMRI data to suggest that item-method directed forgetting recruits a right prefrontal–MTL network to terminate episodic encoding processes. Together, these studies
question a purely passive based view of item-method directed forgetting, which has been the popular account among
experimental psychologists.
List-method directed forgetting
Sometimes, we may wish to forget a set of events that is
extended in time (e.g., a recent doctor’s visit, or dispute with
an unpleasant acquaintance). This situation is modelled by
the list-method of directed forgetting (Box 1). A typical experiment comprises two lists (e.g., 10–20 items in each list), with
a forget or remember cue given after the first list [2,25]. After
encoding the second list, a brief distracting task follows and
then recall is tested. On this final test, people typically recall
the first list more poorly when it is followed by a forget,
compared with a remember, instruction. Interestingly, people
recall the items following a forget cue better than they do
items studied after a remember cue (Box1, Figure 1C) (e.g.,
[25]). These complementary effects are referred to as list-1
forgetting and list-2 enhancement. These effects arise on free
recall, cued recall, and recognition tests, although, in the
latter case, deficits are often restricted to source memory.
List-method directed forgetting effects have also been observed in autobiographical memory [26,27]. Poorer recall of
the to-be-forgotten list is believed to reflect reduced accessibility rather than reduced availability of the forgotten material [2,28,29]. Both active [5,30] and passive mechanisms have
also been proposed for this phenomenon [3,31].
As with item-method directed forgetting, imaging research with the list method indicates that instructions to
forget trigger an active process that disrupts mnemonic
activity. For example, two studies examined the neurophysiological mechanisms of directed forgetting by focussing on
brain oscillations [30,32]. Prior work established that memory formation is typically accompanied by increased large
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Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
scale synchrony, a neural marker thought to reflect upregulated synaptic plasticity [33–35]. Strikingly, cuing people to
forget a just-studied list decreased the large-scale synchrony
in a widespread cortical network in the alpha/beta frequency
range. Individual differences in this effect predicted forgetting of to-be-forgotten items [30], suggesting that decreasing synchrony disrupts neural processes that would improve
retention. This finding was replicated in a multimodal
electroencephalography (EEG)–fMRI study [32], in which
it was found to be associated with increased BOLD signal in
the left DLPFC (Figure 1B). Following this discovery, a
combined EEG–repetitive transcranial magnetic stimulation (rTMS) experiment demonstrated that stimulating
the left DLPFC with rTMS during a forget instruction also
reduced neural synchrony, significantly increasing directed
forgetting (Figure 1C). Enhanced forgetting following rTMS
indicates that stimulation facilitated processes needed to
implement directed forgetting, consistent with findings
showing that rTMS can enhance, rather than disrupt processing (see [32] for a detailed discussion of the enhancing
effects of slow rTMS on forgetting). This finding supports a
causal role of frontally driven processes in inducing forgetting effects and complements work showing that prefrontal lesions disrupt list-method directed forgetting [36].
Increased activation of the DLPFC together with decreased neural synchrony suggests that an active control
process contributes to directed forgetting. However, these
findings do not specify that this active process necessarily
engages inhibitory control. For example, prior studies have
highlighted the importance of the DLPFC in task switching
(reviewed in [37]). Therefore, DLPFC involvement during
the forget instruction might simply reflect a voluntarily
induced task switch that stops rehearsal, and not inhibition.
However, this overlap between directed forgetting and taskswitching activations may be driven by inhibitory processes
involved during task switching, as numerous studies indicate [38]. Furthermore, in EEG studies, task switching
typically induces a pronounced increase in frontoparietal
theta long-range synchrony [39], in stark contrast to the
decreases in alpha/beta long-range synchrony observed in
list-method directed forgetting. Although these findings
suggest that directed forgetting activations are unlikely
to arise from task switching, the relations between the
processes in the two tasks merits further exploration.
The improved recall of list-2 that accompanies the forgetting of list-1 items might suggest a single mechanism
that enhances list-2 encoding by reducing interference
from list-1 items [2]. However, there are reasons to doubt
this. First, forgetting and enhancement are often uncorrelated (e.g., [30,40,41]). Second, list-1 forgetting often can be
modulated independently of list-2 enhancement [32,42].
Third, whereas all list-1 items suffer forgetting, irrespective of their serial position, the enhancement of list-2
appears to be driven by the first few items [42]. The latter
result fits electrophysiological data suggesting that the
forget cue enhances subsequent encoding because it acts
like a ‘reset button’ that frees cognitive resources and
allows a fresh encoding start. This assumption is reflected
in EEG work showing that oscillatory markers of encoding
exhaustion, which gradually increase with the number of
encoded items, are reset by the forget cue [42]. This latter
Feature Review
effect is most evident in alpha oscillatory amplitude, which
usually decreases during memory encoding [43,44]. Thus,
forgetting and enhancement in list-method directed forgetting appear to reflect different processes that can be
dissociated on a cognitive and neural level.
The list and item methods differ in the target of forgetting. Whereas the item method targets individual
items, the list method typically directs people to forget a
set of items defined by temporal context (i.e., ‘the previous
list’). This broader targeting may be implemented by
directing inhibition at representations of temporal context
rather than individual items. Consistent with this, listmethod directed forgetting induces a shift away from the
mental context of the first list, and this context shift may
make it harder to recall list-1 items. For example, a forget
instruction induces forgetting effects similar to those
caused by other instructions designed merely to shift
mental context away from the first list, without instructing
people to forget [3]. Given that similar forgetting can be
induced without instructing people to forget, some have
argued that directed forgetting need not reflect inhibitory
control [3]. However, an alternative possibility is that
directed forgetting instructions achieve context shifts in
a mechanistically distinct way, by engaging inhibitory
control to force a shift in context. Consistent with this
possibility, directed forgetting and ‘mental context shift’
instructions appear to be mediated by different neural
processes. Whereas mental context shift instructions
mainly affect local alpha and theta synchrony [45,46], only
directed forgetting disrupts long-range alpha/beta neural
synchrony [30,32]. Combined with evidence for a causal
role of prefrontal cortex in inducing these changes in both
long-range synchrony and forgetting [32], these findings
suggest that an active inhibitory process disrupts list-1
context in directed forgetting. However, these dissociations
are based on between-study comparisons because no study
has yet directly contrasted EEG synchrony patterns between directed forgetting and mental context change.
Although inhibition may typically be targeted at temporal context in list-method directed forgetting, other targets are possible. For example, recent research has
examined whether directed forgetting can be targeted
selectively at some, but not all, of the pre-cue information
[47–51]. Three of these studies demonstrated this is possible [48–50]. For instance, one study demonstrated selectivity of directed forgetting in three experiments using
visual (colours) and auditory (words spoken by a female
versus male voice) material [49]. In one experiment, list-1
items were spoken either by a male or a female voice,
alternating on an item-by-item basis, and the participants
were able to forget items selectively based on the gender of
the speaker. However, some studies failed to find selective
directed forgetting [47,51]. The reasons for these discrepancies are currently unknown. If directed forgetting is
selective, it suggests that the inhibitory processes target
dimensions other than temporal context.
To conclude, behavioural and neurophysiological studies
indicate that encoding can be disrupted or truncated by an
active inhibitory control mechanism that limits the representation of an experience in long-term memory. Similar to
inhibitory control in the motor system, where higher-order
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
control regions in the prefrontal cortex suppress activity in
lower-order motor regions to stop a movement [52], the
prefrontal cortex targets memory-related structures in
the MTL [18,22]. These processes reduce encoding activity
and downregulate long-range neural synchrony [32] to disrupt the formation of unwanted memories.
Inhibitory control at retrieval
Unwanted experiences are often stored in memory, despite
efforts to limit encoding. When this happens, limiting
awareness becomes a problem of controlling retrieval.
Retrieval can of course be prevented by avoiding reminders, which is a common behaviour after an unpleasant
event. However, when unwelcome reminders occur, people
often try to exclude the unwanted memory from awareness. Stopping retrieval of an unwanted memory is known
as ‘retrieval suppression’, a process that engages response
override mechanisms formally similar to stopping a reflexive motor action [53,54]. Retrieval suppression is often
studied with the think/no-think paradigm (TNT) [53],
which mimics situations when we try to suppress unwelcome remindings (Box 2).
Behavioural effects of retrieval suppression
The TNT procedure consistently shows that people can
limit retrieval [53–55]. Two main findings support this
conclusion. First, suppressing retrieval consistently
abolishes the benefits of reminders on memory, as
reflected in the sizeable difference in final recall between
Think and No-Think items. Thus, at a minimum, suppressing retrieval reduces the facilitation that retrieved memories usually enjoy. Second, suppressing retrieval often
reduces recall for No-Think items below that observed for
baseline items, a phenomenon known as ‘suppressioninduced forgetting’. Suppression-induced forgetting is
Box 2. The TNT paradigm
The TNT procedure mimics situations in which we encounter a
reminder to a memory we prefer not to think about, and try to keep
the memory out of mind [53]. To create reminders, participants
study cue–target pairs (e.g., word pairs, or picture pairs, such as
‘ordeal roach’) and are then trained to recall the second item (roach)
of the pair whenever they encounter the first (ordeal) as a reminder.
Participants then participate in the TNT phase, in which they are
asked to exert control over retrieval. On each trial, reminders from
the pairs appear in green or red; when the reminder appears in
green, participants are to recall the response, whereas, for red
reminders, participants are asked to avoid retrieving the response,
preventing it from entering awareness. The key question concerns
whether people can recruit inhibition to prevent the memory from
intruding into consciousness, and whether doing so disrupts later
retention of the unwanted memory.
To measure the effects of retrieval suppression, participants
receive a final test in which they are given each reminder and are
asked to recall the associated response. Memory performance is
compared between items that participants suppressed (no-think
trials), items that they retrieved (think trials), and items that they
studied, but neither suppressed nor retrieved during the TNT phase
(baseline trials). Comparing final recall of no-think items to either
think or baseline items indicates whether retrieval suppression
affects retention. Comparing no-think to baseline items is more
appropriate when trying to establish that suppression makes
memory worse, as opposed to merely preventing memory improvement that might arise from repeated reminders.
283
Feature Review
especially informative because it indicates that, during
retrieval suppression, reminders do not merely fail to
enhance retention, but trigger processes that impair access to the unwanted memory. These findings highlight a
central theme of this article: that one’s disposition towards
a memory affects how well it is retained. Reminders
enhance retention when a person is well disposed towards
a memory, but when one has motivations for excluding a
memory from awareness, retrieval can be stopped, preventing the benefits of retrieval and further disrupting the
memory. These symmetrical effects of reminders indicate
a high level of control over the retrieval process, control
that shapes accessibility.
Much is now known about suppression-induced forgetting. First, forgetting increases with the number of times
a memory is suppressed [53,55–60], indicating that suppression yields cumulative effects. The forgetting effect can be
further increased if participants are given time to prepare
for suppression [61], indicating the importance of anticipatory processes. Suppression-induced forgetting arises with
many stimuli, including word pairs, face–scene pairs [62–
65], face–word pairs [58], word–object pairs [66,67], and
pairs comprising words and nonsense shapes [68]. Suppression-induced forgetting has even been observed with autobiographical experiences [69–71], although suppression
impairs memory for event details more than access to the
event itself. Some studies have reported a lack of suppression-induced forgetting when it might otherwise be expected
(see [55] for a detailed discussion with hypotheses). Forgetting effects occur whether the memory is a neutral or
negatively valenced word or scene [59,60,62–65,72–79],
although it remains unclear whether forgetting increases
[60,62], decreases [65,80], or is unaffected [74,75] with
negative, compared with neutral valence. Although few
studies have examined how long forgetting lasts, one study
found that a single suppression session produces forgetting
that lasts at least 24 h [81], with other evidence suggesting
that it may dissipate after a week [70,80]. Suppressioninduced forgetting is diminished in young children [82]
and older adults [56], two populations hypothesised to have
deficient inhibitory control function. Interestingly, individual differences in participants’ perceptions of their ability to
control unwanted thoughts in daily life predict suppressioninduced forgetting of aversive scenes [77].
Suppression-induced forgetting exhibits properties consistent with a role of inhibitory control. For example, the
forgetting often generalises to novel test cues. For instance,
after studying ordeal–roach, if participants suppress
‘roach’ whenever they receive ‘ordeal’ as a cue, roach will
be recalled more poorly, regardless of whether it is tested
with ‘Ordeal’ or ‘Insect’. Thus, suppressing a memory
reduces its accessibility from a variety of cues, a property
known as ‘cue independence’ [53]. Cue independence indicates that the forgetting most likely reflects disruption of
the suppressed trace itself rather than the particular
pathway from the reminder to the trace (reviewed in
[55]; see also [83]). This is usually taken as strong evidence
for an inhibition process that suppresses the trace ([53],
although see [84] for an alternative]. As additional support
for an item-specific inhibition process, forgetting has also
been found on item recognition tests for both words and
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Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
abstract shapes [68,85]. Moreover, the effect even occurs on
indirect priming tests, such as perceptual identification:
participants who suppress retrieval of visual objects are
less likely to identify correctly those objects when they are
presented in visual noise [66,67]. Thus, suppression not
only impairs conscious access to unwanted memories, but
also affects their unconscious influence, at least on tests of
object perception.
Although research on retrieval suppression usually asks
people to recall suppressed items intentionally, this arguably does not reflect real-world circumstances. In most
cases, people are unlikely to try to recall experiences they
were motivated to suppress. A more appropriate measure of
the impact of suppression in real terms would measure the
tendency to retrieve the suppressed content, rather than the
ability to do so [86]. For instance, how likely would people be
to respond with the suppressed content on a free association
test? Interestingly, on such tests, suppression effects are
especially pronounced [86]. This raises the possibility that
intentional recall measures underestimate the change in
spontaneous retrieval patterns that arise in real life.
Changes in retrieval patterns introduced by inhibition
may be sustained over the long term by alternative associations that naturally arise in response to reminders. Indeed,
asking people to generate alternative associations to a
reminder often increases forgetting, compared with not
giving specific instructions. However, as noted shortly,
thought substitution is not necessary to induce forgetting,
and several mechanisms contribute to suppression-induced
forgetting.
Neural basis of retrieval suppression
Similar to directed forgetting, stopping retrieval appears
to be achieved, in part, by control mechanisms mediated by
the prefrontal cortex. Retrieval suppression engages lateral prefrontal cortex, including DLPFC and ventrolateral
prefrontal cortex (VLPFC) often in the right hemisphere
[64,87–91]. These regions resemble areas involved in stopping motor actions, suggesting that suppression engages
general response override mechanisms to stop retrieval (a
point to which we will return). Critically, suppression is
accompanied by reduced activity in brain areas linked to
episodic recollection [64,87–91]. For example, suppression
is associated with reduced hippocampal activity, sometimes along with other subregions of the MTL. Given that
single-unit electrophysiology and functional neuroimaging
have linked hippocampal activity to the presence of retrieved memories in awareness, these findings suggest
that inhibitory control interrupts hippocampal retrieval
processes to suppress mnemonic awareness. Consistent
with this hypothesis, frontohippocampal interactions during suppression have been observed with a range of materials, including words [87–91], visual objects [67], and
negatively valenced scenes [64], suggesting a domain general suppression process.
Although the foregoing pattern suggests that suppression engages the prefrontal cortex to reduce hippocampal
activity, reduced activity during no-think trials (relative to
think trials) might simply reflect hippocampal engagement
during think trials. Thus, rather than showing that suppression terminates retrieval, less hippocampal activity
Feature Review
may reflect a passive failure to engage retrieval during nothink trials. However, evidence has grown that inhibitory
control reduces hippocampal activation. First, hippocampal activity is also reduced compared with activity during a
fixation baseline condition [64,91], suggesting that reductions reflect more than an absence of positive activation.
Second, DLPFC activation during no-think trials is often
negatively correlated with hippocampal activity [63,64].
Indeed, the magnitude of downregulation and the correlation with DLPFC has in some studies increased over blocks
of the TNT phase [64], suggesting progressively improved
hippocampal regulation with practice. Third, reduced hippocampal activity predicts later forgetting of unwanted
memories [64,91]. Finally, effective connectivity analyses
show a top-down modulatory influence of DLPFC on the
hippocampus [67,88], with negative coupling from DLPFC
predicting the amount of suppression-induced forgetting
[88]. Although the pathways implementing this top-down
influence are unknown, some data suggest the cingulum
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
bundle is a plausible candidate for a white matter tract
that could support the frontohippocampal interactions
underlying suppression [90]. Together, these findings
strongly support a role of DLPFC in reducing hippocampal
activity, interrupting recollection, and impairing retention. More broadly, they specify a neurobiological model
of memory control that provides a framework for understanding disordered control over memory (Box 3).
Opposing neural mechanisms underlie direct suppression and thought substitution. Although hippocampal
downregulation is a fundamental tool of retrieval suppression, other mechanisms of controlling awareness are possible. For example, people may redirect attention to other
thoughts about a reminder. Such diversionary thoughts
could either prevent the entrance of the memory into
awareness, or replace an intruding memory. Behavioural
findings indicate that asking participants to generate
thought substitutes for reminders can be effective in
Box 3. Clinical variation in motivated forgetting
Individual differences in memory control may cause either deficient or
exaggerated rates of forgetting of life events [55,106,107] that we
might better understand and remediate with a neurobiological model
of memory control.
Deficient memory control
Intrusive memories and thoughts arise in many clinical conditions,
such as post-traumatic stress disorder (intrusions), depression
(rumination), attention deficit disorder (distracting thoughts), obsessive/compulsive disorder (obsessive thoughts), addiction (craving
related thoughts), and anxiety (worries). This symptom may originate
from deficient inhibitory control over memory. Supporting this,
during retrieval suppression, adults with attention deficit disorder
show impaired suppression-induced forgetting, and also diminished
modulation of hippocampal activity by DLPFC [63]. Similarly, patients
with post-traumatic stress disorder show impaired response inhibition [108], diminished engagement of lateral prefrontal cortex on
response inhibition tasks [109], and, critically, reduced directed
forgetting [110]. Rumination, depression, and anxiety have also been
linked to impaired suppression-induced forgetting [79,111,112].
Deficient memory control may reflect compromised function of the
networks discussed in this article. For example, disordered control
may originate from diminished cortical volume or white matter
connectivity between prefrontal cortex and sites of modulation.
Deficits in neurotransmitters relating to inhibitory control, such as
dopamine, might also underpin disordered control, a possibility
supported by work linking genetic variation in dopamine metabolism
to memory inhibition [113]. Alternatively, poor memory control may
sometimes reflect lack of experience with intrusive memories and,
thus, inadequate development of the control process, which may
exhibit experience dependent plasticity. For example, after a trauma,
cortical thickness in right DLPFC increases significantly over a year,
with the size of the increase predicting reduced post-traumatic stress
disorder symptoms [114]. Taken together, these findings suggest that
both pharmacological and training interventions could be designed to
bolster memory control.
Exaggerated memory control
One striking example of motivated forgetting is psychogenic amnesia,
in which a person exhibits profound amnesia for large chunks of their
personal experiences in the aftermath of an intensely stressful period
[115]. Two studies suggest that such cases in part reflect exceptionally
effective memory control. One study examined two psychogenic
amnesia patients, with amnesia extending years before scanning
[116]. Both patients were scanned as they identified faces. Some faces
were of strangers (novel faces). Others were of people the patients
knew, with half drawn from people they met before their window of
amnesia (identifiable faces), and the other half from during the
window of time affected by amnesia (unidentifiable faces). Unsurprisingly, patients did not recognise the novel faces, and could recognise all
of the identifiable faces. Intriguingly, although neither patient remembered any of the unidentifiable faces, these faces elicited increased
activation in right DLPFC and VLPFC, together with reduced activity in
the hippocampus, as observed in laboratory studies of retrieval
suppression (Figure I). After treatment, one patient recovered their
memories and, upon rescanning, no longer exhibited the suppression
pattern. These findings suggest that extreme psychological distress
leads retrieval suppression to be engaged involuntarily in reaction to
certain stimuli [116]. In an independent study, a patient with
psychogenic amnesia was shown to exhibit dramatically magnified
suppression-induced forgetting in the TNT procedure [117], suggesting
a link between their condition and suppression ability.
(A)
(B)
(i)
(i)
(ii)
(ii)
TRENDS in Cognitive Sciences
Figure I. Brain-imaging data from two patients with dissociative amnesia [116].
Patients 1 (A) and 2 (B) viewed images of faces and decided whether they
recognised them from their life. Images were either strangers (novel), faces they
knew, from outside the window of amnesia (identifiable faces) or faces they
knew from within the amnesic window (unidentifiable faces). (Ai) and (Bi) depict
brain areas that are more active for unidentifiable faces than for identifiable faces
(right dorsolateral prefrontal cortex). (Aii) and (Bii) depict brain areas that are less
active for unidentifiable faces (hippocampus).
285
Feature Review
inducing
forgetting
of
an
unwanted
memory
[59,72,73,81,86,88]. Clearly, however, thought substitution could not involve suppressing retrieval. Given that
the substitutes themselves need to be recollected, this
approach seems to require the opposite outcome sought
with retrieval suppression: the upregulation of retrieval
processes.
Recently, the neural mechanisms of thought substitution and inhibition in the TNT procedure have been studied [88] (Figure 2). A thought substitution group was asked
to avoid unwanted memories whenever they encountered
reminders to them by recalling a thought substitute to
distract themselves. However, the direct suppression
group was urged not to generate distracting thoughts,
but to instead ‘push’ the memory from awareness, if it
intruded. Interestingly, although both groups showed similar forgetting, only direct suppression reduced hippocampal activation. Critically, these strategies engaged distinct
networks. Whereas direct suppression recruited the right
DLPFC region typically associated with retrieval suppression, thought substitution engaged the left inferior frontal
gyrus (IFG) associated with selective retrieval [92]. Effective connectivity analyses revealed that the right DLPFC
was negatively coupled with the hippocampus during direct suppression, more so for people who forgot suppressed
memories. By contrast, during thought substitution, activation in left caudal IFG predicted greater hippocampal
activation during no-think trials, suggesting that it engaged hippocampal retrieval processes to sustain the substitute memory. Thus, two approaches to limiting
awareness (suppression and self-distraction) recruited distinct frontohippocampal networks with opposing effects on
hippocampal processing.
Direct suppression and thought substitution have also
been dissociated electrophysiologically [93]. Prior research
has established an ERP component, known as the late
positive component (LPC), which is sensitive to the level
of episodic recollection and contextual retrieval of a test
item [94]. The LPC occurs over parietal scalp sites 400–
800 ms after a recognition memory target appears, and is
greater for older words than new words. If the LPC indexes
recollection, measuring it during no-think trials should
reveal a reduced LPC compared with that observed during
think trials. This prediction has been confirmed repeatedly
with word pairs [93,95–97], picture–word pairs [58] and
even negatively valenced face–scene pairs [65,98]. Interestingly, participants can for the very same items, make
the LPC come and go when instructions are changed from
retrieval to suppression, suggesting highly efficient control
over recollection [97]. Importantly, direct suppression, but
not thought substitution, modulates the LPC [93]. Given
that thought substitution involves recollecting substitutes
(which itself would generate a LPC), the TNT conditions
should be (and are) electrophysiologically similar. These
findings support the view that direct suppression overrides
conscious recollection, and parallel selective reductions of
hippocampal activity during direct suppression [88].
Suppression mechanisms respond to memory intrusions.
Intrusions of unwanted memories into awareness appear to
have an important role in triggering inhibitory control over
286
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
memory. For example, reduced hippocampal activity was
closely tied to the exclusion of intrusive memories from
awareness in a recent study using phenomenological
reports [91]. To link intrusions to hippocampal regulation,
no-think trials on which an unwanted memory entered
participants’ awareness were isolated, and the intrusions
were then linked to changes in hippocampal activity. Participants classified their experience after each trial according to whether the cue triggered retrieval of its associated
memory. Intrusions elicited strong modulations of hippocampal activity (Figure 2). Although hippocampal downregulation occurred overall during no-think trials, the
depth of reduction was pronounced during intrusions, when
awareness of the memory needed to be suppressed. Strikingly, the depth of the down-regulation during intrusions
strongly predicted suppression-induced forgetting. However, no correlation between downregulation and forgetting
arose during nonintrusions, suggesting that reactivation of
a memory trace is an important condition for memory
disruption [10,99,100]. These findings link the purging of
unwanted mnemonic awareness to reduced hippocampal
activity. Importantly, they also show that intrusions of
unwanted memories decline with repeated suppression,
highlighting the outcome people seek when suppressing
unwelcome remindings.
Intrusive memories seem to leap to mind automatically
given reminders. The need to inhibit such automatic retrievals may engage mechanisms that are similar to those
used to override reflexive actions [9,53,54]. fMRI, behavioural, and EEG evidence supports this possibility. For
example, both retrieval suppression and motor inhibition
engage right DLPFC and VLPFC [63,67,87–91], consistent
with the similar functional demands posed by the two
forms of stopping. Indeed, activation in right lateral prefrontal cortex during retrieval suppression predicts not
only later retrieval suppression effects, but also stop signal
reaction time on motor tasks [63]. Moreover, participants’
stop signal reaction time predicts the proportion of aversive pictures forgotten after retrieval suppression [63].
Electrophysiological components, such as the N2, are larger during suppression than during retrieval [65,85,93,95–
98], echoing findings in motor inhibition research, such as
the no-go N2 and the stop signal N2. Importantly, larger
N2s for no-think items compared to think items are even
more pronounced for no-think items that are later forgotten [96]. Strikingly, in one study, the enhanced N2 for nothink trials predicted N2 enhancement during stop-signal
trials, even when the tasks were separated by a year [96].
Prior work suggests that the source of the motor no-go N2
is either the anterior cingulate cortex or the lateral prefrontal cortex [101], consistent with areas involved in
retrieval suppression. Intriguingly, biomarkers of executive function know to predict individual differences in
motor response inhibition, such as heart rate variability,
also predict the magnitude of suppression-induced forgetting [83]. Taken together, these findings suggest that
the inhibitory process engaged during retrieval suppression recruits general response inhibition mechanisms, although more precise comparison of these mechanisms is
needed. For example, although memory and motor inhibition both often engage right DLPFC and VLPFC,
Feature Review
Mechanism
Prefrontal cortex
Direct suppression
Reminder
Hippocampus
2
x = 36
Contrast es!mates
(A)
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
Right DLPFC
Memory
1.5
1
0.5
0
–0.5
–1
–1.5
–2
Recall
Suppress
Recall
Suppress
Thought subs"tu"on
Reminder
2
Contrast es!mates
y = 25
Le$ VLPFC
Briefly
2
0.1
fMRI subjects (N=18)
70%
–1
–1.5
***
**
*
0.0
–0.1
50%
2.0
40%
Suppression score
Intrusion frequency
60%
30%
20%
10%
0
–0.5
Right hippocampus
O$en
3
Percent signal change
Never
1
1
0.5
–2
Subs#tute Memory
(B)
1.5
1.0
0.0
–1.0
r=–.04
1
2
3
4
5
6
7
8
9
10
11
12
r=.66**
–2.0
–0.1 –0.1 0.0 0.1 0.1 0.2 0.2 –0.1
0.0
0.1
0.2
Reduc!on in ac!vity
Reduc!on in ac!vity
during nonintrusions
during intrusions
0.3
TRENDS in Cognitive Sciences
Figure 2. Conditions that trigger inhibitory modulation of the hippocampus during retrieval suppression. (A) Direct suppression and thought substitution involve distinct
networks that both cause forgetting, but that have differing effects on the hippocampus [88]. Direct suppression involves suppressing episodic retrieval to prevent or
override recollection of an unwanted memory (depicted by angled lines), whereas thought substitution involves engaging retrieval to recall a substitute thought in response
to a reminder. Direct suppression (upper row) engages right dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC), with the former reducing
hippocampal activity (established by effective connectivity analyses). By contrast, thought substitution (lower row) engages a left dominant VLPFC region that does not
reduce hippocampal activity (and in fact, predicts increased hippocampal activity [88]). (B) Measuring intrusions on no-think trials using a trial-by-trial intrusion scale (left,
upper portion; ratings of 2 or 3 indicate an intrusion of the to-be-suppressed memory) reveals intrusions that decline with repeated suppressions (left lower) [91]. Strikingly,
although suppression reduces hippocampal activity overall (right panel, top left subpanel; green bar, think; red bar, no-think), this modulation is driven strongly by trials on
which intrusions occur (right panel, top right subpanel, red bar, intrusions; orange bar, non-intrusions). Hippocampal downregulation (pre-trial - no-think activation,
z-normalized) predicts later memory deficits (baseline - no-think, z-normalized) during intrusions, but not during nonintrusions (right panel, bottom). Abbreviation: fMRI,
functional MRI.
the former has been emphasised more in research on
memory inhibition (e.g., [64,87,88]), and the latter, by
research on motor inhibition [52]. More work is needed
to understand the roles of these two regions in these
forms of stopping, and if a supramodal inhibition process
exists.
Inhibitory control also modulates regions outside the
hippocampus in a content-specific manner. Although
inhibitory control downregulates hippocampal activity
during retrieval suppression, it also modulates activity
in other brain areas, depending on the content being
suppressed. For example, when people suppress retrieval
287
Feature Review
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
memory [66,67]. Echoing this impaired perception, neural
aftereffects are observed in the same fusiform cortex
regions downregulated during retrieval suppression: nothink objects show reduced neural priming (Figure 3Cii).
Given that neural priming is considered a signature of
perceptual memory [102], this finding suggests that perceptual memory traces were disrupted by inhibitory
of visual objects, downregulation is also observed in fusiform regions known to be critical for perceptual awareness
of objects (Figure 3Bii) [67]. Interestingly, on later perceptual identification tests, participants find it more difficult
to see previously suppressed objects in visual noise, compared with either baseline or think objects (Figure 3Ci),
showing that motivated forgetting also impairs implicit
Experimental phases
(A)
Learning
Think
TNT
Duty
Duty
Think
object
x 24
No-think
Wisdom
Wisdom
No-think
object
x 24
Baseline
Priming
Threat
x 24
Unprimed
100
3.5
T values
6
No-think > baseline & think
6
T values
3.5
Think > no-think
Think
No-think
Baseline
Unprimed
2400
2350
2300
2250
Neural priming reduc!on
(ii)
z = –18
Key:
2450
2200
Visual memory suppression
Le$
fusiform
2500
(iii) Fusiform ROI (object localiser)
z = –14
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
ROI-average ac!va!on
(ii)
2550
Reac!on !me (ms)
T values
z = 46
un
mo scramb
vie
c l i p ling
Behavioural priming reduc!on
(i)
Right MFG
3.5
No-think > think
(C)
Memory inhibi!on
Effec"ve connec"vty
(i)
6
(B)
-0%
Nega#ve
Posi#ve
DCM coupling parameters
(MFG --> Fusiform)
TRENDS in Cognitive Sciences
Figure 3. How suppressing retrieval reduces the unconscious influence of unwanted memories, via neocortical inhibition [67]. (A) Adaptation of the think/no-think (TNT)
procedure (67). After learning word–object associations, participants either repeatedly retrieved (think) or suppressed (no-think) objects, using direct suppression [88,93]. On the
final test, participants viewed objects distorted by noise that were gradually revealed, and participants indicated when they could identify the distorted object. (B) Suppressing
retrieval activated the right dorsolateral prefrontal cortex (DLPFC) (i), and reduced activity in fusiform gyrus (ii) (effective connectivity analyses established that the former
modulated the latter). (C) Behavioural and neural aftereffects of suppressing visual memories. All objects showed repetition priming (speeded identification time), relative to novel
objects, but this was reduced for suppressed objects (i). Similarly, all studied objects showed neural priming (reduced neural activity) in fusiform gyrus and the lateral occipital
complex, relative to novel objects, but this was partially reversed for suppressed objects (ii). Negative coupling between DLPFC and fusiform gyrus predicted the magnitude of the
reversal in neural priming on the final perceptual identification test (iii). Abbreviations: DCM, Dynamic Causal Modelling; MGF, middle frontal gyrus; ROI, region of interest.
288
Feature Review
control. Importantly, reductions in neural priming were
well predicted by inhibitory control during the earlier TNT
phase: effective connectivity analyses showed that suppressing retrieval led to negative coupling between right
DLPFC and fusiform gyrus, the magnitude of which predicted the reduced neural priming in fusiform cortex on the
later perception test. Thus, suppressing awareness of visual memories reduced activity not only in the hippocampus, but also in visual cortex, limiting momentary visual
consciousness of the objects and disrupting later perceptual memory. This finding complements fMRI and behavioural evidence for mechanisms that purge unwanted
contents from visual working memory, illustrating their
inhibitory aftereffects on visual neocortex [12,103].
In the foregoing study, inhibitory control may target
visual object regions to reduce reactivation arising from
intrusive memories, reactivation that may arise through
recurrent connections from the hippocampus [67]. This
possibility suggests a broad principle of memory control:
when reminders evoke activity in content-specific areas,
those areas will be targeted by control [67], affecting content
in those regions. Suppressing emotional memories may
provide a second example. When a memory elicits a strong
emotional response, regions involved in affect may be suppressed. Consistent with this, suppressing aversive scenes
(e.g., violence and death) reduces activity in both the hippocampus and the amygdala ([63,64] although see [89]).
Reducing activity in the amygdala could disrupt emotional
learning associated with the event, much like hippocampal
Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
or fusiform modulation disrupts episodic memory or object
priming, respectively. Such modulation may contribute to
the widely observed fading affect bias in autobiographical
memory (Box 4). However, because it remains unknown
whether DLPFC is effectively connected with the amygdala
during suppression, reduced activity may instead be a passive side effect of downregulating recollective activity in the
hippocampus, and the resulting exclusion of the unpleasant
memory from awareness. However, even given this possibility, reduced amygdala activity may reflect success at
achieving a central goal of motivated forgetting in many
real-life circumstances: reduced negative affect arising from
the successful voluntary control of mnemonic awareness.
However, the paradigms discussed here differ from reallife circumstances in important ways. No directed forgetting or retrieval suppression paradigm, for example,
captures the natural motivations that people have for
suppressing awareness of memories that they find personally unwelcome (Box 4). Although the neural mechanisms
identified here likely implement motivated forgetting ‘in
the wild’, this work may underestimate the impact on
retention for someone with a true sustained motive. Understanding the effects of personal motivation will likely
entail a step away from controlled materials, towards
autobiographical experiences unique to an individual.
However, for now, the fundamental control processes that
limit mnemonic processing are emerging, and will inform
our view of how people wilfully shape retention of their
personal experiences.
Box 4. Motives for motivated forgetting
Below is a sampling of motives that may trigger motivated forgetting,
illustrating the breadth of contexts in which these neural mechanisms
are likely to operate.
Regulating negative affect
Memories that evoke fear, anger, sadness, guilt, shame, anxiety, and
embarrassment trigger people to regulate their emotions by suppressing offending memories [118]. In the short term, emotion regulation
helps to reduce negative feelings, returning to a state of homeostasis
[119]. In the long term, this may contribute to the reliably reduced
frequency of negative autobiographical memories compared with
positive ones for most people [120,121]. It may also contribute to the
fading affect bias, wherein affect associated with negative memories
fades more rapidly than other affective content [120,122].
Justifying inappropriate behaviour
People sometimes engage in dishonest acts that conflict with their
desire to be moral. This dissonance creates discomfort that people
may reduce via motivated forgetting. In experimental settings, people
show increased forgetting of moral rules after behaving dishonestly
(e.g., cheating) even though they are equally likely to remember
morally irrelevant rules as participants who do not cheat [123,124].
Maintaining beliefs and attitudes
People’s beliefs are often resistant to contradictory evidence. This
resilience may be supported by selectively forgetting information not
congenial to one’s beliefs. For example, republicans and democrats
show enhanced directed forgetting for attitude statements that are
incongruent with their beliefs, compared with congruent statements
[125]. Moreover, one’s memory can be shaped by selectively
recounting elements of an event [126–128], a form of thought
substitution [88,129]. Intriguingly, this can undermine listeners’
memories of the omitted facts, a phenomenon called ‘socially shared
retrieval-induced forgetting’ [126–128].
Deceiving others and oneself
Memory inhibition may contribute to creating a state of false belief,
necessary to deceive others and even oneself [130]. Consistent with
this, people can use retrieval suppression to disguise guilty knowledge of a crime when confronting reminders to the crime event,
effectively eliminating EEG markers of recollection [131].
Preserving self image
People protect their self-image by selectively remembering feedback
consistent with positive traits and forgetting that which threatens
their sense of self. This robust ‘mnemic neglect’ effect arises despite
holding encoding time constant, and is only present for encoding
traits in relation to oneself, not others. Mnemic neglect is markedly
attenuated, if, before encoding, people receive positive, self-enhancing feedback on a separate task, reducing their urge for selfprotection [132–134].
Forgiving others
Interpersonal relations are sometimes accompanied by the need to
forgive relationship partners for offenses that provoke anger.
Individual differences in forgiveness are well predicted by inhibitory
control ability [135], and it has been argued that memory inhibition
may be key in overcoming rumination about transgressions [136].
Forgiving and forgetting may indeed be closely related.
Maintaining attachment
The need to maintain an attachment relationship with a parent,
guardian, or powerful authority figure (e.g., a boss) may be essential
to survive or thrive in an environment. Behaviours that promote good
relations or attachment to the influential individual may motivate
selective remembering of experiences compatible with attachment
[129], and forgetting of those that are incompatible. Betrayal trauma
theory, for example, posits that motivated forgetting of childhood
abuse by a trusted caretaker is driven by this attachment need [137,138].
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Feature Review
Concluding remarks
In this article, we reviewed evidence for the active role that
people have in shaping retention. We focussed in particular
on the function of inhibitory control processes in modulating the efficacy of memory processes at both encoding and
retrieval. If, upon encoding an experience, people intentionally exclude the event from awareness, retention of the
experience is impaired, compared with cases in which they
intend to remember the event. Although this deficit arises
from several sources, one factor is the termination of
encoding by inhibition, and the disruption of episodic
traces formed up until that point. Similarly, upon encountering reminders to existing memories, people can engage
inhibitory control to stop retrieval. In both encoding and
retrieval suppression, multiple sources of evidence indicate that control mechanisms mediated by the prefrontal
cortex interrupt mnemonic function and impair memory.
Thus, excluding unwanted memories from awareness does
not merely deprive experiences of further rehearsal, it
contributes to forgetting by disrupting the suppressed
memory. However, much remains to be understood about
the pathways and neural mechanisms of this suppression
(Box 5).
Understanding forgetting is one of the fundamental
goals of the science of memory. We have argued that the
focus on incidental forgetting mechanisms over the past
century, although profitable, has profoundly neglected one
of the most systemic forces shaping retention of life events:
ourselves. Forgetting does indeed happen due to forces
beyond our control; but we are, without a doubt, conspirators in our own forgetting. We wield control over mnemonic
processes, choosing, among life’s experiences, winners and
losers for the potent effects of attention, reflection, and
suppression. Modern behavioural and neurobiological
research is revealing how our momentary choices to stop
encoding or retrieval unfold in the brain, and how control
processes disrupt the normal functioning of memory. These
momentary choices are, in turn, driven by our affective,
motivational, social, and cognitive goals. Thus, to understand why human beings remember what they do of their
life histories, a scientific theory of forgetting must account
for the foundational control mechanisms that implement
the ongoing and active role that we play in shaping the fate
of experience in memory.
Box 5. Outstanding questions
! How do motivation and emotion influence the ability to inhibit
memories?
! What are the critical pathways by which DLPFC modulates neural
activity in the hippocampus or neocortex to suppress memories?
! Is there plasticity in the networks underlying memory control that
might be exploited to train people’s management of intrusive
memories?
! What neural changes underlie the disrupted memory performance
associated with memory inhibition and is it related to reconsolidation?
! Are the inhibitory control mechanisms that support the stopping
of encoding and retrieval the same?
! How is activity in the prefrontal–hippocampal memory control
network orchestrated by means of brain oscillations?
! Do cases of psychogenic amnesia arise from motivated forgetting
mechanisms discussed here, or is psychogenic amnesia qualitatively different?
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Trends in Cognitive Sciences June 2014, Vol. 18, No. 6
Acknowledgements
This work was supported by a grant from the UK Medical Research
Council (MC-A060-5PR00) to M.C.A. and by a grant from the Deutsche
Forschungsgemeinschaft (HA 5622/1-1) to S.H. The authors thank Roland
Benoit, Pierre Gagnepain, and Avery Rizio for assistance in creating
Figures 1, 2, and 3, and Jonathan Fawcett for providing comments on the
manuscript.
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569889
research-article2015
PSSXXX10.1177/0956797615569889Catarino et al.Inhibitory Control and Posttraumatic Stress Disorder
Psychological Science OnlineFirst, published on April 6, 2015 as doi:10.1177/0956797615569889
Research Article
Failing to Forget: Inhibitory-Control
Deficits Compromise Memory
Suppression in Posttraumatic Stress
Disorder
Psychological Science
1–13
© The Author(s) 2015
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DOI: 10.1177/0956797615569889
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Ana Catarino1, Charlotte S. Küpper1,2, Aliza Werner-Seidler1,3,
Tim Dalgleish1,3, and Michael C. Anderson1,4
1
Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, United Kingdom;
Department of Clinical Psychology and Psychotherapy, Freie Universität Berlin; 3Cambridgeshire and
Peterborough NHS Foundation Trust (CPFT), Cambridge, United Kingdom; and 4Behavioural and Clinical
Neurosciences Institute, University of Cambridge
2
Abstract
Most people have experienced distressing events that they would rather forget. Although memories of such events
become less intrusive with time for the majority of people, those with posttraumatic stress disorder (PTSD) are afflicted
by vivid, recurrent memories of their trauma. Often triggered by reminders in the daily environment, these memories
can cause severe distress and impairment. We propose that difficulties with intrusive memories in PTSD arise in part
from a deficit in engaging inhibitory control to suppress episodic retrieval. We tested this hypothesis by adapting
the think/no-think paradigm to investigate voluntary memory suppression of aversive scenes cued by naturalistic
reminders. Retrieval suppression was compromised significantly in PTSD patients, compared with trauma-exposed
control participants. Furthermore, patients with the largest deficits in suppression-induced forgetting were also those
with the most severe PTSD symptoms. These results raise the possibility that prefrontal mechanisms supporting
inhibitory control over memory are impaired in PTSD.
Keywords
memory suppression, forgetting, trauma, PTSD, inhibitory control, thought control
Received 8/22/14; Revision accepted 1/8/15
Most people experience traumatic events that they would
rather forget. Frequently, such forgetting is made difficult
because stimuli in the environment resemble something
from the trauma, eliciting intrusive remindings of what
happened—an object in a drawer can remind people of
someone lost because of death or a broken relationship;
driving past a familiar road can remind people of a horrible accident they witnessed. For most trauma survivors,
intrusions decline naturally over the first few months
after trauma (Ehlers, 2010). However, for a small group of
survivors, intrusive memories persist over extended periods in the form of both thoughts and images, causing
marked functional impairment (Sherin & Nemeroff, 2011).
This is a key feature of posttraumatic stress disorder
(PTSD), a debilitating psychiatric condition that results
from exposure to a severe traumatic event and is characterized by persisting clinical symptoms such as intrusive
memories, flashbacks, avoidance, and emotional numbing (American Psychiatric Association, 2013).
Avoidance strategies are commonly employed by people with PTSD to evade reminders of trauma and mitigate
the distress that consequent intrusions cause. However,
although avoidance of the reminders themselves is one
means to reduce memory intrusions, previous research
has shown that people are often able to voluntarily
Corresponding Author:
Ana Catarino, MRC Cognition and Brain Sciences Unit, 15 Chaucer
Rd., Cambridge, CB2 7EF, United Kingdom
E-mail: [email protected]
Downloaded from pss.sagepub.com by guest on April 9, 2015
Catarino et al.
2
suppress unwanted memories even when confronted
with a reminder, a phenomenon known as suppressioninduced forgetting (Anderson & Green, 2001; Anderson
& Hanslmayr, 2014; Anderson et al., 2004; Benoit &
Anderson, 2012; Depue, Banich, & Curran, 2006; Depue,
Curran, & Banich, 2007; Gagnepain, Henson, & Anderson,
2014; Hertel & Mahan, 2008; Levy & Anderson, 2012).
Large individual differences in this suppression ability
have been observed, and these findings suggest that relative difficulties with suppression may underlie memorycontrol difficulties in conditions like PTSD (Hertel &
Gerstle, 2003; Levy & Anderson, 2008; Marzi, Regina, &
Righi, 2014). Do difficulties with intrusive memories in
PTSD arise in part from compromised retrieval suppression? To address this question, in the present study (the
first of its kind, to our knowledge), we examined suppression-induced forgetting in individuals with PTSD.
We wanted to look at people’s ability to suppress
memories of aversive scenes in a naturalistic way. So,
rather than using stimuli such as neutral word pairs or
arbitrary face-scene associations (Depue et al., 2006;
Depue et al., 2007; van Schie, Geraerts, & Anderson,
2013), we asked participants to study object-scene pairs.
The cue objects resembled objects embedded in the aversive target scenes, thus serving as powerful triggers to
remembering the scenes themselves. The stimuli therefore provided a meaningful analogue to situations in
which traumatic intrusions are triggered by environmental elements related to the trauma (Ehlers, 2010; Küpper,
Benoit, Dalgleish, & Anderson, 2014). At the end of the
experiment, we tested memory recall for all the scenes. A
previous study using the same think/no-think (TNT) paradigm found that healthy volunteers could suppress
memories of aversive scenes, but suppression ability was
weaker in those with lower scores on a measure of selfperceived thought-control ability (Küpper et al., 2014).
This study provides a robust platform for examining
memory suppression in PTSD.
We aimed to use this novel paradigm to investigate how
well trauma-exposed individuals, with and without PTSD,
could suppress retrieval of aversive images when triggered
by powerful reminders. We propose that the ability to
engage inhibitory control to support retrieval suppression
is compromised in people with PTSD and that this deficit
poses a central problem in regulating intrusive memories.
Our hypothesis receives support from evidence for inhibitory-control deficits in PTSD as measured by motor
response-inhibition tasks such as the go/no-go and stopsignal tasks (Falconer et al., 2008), as well as by other tasks
that putatively involve memory inhibition, such as directedforgetting and retrieval-induced-forgetting tasks (Amir,
Badour, & Freese, 2009; McNally, 1998; McNally, Metzger,
Lasko, Clancy, & Pitman, 1998). However, although the latter findings are highly promising as evidence for deficient
memory control, their relevance to controlling intrusive
memories is arguably indirect: Retrieval-induced forgetting
concerns the tendency for retrieval of some items to incidentally inhibit other competing memories; directed forgetting concerns the ability to forget an immediately
preceding event, in some cases by terminating encoding.
Though both tasks may involve inhibitory control, neither
addresses the situation most clearly relevant to combating
intrusive memories of trauma: confronting unwelcome
reminders and needing to suppress episodic retrieval to
prevent awareness of an intrusive memory. Studying
retrieval suppression of aversive images may therefore be
particularly relevant to understanding key symptoms of
PTSD, in which the high prevalence of intrusive memories
and images results in significant distress and functional
impairment.
We hypothesized that individuals with PTSD, compared with trauma-exposed individuals who have never
developed PTSD, are less able to suppress retrieval of
aversive scenes when confronted with powerful reminders. Additionally, given previous findings of a correlation
between suppression-induced forgetting and self-perceived thought-control ability (Küpper et al., 2014), we
predicted that lower retrieval-suppression abilities are
associated with more severe PTSD symptoms and also
with lower self-perceived thought-control abilities. These
findings might help answer the key question of why
some individuals recover naturally after trauma, whereas
others continue to experience distressing intrusions that
contribute to the development and persistence of PTSD.
Method
Participants
Eighteen individuals with a current diagnosis of PTSD (11
females; mean age = 34 years, SD = 13) and a control
group of 18 trauma-exposed individuals with no current
or past history of PTSD (11 females; mean age = 37 years,
SD = 14) were recruited from the local community and
departmental participant panels through print advertisements. Sample size was calculated with an a priori power
analysis, using the effect sizes reported by Küpper et al.
(2014), who used identical procedures, materials, and
dependent measures. We determined that a minimum
sample size of 7 per group would be necessary for 95%
power to detect an effect. Additionally, our choice of a
sample size of 18 per group is consistent with sample
sizes used in previous think/no-think (TNT) studies
(Anderson et al., 2004; Benoit & Anderson, 2012; Benoit,
Hulbert, Huddleston, & Anderson, 2015; Gagnepain
et al., 2014; Küpper et al., 2014). Diagnostic status was
determined using the Structured Clinical Interview for the
DSM-IV Axis I Disorders (First, Spitzer, Gibbon, &
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Inhibitory Control and Posttraumatic Stress Disorder
3
Table 1. Comparison of the Demographic and Clinical Characteristics of the Posttraumatic Stress Disorder (PTSD) and Control
Groups
Characteristic
Age (mean in years)
Gender
PDS (mean score)
IES-R (mean score)
Intrusion
Avoidance
Hyperarousal
BDI-II (mean score)
STAI-T (mean score)
STAI-S (mean score)
TCAQ (mean score)
NART (mean score)
PTSD group
(n = 18)
Control group
(n = 18)
34.2 (13.4)
11 females, 7 males
29.4 (7.4)
44.2 (17.2)
15.6 (7.9)
15.7 (7.7)
13.0 (5.4)
25.4 (13.8)
54.1 (9.0)
40.1 (7.4)
55.7 (14.7)
31.3 (7.5)
36.8 (14.2)
11 females, 7 males
5.9 (5.4)
11.0 (13.4)
4.2 (5.1)
5.2 (7.5)
1.6 (3.4)
7.2 (5.5)
39.3 (8.3)
31.2 (6.4)
83.9 (14.6)
34.2 (6.0)
Group comparison
t(34) = –0.57, p = .57, d = –0.19, 95% CI = [–11.94, 6.72]
t(34) = 10.84, p < .001, d = 3.63, 95% CI = [19.09, 27.91]
t(34) = 6.48, p < .001, d = 2.15, 95% CI = [22.80, 43.64]
t(29.073) = 5.12, p < .001, d = 1.71, 95% CI = [6.81, 15.86]
t(34) = 4.11, p < .001, d = 1.38, 95% CI = [5.28, 15.61]
t(34) = 7.60, p < .001, d = 2.53, 95% CI = [8.38, 14.51]
t(22.212) = 5.20, p < .001, d = 1.73, 95% CI = [10.93, 25.41]
t(34) = 5.04, p < .001, d = 1.71, 95% CI = [7.43, 17.46]
t(33) = 3.81, p = .001, d = 1.29, 95% CI = [4.15, 13.64]
t(34) = –5.79, p < .001, d = –1.92, 95% CI = [–38.14, –18.31]
t(34) = –1.31, p = .20, d = –0.43, 95% CI = [–7.53, 1.64]
Note: Standard deviations are given in parentheses. CI = confidence interval for the group difference; PDS = Posttraumatic Stress Diagnostic Scale
(Foa, 1995); IES-R = Impact of Event Scale–Revised (Weiss, 2007); BDI-II = Beck Depression Inventory-II (Beck, Steer, & Brown, 1996); STAI-T =
Trait score on the State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983); STAI-S = State score on the State-Trait
Anxiety Inventory (Spielberger et al., 1982); TCAQ = Thought Control Ability Questionnaire (Luciano, Algarabel, Tomás, & Martínez, 2005);
NART = National Adult Reading Test (Nelson, 1982).
Williams, 1996). Exclusion criteria for PTSD participants
included a current diagnosis of psychosis or borderline
personality disorder. Control participants with any current psychiatric disorder were also excluded.
The range of participants’ trauma experiences was
similar between the two groups and included experiencing or witnessing serious accidents (PTSD: n = 4; control:
n = 9), violence (PTSD: n = 2; control: n = 3), sexual
assault (PTSD: n = 2; control: n = 1), life-threatening illnesses (PTSD: n = 3; control: n = 4), combat situations
(PTSD: n = 1), and traumatic death of family members or
close friends (PTSD: n = 6; control: n = 1). Time since
trauma was matched across the groups and varied from 3
months to more than 5 years. We recruited a heterogeneous trauma group because our hypothesis was that a
key symptom of the disorder—intrusive, difficult-to-control memories—reflects at least a partial deficit in retrieval
suppression, which ought to transcend trauma type.
All participants were native English speakers with no
history of severe head injury, neurological disease, or
learning disability. Further demographic details are provided in Table 1. This study received ethical approval
from the Cambridgeshire and Peterborough Research
Ethics Committee. All participants provided written
informed consent.
Materials
The TNT memory task involved asking participants initially to study various object-scene pairs. Next, in the
main phase of the experiment, the cue objects were
presented, and participants were asked to either recall or
suppress their memories for the associated scenes. At the
final test, participants were shown the cue objects and
asked to provide brief descriptions of the associated
scenes.
The stimuli used were 60 object-scene pairs: 48 critical
pairs and 12 fillers (Fig. 1). The scenes for these pairs
were emotionally negative images taken from the
International Affective Picture System (Lang, Bradley, &
Cuthbert, 2008) and online sources. We used negative
emotional material rather than trauma-specific scenes
because our hypothesis was that there is a generic inability to inhibit memories for aversive information in PTSD.
The cue objects were colored photographs of familiar,
neutral objects (taken from Brady, Konkle, Alvarez, &
Oliva, 2008). Each cue object was chosen to resemble an
item that was already naturally embedded as an incidental detail in its paired scene but not intrinsically related to
the gist of the scene. This prevented guessing of the
scenes during later recall. The 48 critical pairs were
divided into three sets (referred to as sets A, B, and C)
that were matched on salience of the cues, as well as the
emotional valence and arousing nature of the scenes
(Küpper et al., 2014). On a scale from 1 (not salient) to 5
(very salient), the mean rated salience of the cues was 2.7
for set A, 2.9 for set B, and 2.4 for set C; on a scale from
1 (unpleasant) to 9 (pleasant), the mean rated emotional
valence of the scenes was 3.5 for set A, 3.3 for set B, and
3.3 for set C; and on a scale from 1 (unarousing) to 9
(arousing), the mean arousal rating of the scenes was 4.8
for set A, 4.7 for set B, and 4.8 for set C. Assignment of
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Catarino et al.
4
(STAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs,
1983) is a self-administered questionnaire assessing trait
and state anxiety. The Posttraumatic Stress Diagnostic
Scale (PDS; Foa, 1995) is a self-administered questionnaire designed to aid in the detection and diagnosis of
trauma-related symptoms. The National Adult Reading
Test (NART; Nelson, 1982) is a researcher-administered
test used to estimate an individual’s level of intellectual
ability.
TNT procedure
Fig. 1. A representative object-scene pair consisting of a neutral cue
object and an unpleasant scene. Each cue object was chosen to resemble an item that was naturally embedded as an incidental detail in the
associated scene.
We adapted the TNT procedure, developed by Anderson
and Green (2001), to study the suppression of aversive
scenes. The paradigm had three phases: study phase,
TNT phase, and final test phase (Fig. 2).
the sets to the three conditions (see the TNT Procedure
section) was counterbalanced across participants.
At the end of the experiment, participants filled out
the following questionnaires. The Thought Control
Ability Questionnaire (TCAQ; Luciano, Algarabel,
Tomás, & Martínez, 2005) is a 25-item questionnaire that
assesses the self-perceived ability to exert control over
thought intrusions; higher scores reflect better control
ability. The Beck Depression Inventory-II (BDI-II; Beck,
Steer, & Brown, 1996) is a self-administered questionnaire measuring the intensity of depressive symptoms.
The Impact of Event Scale–Revised (IES-R; Weiss, 2007)
is a self-report measure assessing the magnitude of
symptomatic response, in the past 7 days, to a specific
traumatic life event—in this case, the participant’s most
significant trauma. The State-Trait Anxiety Inventory
Study phase. Participants started by studying all 60
object-scene pairs, which were presented for 6 s each in
a blocked randomized order. Test-feedback cycles then
followed. On each test trial, participants were shown a
cue object and indicated, by pressing the “yes” or “no”
button, whether they could recall the scene with which it
had been paired. If they answered “yes,” three scenes
were presented, and they were asked to select the correct
one. Participants then were shown the correct objectscene pair again for 2.5 s, as feedback to enhance encoding. The testing cycled through all items repeatedly until
participants reached a set criterion of at least 60% correct
recognition (all succeeded within four cycles). When
they reached this criterion, a final test cycle (without
feedback) including all pairs assessed which pairs had
been learned.
Study Phase
TNT Phase
Final Test Phase
People in a
wardrobe. . .
Dead man and
boy lying in a
ditch next to a
big basket. . .
Fig. 2. Schematic representation of the experimental procedure. In the study phase, participants encoded cue-target pairs. During the think/
no-think (TNT) phase, participants were instructed to directly suppress memories associated with cue objects presented inside a red frame
and to recall memories associated with cue objects presented inside a green frame. Finally, in the test phase, participants were asked to
remember and verbally describe all the scenes that they had previously recalled (recall items) or suppressed (suppress items), as well as all
the scenes that they had initially learned but had not seen during the TNT phase (baseline items).
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Inhibitory Control and Posttraumatic Stress Disorder
TNT phase. In this phase, participants were shown the
cue objects alone. Each object appeared for 3 s in the center of the screen, surrounded by a colored frame, and was
followed by a fixation cross of varying duration (interstimulus interval = 2 s ± 600 ms). Participants were asked to
suppress the associated scene when an object was surrounded by a red frame (no-think trial) and to recall the
associated scene when an object was surrounded by a
green frame (think trial). At the start of this phase, participants were given direct suppression instructions for nothink trials (Benoit & Anderson, 2012), which requested
them to suppress the associated scene and additionally
avoid any distracting thoughts from coming into awareness. These instructions reduce the use of thought substitution and self-distraction, which results in a better measure
of the role of inhibitory control in memory suppression
(Benoit & Anderson, 2012). For recall trials, participants
were instructed to recall the scene in as much detail as
possible. Practice trials using filler items were presented to
ensure that subjects understood the instructions. Following practice, participants were presented with 32 critical
experimental cue objects: 16 in the recall condition and 16
in the suppress condition. The critical trials were split into
five blocks, and each of these 32 objects was presented
twice in each block. Thus, each associated scene was suppressed or recalled 10 times over the course of these trials.
The remaining 16 critical object-scene pairs were baseline
items; that is, they were learned in the study phase but
were not presented in the TNT phase.
Final test phase. Participants’ memory for all critical
scenes was tested after the TNT phase. All cue objects
(from the recall, suppress, and baseline conditions) were
presented, one at a time and without a colored frame. On
each trial, participants were given 15 s to verbally describe
the associated scene in as much detail as possible, so that
it could be uniquely identified. The descriptions were
recorded for later transcription.
Dependent measures
Participants’ descriptions were scored on three dependent measures assessing quantitative and qualitative
aspects of the memories. For the identification measure,
a description was scored as correct if it included enough
detail that the specific scene could be uniquely identified
(Depue et al., 2006). For the details measure, each
description was divided into small, meaningful segments
conveying independent information, and the number of
correct details was counted. Finally, gist was defined as
any element pertaining to the central story of a scene that
could not be changed or excluded without changing the
main theme. Prior to the experiment, two independent
judges determined two to four specific elements that
5
contained the general gist of each scene. Descriptions
were scored as correct on the gist measure if they
included all necessary elements of the scene.
All descriptions were scored by two independent raters who were blind to the conditions. Interrater agreement was high—identification: r = .99; details: r = .95;
gist: r = .93.
Data analysis
Suppression-induced forgetting (lower memory performance for suppress compared with baseline items) was
assessed using a mixed analysis of variance (ANOVA) with
condition (baseline, suppress) as a within-subjects factor
and group (PTSD, control) and set assignment (i.e., which
of sets A, B, and C was assigned to each condition) as
between-subject factors. Facilitation effects (better memory for recall compared with baseline items) were assessed
using the same model, but the levels for condition were
instead baseline and recall. ANOVAs were performed separately for each dependent measure (identification, gist,
and details). Effect sizes ( p2 or Cohen’s d) and 95% confidence intervals (CIs) are reported. Nonsignificant effects
were further explored through Bayesian analysis, by computing whether Bayes factors favored the alternative or
null hypothesis.
We report correlations between the dependent variable showing the strongest suppression-induced-forgetting effect in the primary analysis (the details measure)
and TCAQ and PDS scores (though correlations with all
memory measures were similar). Self-perceived thoughtcontrol ability as measured by the TCAQ and PTSD
symptomatology as measured by the PDS were chosen as
main predictors of interest for the correlation analysis to
test our hypothesis that suppression-induced forgetting is
related to thought control in daily life, as well as to the
severity of PTSD symptoms. We also calculated semipartial correlations between our details measure and TCAQ
and PDS scores covarying out BDI-II scores in order to
explore the role of depression symptoms as a possible
confounding factor, given their high prevalence of comorbidity with PTSD. Rank correlations were used when data
differed significantly from normality. Alpha was set at .05.
All statistical analyses were repeated using final-test data
conditionalized on correct initial learning of the objectscene pairs. A pair was judged as initially learned if it was
recalled correctly in the final test cycle of the study phase.
Results
Description of the samples
Demographic and clinical information about the participants is presented in Table 1. As expected, the two
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Catarino et al.
6
groups differed significantly in their scores for clinical
measures tapping into PTSD symptomatology and symptoms that commonly co-occur with PTSD, such as depression and anxiety.
TNT-task performance
If, as we hypothesized, inhibitory control over memory
retrieval is impaired in PTSD, control participants should
exhibit larger suppression-induced forgetting than PTSD
patients do. This predicted pattern was confirmed for all
three dependent measures (Fig. 3).
Identification. A significant group difference was
found in suppression-induced forgetting, as revealed by
a significant condition-by-group interaction, F(1, 30) =
7.556, p = .010, p2 = .201. This interaction was driven by
a significant inversion of suppression-induced forgetting
in the PTSD group (i.e., better memory performance for
suppress items compared with baseline items; M =
−5.2%), F(1, 15) = 5.288, p = .036, p2 = .261 (Bayes factor
favoring the alternative hypothesis = 2.09), in the absence
of significant suppression in the control group (M = 3.5%,
n.s.). Although the PTSD group showed a significant
facilitation effect (M = 5.9%), F(1, 15) = 7.525, p = .015,
2
p = .334, and the control group did not (M = 2.8%, n.s.),
there was no significant group difference in facilitation,
F(1, 30) = 1.215, p = .279, p2 = .039 (Fig. 3a). The absence
of reliable facilitation and suppression effects in the control group may have been due to ceiling effects.
Gist. A significant group difference in suppressioninduced forgetting was observed, as indicated by a significant condition-by-group interaction, F(1, 30) = 4.573,
p = .041, p2 = .132. This interaction was driven by significant suppression-induced forgetting in the control group
(M = 10.8%), F(1, 15) = 22.123, p < .001, p2 = .596, in the
absence of significant suppression-induced forgetting in
the PTSD group (M = 1.7%), F(1, 15) = 0.297, p = .594,
2
p = .019. A Bayesian analysis indicated strong evidence
in favor of the null hypothesis, confirming the absence of
suppression-induced forgetting in the PTSD group (Bayes
factor in favor of the null hypothesis = 3.74). No significant facilitation effect was found in either group (PTSD:
M = −0.3%; control: M = −0.7%; Fig. 3b).
Details. As for the two previous measures, a significant
group difference in suppression-induced forgetting was
observed, established by a significant condition-by-group
interaction, F(1, 30) = 14.231, p = .001, p2 = .322. This
interaction was driven once again by a significant suppression effect in the control group (M = 1.19), F(1, 15) =
33.548, p < .001, p2 = .691, in the absence of significant
suppression in the PTSD group (M = −0.14), F(1, 15) =
0.224, p = .643, p2 = .015. Again, a Bayesian analysis
indicated strong evidence in favor of the null hypothesis,
confirming the absence of suppression-induced forgetting in the PTSD group (Bayes factor in favor of the null
hypothesis = 3.70). Although the PTSD group showed a
significant facilitation effect (M = 0.68), F(1, 15) = 5.575,
p = .032, p2 = .271, and the control group showed only
a marginally significant effect (M = 0.56), F(1, 15) = 4.368,
p = .054, p2 = .226, the difference between the groups
was not significant, F(1, 30) = 0.100, p = .754, p2 = .003
(Fig. 3c).
Measures of overall learning. No significant group
differences were found in initial learning of the pairs
(control: M = 87%, SD = 11%; PTSD: M = 93%, SD = 7%).
The groups also did not differ in accuracy for baseline
items, whether assessed by the identification measure
(control: M = 92%, SD = 8%; PTSD: M = 90%, SD = 12%),
the gist measure (control: M = 58%, SD = 13%; PTSD: M =
55%, SD = 18%), or the details measure (control: M = 10.27,
SD = 2.38; PTSD: M = 9.39, SD = 2.62). Finally, the groups
also did not differ in accuracy for recall items, whether
assessed by the identification measure (control: M = 95%,
SD = 7%; PTSD: M = 96%, SD = 7%), the gist measure
(control: M = 57%, SD = 16%; PTSD: M = 55%, SD = 16%),
or the details measure (control: M = 10.82, SD = 2.26;
PTSD: M = 10.07, SD = 2.03). These results show that the
two groups had comparable learning and baseline memory performance, and that there was no clear bias toward
better recall of the emotionally negative scenes in the
PTSD group.
Thought Control Ability Questionnaire
The control group reported higher self-perceived
thought-control ability than did the PTSD group (Table
1), although TCAQ scores across the whole sample
showed a continuous normal distribution. Across all participants, suppression-induced forgetting on our details
measure and TCAQ scores had a robust positive correlation (Kendall’s = .5, p < .001; Fig. 4a). This correlation
remained significant when we covaried out BDI-II scores
using semipartial correlation analyses ( = .34, p = .004),
which suggests that the correlation was not simply a
function of participants’ current symptom levels.
Posttraumatic Stress Diagnostic Scale
The PTSD group reported more severe PTSD symptoms
than did the control group (Table 1). As expected, the
distribution of PDS scores across groups was not continuous, given that the control group was mostly at floor
for this measure. For this reason, correlation analyses
were performed only within the PTSD group. A
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Inhibitory Control and Posttraumatic Stress Disorder
a
Control Group
b
85%
80%
75%
65%
Scenes Correctly Recalled (%)
Scenes Correctly Recalled (%)
90%
Baseline
Recall
***
**
50%
45%
Recall
10.0
9.0
Recall
Suppress
Number of Details Recalled
Number of Details Recalled
Details
***
Baseline
80%
75%
Baseline
Recall
Suppress
Baseline
Recall
Suppress
Recall
Suppress
60%
55%
50%
45%
12.0
***
11.0
8.0
85%
40%
Suppress
c
12.0
90%
65%
55%
Baseline
*
95%
70%
Suppress
60%
40%
*
100%
*
95%
70%
PTSD Group
Scenes Correctly Recalled (%)
Scenes Correctly Recalled (%)
Identification
100%
Gist
7
11.0
*
10.0
9.0
8.0
Baseline
Fig. 3. Memory performance in the final test for the trauma-exposed control group (n = 18; left column) and the
posttraumatic stress disorder (PTSD) group (n = 18; right column). Results are shown separately for baseline, recall,
and suppress items for each dependent measure: (a) identification, (b) gist, and (c) details. Suppression-induced
forgetting is when significantly fewer suppress than baseline items are remembered. Memory facilitation is when
significantly more recall than baseline items are remembered. Error bars represent ±1 SE. Asterisks indicate significant differences between conditions (*p < .05; **p < .01; ***p < .001).
marginally significant negative correlation was found
between suppression-induced forgetting on the details
measure and PDS scores in the PTSD group (Kendall’s
= −.33, p = .07; Fig. 4b). This correlation was rendered
significant when we covaried out BDI-II scores using
semipartial correlation analyses ( = −.35, p = .049).
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Catarino et al.
8
a
b
PTSD Group
Control Group
Number of Details Recalled
(Baseline – Suppress)
4.00
4.00
τ = .51, p < .001
3.00
3.00
2.00
2.00
1.00
1.00
0.00
20
40
60
80
100
120
0.00
–1.00
–1.00
–2.00
–2.00
–3.00
τ = –.33, p = .07
15
–3.00
TCAQ Score
20
25
30
35
40
45
PDS Score
Fig. 4. Scatter plots illustrating the relation between suppression-induced forgetting as assessed by the details measure (number of details for
baseline items – number of details for suppress items) and (a) self-perceived thought-control ability across the entire sample and (b) severity of
posttraumatic stress disorder (PTSD) symptoms in the PTSD group. Thought-control ability was assessed by the Thought Control Ability Questionnaire (TCAQ; Luciano, Algarabel, Tomás, & Martínez, 2005), and PTSD symptoms were assessed by the Posttraumatic Stress Diagnostic Scale (PDS;
Foa, 1995).
The effects of depressive symptoms
The PTSD group had significantly higher BDI-II scores
than did the control group (see Table 1). Because depressive symptoms have been associated with deficits in suppression-induced forgetting (Hertel & Gerstle, 2003;
Joormann, Hertel, LeMoult, & Gotlib, 2009), the suppression deficit in the PTSD group may reflect effects of this
comorbidity. As just noted, however, the negative correlation between PTSD symptom severity and suppressioninduced forgetting actually strengthened after BDI-II
scores were partialed out, which suggests that depressive
symptoms may not have been the main factor underlying
the observed deficit.
To scrutinize this issue further, we performed a median
split within each group according to BDI-II scores. We
found that within the PTSD group, suppression-induced
forgetting (i.e., difference in memory performance
between baseline and suppress items) was never found
in either the low-BDI-II group (mean BDI-II score = 15.1,
SD = 8.8) or the high-BDI-II group (mean BDI-II score =
35.7, SD = 9.4), regardless of the dependent measure
examined—identification: M = −5% for the low group
and −6% for the high group; gist: M = 7% for the low
group and −3% for the high group; details: M = −0.03 for
the low group and −0.24 for the high group. For the control group, in contrast, suppression-induced forgetting
was observed in both the low-BDI-II group (mean BDI-II
score = 3.6, SD = 3.1) and the high-BDI-II group (mean
BDI-II score = 10.9, SD = 4.9), despite the small sample
sizes (n = 9). Specifically, both groups showed significant
suppression-induced forgetting on the gist measure—low
group: M = 13%, F(1, 6) = 30.388, p = .001, p2 = .835;
high group: M = 8%, F(1, 6) = 8.641, p = .026, p2 = .590.
They also showed highly significant suppression-induced
forgetting on the details measure—low group: M = 1.35,
F(1, 6) = 11.631, p = .014, p2 = .660; high group: M =
1.03, F(1, 6) = 27.222, p = .002, p2 = .819. Note, though,
that the high-BDI-II group showed numerically less suppression-induced forgetting than did the low-BDI-II
group.
When we more closely matched depression symptoms
by comparing the low-BDI-II PTSD group with the highBDI-II control group, we observed a marginally significant group-by-condition interaction for the details
measure, F(1, 16) = 4.032, p = .062, p2 = .201. Significant
suppression-induced forgetting was observed for the
high-BDI-II control group, but not for the low-BDI-II
PTSD group, despite BDI-II scores that did not differ
appreciably. Taken together, these results suggest that the
observed deficits in suppression-induced forgetting in
the PTSD group are unlikely to solely reflect the effects
of depressive symptomatology on memory control.
Conditionalized final-test data
Analyses of the data conditional on correct initial learning yielded similar results, except in the case of the identification measure. In that analysis, the group difference
in suppression-induced forgetting only approached significance, F(1, 30) = 3.869, p = .058, p2 = .114.
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Inhibitory Control and Posttraumatic Stress Disorder
Discussion
Inhibitory control mechanisms serve an important role in
various domains of cognition. If faced with a falling
object, one’s first instinct is to try and catch it. However,
if the object is a sharp knife, one suppresses this prepotent motor response, letting the knife fall on the floor to
preserve one’s physical well-being. A similar inhibitory
response occurs with memory. When people are confronted with reminders of unpleasant experiences, memories flood awareness, and attempts to stop the retrieval
process follow quickly. Previous research suggests that
suppressing retrieval of intrusive memories reduces their
accessibility (Anderson & Green, 2001; Küpper et al.,
2014), raising the possibility that, just as inhibiting actions
helps preserve physical well-being, inhibiting memories
may preserve emotional well-being.
We hypothesized that deficient inhibitory control in
people with PTSD compromises their ability to suppress
episodic retrieval, causing persistent difficulties with
intrusive memories. Supporting this hypothesis, our
results showed that for all measures, suppression-induced
forgetting was diminished in the PTSD group compared
with the control group. We also observed a negative correlation between suppression-induced forgetting and
PTSD symptom severity, which indicates that retrieval
suppression is most compromised in people with the
most severe symptoms. This finding is consistent with
clinical observations of more frequent intrusions in cases
of more severe PTSD (Ehlers, 2010; Sherin & Nemeroff,
2011). Finally, we found a large positive correlation
between suppression-induced forgetting and selfreported thought-control ability, which suggests that this
forgetting reflects mechanisms contributing to memory
control in daily life (Küpper et al., 2014; Williams et al.,
2010). Taken together, our results suggest that difficulties
in suppressing memories of aversive scenes may be
related to PTSD patients’ broader difficulties controlling
intrusive memories outside the laboratory.
It is important to note that our two groups performed
comparably during initial learning and also on the final
test for baseline and recall items. Indeed, PTSD participants showed reliable facilitation for recall items. Thus,
the group differences in suppression-induced forgetting
cannot be explained by differences in overall performance or by a bias toward better memory for negative
scenes in the PTSD group. Enhanced memory for think
(recall) items was weak in both groups, however; this is
a recurring observation in TNT studies using complex
event and scene stimuli (Depue et al., 2007; Küpper
et al., 2014; Stephens, Braid, & Hertel, 2013), and it
remains to be understood. Clearly, however, the PTSD
and control groups differed primarily in whether suppression impaired memory for suppress items, and our
9
results are consistent with deficient inhibitory control
over retrieval in people with PTSD. It would be useful for
future studies to provide converging evidence that these
suppression-induced forgetting effects reflect lingering
inhibition of suppressed memories, as found in other
work (Anderson & Green, 2001).
What remains unclear, however, is whether impaired
memory control is caused by PTSD, or is a risk factor for
its development. Although our data do not provide an
answer to this question, our results show that trauma
exposure, by itself, is not sufficient to impair memory
suppression, as all participants had experienced trauma.
It is noteworthy, however, that the correlation between
self-perceived thought-control ability (TCAQ score) and
suppression-induced forgetting remained significant after
we covaried out a measure of emotional functioning (the
BDI-II), which raises the possibility that deficient inhibitory control predisposes people to develop PTSD (Küpper
et al., 2014). This possibility is consistent with evidence
indicating that lower scores on cognitive-ability measures
predict increased risk of developing PTSD (McNally &
Shin, 1995), even in prospective designs (Breslau, Lucia,
& Alvarado, 2006; Macklin et al., 1998). If deficient inhibitory control predisposes people to develop PTSD, TCAQ
scores may provide a screening tool to identify those at
greater risk.
Regardless of its origins, deficient retrieval suppression in PTSD may reflect disordered prefrontal control
over memory-related brain areas. Retrieval suppression
engages right dorsolateral prefrontal cortex (DLPFC) to
reduce activity in the hippocampus (for a review, see
Anderson & Hanslmayr, 2014; Anderson et al., 2004;
Benoit & Anderson, 2012; Benoit et al., 2015; Depue
et al., 2007; Gagnepain et al., 2014; Levy & Anderson,
2012; Paz-Alonso, Ghetti, Matlen, Anderson, & Bunge,
2009). Reports of structural and functional abnormalities
in prefrontal cortex of people with PTSD (Fani et al.,
2012; Lyoo et al., 2011; Moores et al., 2008; Shin et al.,
2004; Yamasue et al., 2003), along with findings that they
have deficient inhibitory control, suggest that they have
difficulty engaging this DLPFC-hippocampal pathway,
which causes the symptom of persistent reexperiencing.
If these neural mechanisms are impaired in PTSD, then
suppression may not only be ineffective but may actually
increase symptom severity and persistence if intruding
memories are enhanced, a finding confirmed by previous
research (Brewin, 2011; Ehlers & Clark, 2000; Ehlers,
Mayou, & Bryant, 1998; Mayou, Ehlers, & Bryant, 2002).
The present study shows that one can investigate suppression-induced forgetting in a laboratory environment
in a way that is relevant to deficits experienced by people
with PTSD. However, although we used a relatively naturalistic design, the aversive images elicited during the
TNT task only approximate the intrusive memories
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Catarino et al.
10
people experience in real life. We can speculate that the
latter may be either more or less vulnerable to suppression than the former. On the one hand, traumatic memories, usually associated with guilt, fear, or anger, may be
more difficult to suppress than our aversive images.
Additionally, PTSD symptoms such as negative selfappraisal may make suppressing intrusive memories difficult, increasing symptom severity (Meiser-Stedman,
Dalgleish, Glucksman, Yule, & Smith, 2009). On the other
hand, trauma survivors may be particularly motivated to
suppress their intrusive memories, persisting in their suppression efforts over long periods, and thereby enhancing forgetting. Previous research indicates that cumulative
suppression efforts totaling 1 min for a specific item (over
a think/no-think task lasting 30 to 45 min) cause suppression-induced forgetting that can last from 24 hr to 1 week
(Anderson & Huddleston, 2012). It is therefore possible
that trauma survivors’ persisting efforts yield longer-lasting suppression-induced forgetting than our TNT task
does.
The impact of long-term suppression efforts on symptom severity remains unexplored. It is widely believed
that avoiding distressing memories exacerbates PTSD
symptoms by keeping suppressed memories accessible.
Cognitive behavioral therapy for PTSD is thought to be
effective because it encourages patients to stop avoiding
memories and to confront reminders until the traumatic
memories become less distressing (Kar, 2011). We suggest an alternative possibility, proposing that there is an
important distinction between avoiding reminders, on
the one hand, and avoiding the memory (via suppression) given that a reminder is confronted, on the other.
The former strategy should preserve memories by depriving people of opportunities to forget via inhibitory control, whereas avoidance by retrieval suppression is
beneficial, if implemented effectively. Thus, like cognitive
behavioral therapy, retrieval suppression forces people to
confront reminders, but to learn to control awareness of
their memories. One interesting hypothesis is that cognitive behavioral therapy is effective, in part, because confronting reminders and learning to redirect one’s thoughts
relies on the inhibitory processes observed in the current
experiment. If so, perhaps interventions may be devised
to complement behavioral therapy by training PTSD
patients to be more effective at memory control.
Another issue is our choice to use generically aversive
materials rather than materials tailored to individual participants’ trauma experiences. This choice arose from our
hypothesis that impaired retrieval suppression should not
be limited to memories of the trauma itself, but should
broadly affect memory control. Although our data support this hypothesis, a remaining question is whether
retrieval-suppression deficits in PTSD extend to nonemotional material.
Another question for future research is whether deficient retrieval suppression extends to other psychiatric
conditions. Although we focused on PTSD, retrievalsuppression deficits may reflect a transdiagnostic problem associated with other disorders in which ruminative
tendencies are common and intrusive memories prevail,
such as depression, generalized anxiety disorder, and
obsessive-compulsive disorder (Hertel & Gerstle, 2003;
Marzi et al., 2014; Patel et al., 2007; Speckens, Hackmann,
Ehlers, & Cuthbert, 2007). Indeed, depressed participants
show diminished suppression-induced forgetting (though
not when given thought-substitution strategies; Joormann
et al., 2009), as do ruminators regardless of depression
symptomatology (Fawcett et al., 2015; Hertel & Gerstle,
2003). Whether this recurring pattern reflects a single
transdiagnostic deficit with a common cause or a collection of disorder-specific deficits remains to be established. One must also consider whether worse suppression
reflects a deficiency in memory control or, instead, a failure to sustain effort arising from a compromised emotional state. What is clear in the present study, however,
is that deficient suppression occurred in PTSD patients
regardless of their depression symptoms, and this suggests that depression is not a prerequisite for this deficit.
This possibility should be confirmed in future work.
Although remembering past experiences serves an
adaptive role, intrusive memories of negative events can
severely affect emotional well-being. This is particularly
true for people with PTSD, whose everyday functioning
is severely impaired by recurrent intrusive memories of
trauma. We used a naturalistic design to investigate, for
the first time, voluntary retrieval suppression of aversive
scenes in PTSD. Our findings suggest that the difficulties
with intrusive memories in PTSD may be caused, in part,
by deficient inhibitory control over retrieval. Moreover,
by virtue of the correlation between deficits in inhibitory
control and scores on clinical scales, our results affirm
the relevance of retrieval suppression to memory-control
deficits in PTSD and other disorders characterized by
uncontrolled memories and thoughts. Along with the
rapidly emerging literature on the neural mechanisms
supporting retrieval suppression (for a review, see
Anderson & Hanslmayr, 2014), the present findings open
the door to a precise characterization of the neurobiological mechanisms underlying PTSD and other disorders
of mnemonic control.
Author Contributions
All authors were involved in developing the study’s concept and
design. A. Catarino and C. S. Küpper were involved in data collection and analyses, under the supervision of M. C. Anderson.
All authors were involved in interpretation of the data.
A. Catarino and M. C. Anderson drafted the manuscript, and
A. Werner-Seidler and T. Dalgleish provided critical revisions.
Downloaded from pss.sagepub.com by guest on April 9, 2015
Inhibitory Control and Posttraumatic Stress Disorder
All authors approved the final version of the manuscript for
submission. A. Catarino had full access to all data in the study
and takes responsibility for the integrity and accuracy of the
analyses.
Acknowledgments
We thank Jonathan Fawcett for contributions toward the correlation analyses reported here.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.
Funding
This work was supported by UK Medical Research Council
grant MC-A060-5PR00 (to M. C. Anderson).
Open Practices
The stimuli used in the main think/no-think task reported in
this article are available at https://osf.io/t9j8e/?view_only=f171
281f212f4435917b16a9e581a73b. The complete Open Practices
Disclosure for this article can be found at http://pss.sagepub
.com/content/by/supplemental-data.
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ARTICLES
Retrieval induces adaptive forgetting of competing
memories via cortical pattern suppression
© 2015 Nature America, Inc. All rights reserved.
Maria Wimber1,2, Arjen Alink2, Ian Charest2, Nikolaus Kriegeskorte2 & Michael C Anderson2,3
Remembering a past experience can, surprisingly, cause forgetting. Forgetting arises when other competing traces interfere
with retrieval and inhibitory control mechanisms are engaged to suppress the distraction they cause. This form of forgetting is
considered to be adaptive because it reduces future interference. The effect of this proposed inhibition process on competing
memories has, however, never been observed, as behavioral methods are ‘blind’ to retrieval dynamics and neuroimaging
methods have not isolated retrieval of individual memories. We developed a canonical template tracking method to quantify
the activation state of individual target memories and competitors during retrieval. This method revealed that repeatedly
retrieving target memories suppressed cortical patterns unique to competitors. Pattern suppression was related to engagement
of prefrontal regions that have been implicated in resolving retrieval competition and, critically, predicted later forgetting.
Thus, our findings demonstrate a cortical pattern suppression mechanism through which remembering adaptively shapes
which aspects of our past remain accessible.
Remembering, it seems, is a double-edged sword. Research in humans
and animals points to the pivotal role of retrieval in shaping and stabilizing memories1,2. However, the remembering process also induces
forgetting of other memories that hinder the retrieval of the memory
that we seek1,3,4. It has been hypothesized that this surprising dark
side of remembering is caused by an inhibitory control mechanism
that suppresses competing memories and causes forgetting; this
putative process is adaptive because it limits current and future distraction from competitors5,6. However, no study has ever directly
observed memories as they are suppressed by this hypothesized
inhibitory control mechanism. Behavioral methods are, by their
nature, blind to the internal processes unfolding during retrieval, and
neuroscience has lacked methods capable of isolating neural activity
associated with individual memories. Using functional magnetic resonance imaging (fMRI), we tested for the existence of the hypothesized
adaptive forgetting process by developing a template-based patterntracking approach that quantifies the neural activation state of single
memory traces. Thus, we tracked the fate of behaviorally invisible
traces, providing a window into the suppression process thought to
underlie adaptive forgetting in the human brain.
Our effort to observe the dynamics of adaptive forgetting builds on
work examining the neural processes associated with retrieval competition. One approach used multi-voxel pattern analysis to measure
visual cortical activity when a retrieval cue concurrently elicits multiple visual memories. These studies revealed that pattern classifiers
have difficulty discriminating whether a retrieval cue is eliciting a
memory of a face or an object when both types of content are associated with it, even when only one type of content is to be retrieved7,8.
It cannot be discerned, however, whether this finding reflects the
coactivation of individual memories or of the broad categories to
which the memories belong (for example, faces, objects). A second
approach has focused on control mechanisms that resolve retrieval
competition by selecting between competing memories. Competition
during episodic retrieval engages prefrontal cortical areas associated with selection during semantic retrieval9. Specifically, during
selective recall of a target memory, ventrolateral prefrontal cortex
activity predicts later forgetting of competing memories5,6,10,11,
consistent with the possibility that this area contributes to resolving
competition. Together, these two lines of work suggest that lateral
prefrontal cortex contributes to adaptive forgetting by exerting a topdown modulatory influence on competing memories in posterior
representational areas.
We sought to isolate neural indices of individual memory traces
so that we might observe retrieval competition and its resolution as
it unfolds in the brain, and to link these dynamics to adaptive forgetting. To achieve this, we trained participants to associate two images
(for example, Marilyn Monroe and a hat) to each of a set of cue words
and then recorded brain activity during a selective retrieval phase in
which one of those visual memories (for example, Marilyn Monroe)
was repeatedly retrieved (Fig. 1a,b). On each retrieval trial, participants covertly retrieved the first picture they had associated with
the cue (henceforth, the target) in as much detail as possible. Across
the selective retrieval session, participants retrieved each target four
times. Notably, one quarter of the cue words were set aside and did
not appear in the selective retrieval task. As such, the associations for
these cues served as a baseline for assessing the behavioral and neural
changes induced by repeated target retrieval.
Our main concern was how retrieving the target affected the
competing memory associated with the same cue (henceforth, the
competitor). We assumed that the reminder initially would coactivate
1School
of Psychology, University of Birmingham, Birmingham, UK. 2MRC Cognition and Brain Sciences Unit, Cambridge, UK. 3Behavioural and Clinical
Neurosciences Institute, Cambridge, UK. Correspondence should be addressed to M.W. ([email protected]).
Received 29 December 2014; accepted 6 February 2015; published online 16 March 2015; doi:10.1038/nn.3973
NATURE NEUROSCIENCE
ADVANCE ONLINE PUBLICATION
1
the target and the competitor, and that resolving this competition
in favor of the target would engage inhibitory control to degrade
the competitor’s neural representation in visual and memory
processing regions. We further hypothesized that this degradation
would hinder later retrieval of the affected representation, so that
on a final visual recognition test, participants should be worse at
discriminating inhibited pictures from similar lures, compared to
their discrimination accuracy for baseline pictures (Fig. 1a).
Our primary goal was to track the suppression of individual
memories in visual and memory processing regions. Tracking
competitor suppression required a way to discern evidence during
selective retrieval that the neural pattern associated with a target or
its competitor was reactivated. To achieve this, we had participants
perform a perceptual localizer task (not shown in Fig. 1a) in which
they viewed a subset (50%) of the target, competitor and baseline
pictures multiple times. For each picture, we derived a canonical multivariate activity pattern representing the perceptual trace
that it typically evoked. We assumed that this canonical signature
pattern might resemble the visual memory formed during encoding and provide a template for assessing objectively how much the
visual memory was reactivated during each retrieval trial. Indeed,
previous findings12–14 indicate that episodic retrieval reinstates
perceptual traces established during encoding in late visual processing
areas. Memory-unique representations also have been observed in the
hippocampus during retrieval15. Together, these findings suggest that
it may be possible to isolate individual memory patterns in visual and
memory processing areas during retrieval, and use them to track the
dynamics of selective retrieval.
We therefore hypothesized that across repeated recall trials, as
retrieval became more successful and complete, the reactivated
pattern in visual and memory processing regions would become
increasingly similar to the canonical template of the target being
retrieved. Memory-unique target reactivation during each retrieval
trial would be present when the pattern measured on that trial resembled the target template (for example, Marilyn Monroe) more than it
resembled baseline templates from the same category (for example,
Albert Einstein). Notably, if inhibitory control degrades competing memories, the neural pattern during target recall should grow
progressively less similar to the canonical template of that target’s
competitor. Memory-unique competitor suppression during each
retrieval trial would be present if similarity of the measured pattern to the specific competitor (for example, hat) template is driven
below its similarity with baseline templates from the same category
(for example, goggles).
RESULTS
Performance during initial training
Training of the first and second associates to each cue occurred in
learning-test cycles outside the scanner (Online Methods). During
training, first associates were recalled at 77.1% (s.e.m. = 2.9%) in the
first retrieval cycle and at 86.4% (s.e.m. = 2.6%) in the second. The
second associates were recalled at 70.7% (s.e.m. = 3.0%) in their first
and only retrieval cycle.
Performance during selective retrieval
Selective retrieval was performed in the scanner. Because on each
trial, participants classified which category of memory they retrieved,
we could determine whether they had recalled the correct target
category. Participants selected the correct category for the target on
74.7% (s.e.m. = 2.9%) of the trials (Fig. 1c). When they made errors,
a
c
d
2
Percentage
recognized
Percentage Percentage
correct
intrusions
Percentage
recognized
Percentage
responses
b
s
fMRI
scanning
Training:
Training:
Selective retrieval of
Visual recognition of
Figure 1 Schematic of the procedure (excluding
first associate
second associate
first associate
all associates
initial familiarization and the pattern localizer) and
behavioral results. (a) Participants were trained on
novel word-picture pairs, each word being linked
sand
sand
sand
with two associates. During scanning, participants
were cued with a word (four times each across the
sand
+
+
entire selective retrieval task) and were asked to
FOS?
retrieve the first associate that they studied (the
target), with the second associate (the competitor)
antique
antique
assumed to interfere. On each trial they classified
the memory that came to mind as being a face (F),
(Baseline memories
object (O), scene (S) or unsuccessful retrieval (?).
not cued during this phase)
Some of the originally trained targets were not
tested during this phase and served as a baseline
Competitor associates
against which we assessed the effect of selective
100
Cued words
target recall. We expected to observe a disruptive
*
100
Sand
aftereffect of selective retrieval on competing
50
*
80
associates on a forced-choice visual recognition
0
60
task that required participants to distinguish
Target Competitor Unrelated Don’t
know
error
studied pictures from familiar foils. The colored
40
Target Competitor
Cued Baseline
frames illustrate item types and were not visible
word
word
15
Baseline words
10
to participants. (b) Illustration of the associative
Target
associates
Antique
5
relationships assumed to have been formed after
n.s.
0
training and of the different types of items created
100
90
by the experimental procedure. (c) Behavioral
80
80
data from the selective retrieval phase. Top, the
Target Competitor
70
60
proportion of trials on which participants correctly
60
40
1st
2nd
3rd
4th
selected the category of the target (for example,
Cued Baseline
Repetition
word
word
face), or incorrectly selected the category of the
competitor (for example, object), the third (unrelated) category not linked to the current cue word (for example, scene), or ‘don’t know’. Bottom, the number
of intrusion errors (competitor responses) and the corresponding increase in correct responses across repetitions. (All data in c represent mean o s.e.m.
across subjects.) *P < 0.05. (d) Behavioral results from the visual recognition memory task. Top, the disruption of discrimination performance for competitors
compared with their matched baseline items. Bottom, no difference was found in discrimination performance between targets and their matched baseline
items (P = 0.713). Boxes reflect median (o first and third quartile, error bars show minimum and maximum). *P < 0.05; n.s. = not significant.
4
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Similarity (R)
Similarity (R)
Neural pattern during
Sensory templates
Hypotheses
Figure 2 Rationale of the item-specific
selective retrieval
Target reactivation
canonical pattern analysis approach.
For each ROI, we extracted multivoxel
activity patterns elicited during a given
selective retrieval trial (left) and computed
sand
similarity with the canonical neural templates
obtained from the sensory pattern localizer
Competitor suppression
(middle). Item-specific similarity was assessed
by correlating the selective retrieval pattern in
a given ROI, trial-by-trial, with the item-unique
Memory
reactivation
template of the current target, the template of
(Pearson’s R)
the current competitor, and the templates of
1st
baseline items that were initially trained and
1st 2nd 3rd 4th
came from the same categories as the target
Repetition
2nd
Re
and competitor, respectively, but were never
pe
3rd
Target
titi
cued by a reminder word during the selective
on
Baseline (target)
4th
retrieval phase. The graphs show the
Competitor
hypothesized changes in pattern similarity
Baseline (competitor)
across the four repeated retrieval trials.
As sketched in these graphs, we expected the patterns during target retrieval to show increasing similarity with the target template (for example, Marilyn
Monroe) compared with baseline first associates from the same category (for example, Albert Einstein), and decreasing similarity with the competing
template (for example, hat) relative to baseline second associates from the same category (for example, goggles).
they selected the competing picture’s category significantly more
often (mean = 9.2%, s.e.m. = 1.1%) than the third, unrelated category
(mean = 2.3%, s.e.m. = 0.3%; t23 = 6.53, P < 0.001). These competitor
intrusion errors varied across the four repetitions (F3,69 = 21.8,
P < 0.001; Fig. 1c), showing a linear decline (F1,23 = 55.4, P < 0.001).
This pattern is consistent with the possibility that inhibitory control
rendered competitors less interfering over repetitions.
Selective retrieval induces forgetting of competitors
As a first step, we tested whether presenting an item’s cue
during retrieval had different effects on recognition performance
depending on whether an item was a first or second associate.
A 2 × 2 repeated-measures ANOVA with the factors item type
(cued versus baseline) and associate (first versus second) revealed a
significant interaction (F1,23 = 4.70, P = 0.041). Post hoc t tests confirmed that selective retrieval reduced later recognition of competitors
(mean = 75.2%, s.e.m. = 17.6%) compared with recognition of corresponding baseline items from the second training set (mean = 82.1%,
s.e.m. = 17.1%; t23 = 4.91, P < 0.001; Fig. 1d). Thus, remembering
the targets induced forgetting of competing memories (irrespective
of their category; Supplementary Fig. 1), consistent with past work1,4.
Notably, below-baseline forgetting correlated, across individuals, with
the number of intrusions observed during selective retrieval (R = 0.39,
P = 0.030), consistent with the idea that retrieval-induced forgetting
arises from a control process that reduces interference.
In contrast, recognition of targets (mean = 78.6%, s.e.m. = 16.7%)
did not differ reliably from recognition of corresponding firststudied baseline items (mean = 79.7%, s.e.m. = 23.9%, t23 = 0.57,
P = 0.713), providing little evidence for retrieval-based enhancement. Recognition of the two types of baseline items (first and second
associates) did not differ reliably (t23 = 0.93, P = 0.362). Overall,
results from the visual recognition test confirmed that selectively
recalling target memories disrupts later memory for competitors,
supporting the possibility that inhibitory control disrupted competitors’ visual-episodic representations.
Measuring the reactivation of unique memories
Using a new canonical pattern tracking approach, we quantified
changes in activation of each unique target and competitor across
repeated retrievals (Fig. 2). We hypothesized that ventral visual cortex
and the hippocampus would carry item-specific information about
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retrieved content12–15 and that ventral visual regions would also show
strong categorical reactivation7,8,12. At the end of scanning, we presented half of the trained pictures six times each in a one-back task
(Online Methods). From this, we constructed canonical multivariate
templates based on the average voxel-wise activity pattern elicited by
each picture (for example, Marilyn Monroe). These templates gave us
a neural standard against which to assess how much a visual memory
was reactivated during selective retrieval.
To quantify item-specific reactivation, we correlated (using Pearson
coefficients) the observed neural pattern elicited on each retrieval
(for example, cuing participants with the word ‘sand’ in the
examples in Figs. 1 and 2) with the current target template (for example,
Marilyn Monroe), and with the current competitor template
(for example, the hat). Notably, we also computed templates for
baseline pictures (for example, Albert Einstein and goggles). These
baseline templates allowed us to quantify how much the specific neural
patterns representing the target (for example, Marilyn Monroe) and
the competitor (for example, hat) were reinstated during a retrieval
trial, above and beyond categorically matched baseline items.
All selective retrieval trials for which item-specific templates were
available were analyzed (Supplementary Fig. 2 reports the same
results excluding incorrect retrievals).
Emergence of item-unique target patterns
Both ventral visual cortex and the hippocampus showed evidence for
target-unique memory reinstatement (Fig. 3). Specifically, similarity
of the observed pattern with the target template, relative to samecategory baseline templates, showed a significant (positive) linear
trend across repetitions in both regions of interest (ROIs; ventral
visual cortex: F1,23 = 12.97, P = 0.002; hippocampus: F1,23 = 11.91,
P = 0.002; Fig. 3), as tested in a repeated-measures ANOVA with the
factors item type (target versus baseline) and repetition (one to four).
There was a significant item type × repetition interaction in ventral
visual cortex (F3,69 = 4.15, P = 0.009) and hippocampus (F3,69 = 4.72,
P = 0.007). Post hoc tests showed that target reactivation exceeded
baseline in the hippocampus on the final (fourth) recall attempt
(t23 = 2.50, P = 0.010), whereas ventral visual cortex showed significant target reactivation on the third (t23 = 2.01, P = 0.028), but
not on the fourth, repetition (t23 = 1.44, P = 0.082; Fig. 3). Neural
patterns during retrieval therefore suggest that the unique memory was
reinstated increasingly over repetitions, one of the few demonstrations
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b
0.10
0.09
0.08
0.07
0.06
0.05
Similarity with the sensory templates
0.03
Similarity (R)
Similarity (R)
a
0.02
0.01
0
–0.01
0.02
0.01
0
–0.01
*
–0.02
Difference R
Suppression of unique competing memories
Difference R
Figure 3 Item-specific target reactivation and competitor suppression.
(a,b) The multivoxel pattern during selective retrieval was extracted and
compared with the sensory template patterns in ventral visual cortex (a)
and hippocampus (b). The first row shows an overlay of the respective
anatomical ROIs on a standard MNI brain. The second row shows the
raw average correlation (similarity) between selective retrieval activity
and the canonical template of the current target (black), the templates
of non-cued baseline items from the target category (gray), the current
competitor (red) and the templates of non-cued baseline items from the
competitor category (pink). Along the x axis, changes in similarity across
the four repetitions of retrieving the same target memory are shown. The
third row shows mean competitor-related similarity, subtracting similarity
with the respective baseline templates (solid red; mean o s.e.m. across
single subject estimates), along with the average of the best linear fit
(ML estimates) across participants (dashed red). The bottom row shows
the same baseline-corrected measures for target-related similarity.
Evidence for item-specific memory reactivation or suppression is indicated
by a significant (*P < 0.05) deviation from zero difference.
0.04
0.02
0
–0.02
–0.04
Competitor suppression predicts adaptive forgetting
If inhibition disrupts competing traces during retrieval, our
index of cortical competitor suppression should predict adaptive
forgetting. Confirming our hypothesis, the extent to which participants
downregulated the competing neural patterns in ventral visual
4
0.02
0.01
*
0
–0.01
–0.02
1st 2nd 3rd 4th
Repetition
Target
Baseline (target)
Difference R
© 2015 Nature America, Inc. All rights reserved.
Suppression of unique neural patterns representing
competing memories
Next, we correlated the observed pattern during each selective
retrieval trial to the competitor’s template. Notably, across the four
repetitions, memory-specific competitor activation showed a significant (negative) linear trend in ventral visual cortex (F1,23 = 10.52,
P = 0.004), but not in hippocampus (F1,23 = 1.07, P = 0.312; note that
the hippocampus showed a trend toward suppression when including
correct trials only; Supplementary Fig. 2). The item type × repetition
ANOVA revealed a significant interaction in ventral visual cortex
(F3,69 = 3.71, P = 0.016), but not the hippocampus (F3,69 = 0.52,
P = 0.670). Thus, unlike target reactivation, competitor activation in
ventral visual areas declined significantly across repeated retrievals.
We considered the possibility that this negative trend simply reflects
target reactivation becoming more successful and complete, such that
the cue would grow more likely over repetitions to selectively elicit
the target. If so, competitor reactivation would decline across trials,
but cease at a baseline level where the probability of the cue eliciting
the competitor would match its probability of eliciting baseline
memories. Conversely, if inhibition suppresses interfering memories
during retrieval, similarity between the selective retrieval pattern and
the competitor template should decrease significantly below the level
of non-cued baseline memories. Supporting the latter, the difference
between competitor and baseline similarity (Fig. 3) showed a trend
toward competitor reactivation during the first retrieval in ventral
visual cortex (t23 = 1.70, P = 0.050), but not in the hippocampus
(t23 = 0.13, P = 0.449), irrespective of whether we excluded incorrect trials (Supplementary Fig. 2). By the final (fourth) repetition, however, similarity with the competitor’s template was driven
below similarity with same-category baseline templates in both
regions (ventral visual cortex: t23 = 2.14, P = 0.022; hippocampus:
t23 = 1.97, P = 0.030). These findings indicate that reminders
initially tend to activate competitors, but competitors are progressively suppressed below baseline, consistent with the hypothesized
inhibition process.
Difference R
Reactivation of unique target memories
that a memory-specific cortical trace can be elicited by an associatively linked cue (see refs. 14,16 for related findings).
0.04
*
0.02
0
–0.02
–0.04
1st 2nd 3rd 4th
Repetition
Competitor
Baseline (competitor)
cortex across repetitions predicted below-baseline forgetting of
competing memories on our recognition test (R = −0.35, P = 0.047;
Fig. 4). No significant correlation was observed in the hippocampus
(R = 0.17, P = 0.217).
We also tested whether pattern suppression predicted which individual memories would be forgotten. To do this, we derived, for
each participant, a measure of pattern suppression for every individual competitor by fitting a linear regression to the decrease in its
similarity to its template across the four retrieval trials, relative to
baseline similarity (Fig. 3). These fits yielded maximum-likelihood
(ML) estimates of the slope of the best fitting regression line for each
competitor that quantifies its pattern suppression. Consistent with the
linear trend analysis, below zero estimates were found in ventral visual
cortex (t23 = 3.33, P = 0.001), but not the hippocampus (t23 = 1.03,
P = 0.157). We then tested whether these memory-specific estimates
predicted whether items were forgotten, using logistic regression.
In ventral visual cortex, items showing more pattern suppression were indeed more likely to be forgotten (B = 5.38, P = 0.037).
Together, these findings support the hypothesis that cortical pattern
suppression underlies adaptive forgetting.
The role of prefrontal cortex in cortical pattern suppression
The prefrontal cortex (PFC) is a key candidate region for the source
of the top-down control signal that induces pattern suppression5,10,11.
To test this possibility, we defined prefrontal ROIs on the basis of a functional comparison between early and late selective retrieval trials5. The
rationale behind this contrast is that demands on the control mechanism
should decrease across repetitions as interference is reduced. Replicating
past work on retrieval-induced forgetting5,10,11, this contrast revealed
clusters in left and right mid-ventrolateral prefrontal cortex and the
inferior frontal junction (including middle and inferior frontal gyri;
left BA6/8: xyz = −48, 5, 43, k = 635 voxels, tpeak = 5.73; right Brodmann
area 9: xyz = 48, 11, 31, k = 332 voxels, tpeak = 5.42; Fig. 5a).
To test for a role of prefrontal cortex in pattern suppression, we first
correlated participants’ prefrontal activity during selective retrieval with
their slope of competitor suppression (average ML estimate). Notably,
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average beta estimates in both prefrontal ROIs strongly predicted the
slope of competitor suppression in visual cortex (left PFC: R = −0.65,
P < 0.001; right PFC: R = −0.48, P = 0.009; Fig. 5b). No relationship
was found between prefrontal activity and the slope of target upregulation (left PFC: R = 0.25, P = 0.124; right PFC: R = −0.10, P = 0.324).
The correlation of prefrontal activity with competitor suppression
was more negative than its correlation with target enhancement in
left PFC (Hotelling’s t21 = 4.58, P < 0.001), and marginally so in right
PFC (Hotelling’s t21 = 1.52, P = 0.072). We also tested whether the
prefrontal activity during the selective retrieval of individual memories predicted pattern suppression (ML estimate) for that memory’s
competitor, within participants. Higher prefrontal cortex activity was
indeed related to greater pattern suppression (left PFC: R = −0.123,
P = 0.008; right PFC: R = −0.104, P = 0.021).
To further illustrate the link between prefrontal activation and pattern suppression, we median split our sample on the basis of prefrontal
recruitment (Fig. 5d). Participants with high right PFC engagement
showed steeper suppression slopes (t22 = 1.77, P = 0.045) and more
competitor suppression on the fourth retrieval (t22 = 2.31, P = 0.015).
This split revealed no difference in the slope of target enhancement
(t22 = 0.29, P = 0.387) and target reactivation on the fourth retrieval
(t22 = 1.41, P = 0.086). Similar patterns were observed when splitting
the sample by left PFC. These analyses support a specific functional
relationship between PFC recruitment and competitor suppression
in visual cortex.
a
z = 30
t=0
Left VLPFC
(beta)
t = 7.0
R = –0.65 *
20
10
0
–10
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0
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VVC suppression slope
c
R = –0.48 *
20
10
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0
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VVC suppression slope
Competitor suppression
Target reactivation
Split by left
PFC
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High PFC
Low PFC
1st
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Split by right
PFC
0.03
VVC slope
d
z = 26
Right VLPFC
(beta)
b
VVC slope
© 2015 Nature America, Inc. All rights reserved.
0
–0.03
1st
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a
b
Correlation with forgetting
0.06
R = –0.35*
0.02
–0.02
–0.06
–10 0 10 20 30
Percentage forgetting
Difference R
0.06
Difference R
Figure 4 Correlation between item-specific competitor suppression and
forgetting. (a,b) Across-participant correlations between cortical and
behavioral suppression of competing memories are shown separately for
ventral visual cortex (a) and hippocampus (b). The x axis in each graph
shows our behavioral forgetting index on the delayed visual recognition
memory test (forgetting of competitors relative to baseline items, with
positive scores indicating more forgetting), and the y axis shows the
overall cortical suppression of competitors during the selective recall
task, calculated as the difference between reactivation of competitors and
baseline items, averaged across all four repetitions. *P < 0.05.
R = 0.17
0.02
–0.02
–0.06
–10 0 10 20 30
Percentage forgetting
Finally, a whole brain analysis identified several clusters that
predicted pattern suppression (Fig. 5c), mostly in left and right
prefrontal cortices (Supplementary Table 1). Only one small cluster in the left middle frontal gyrus predicted target enhancement
(Fig. 5c). Together, our results support the possibility that the
mid-ventrolateral prefrontal cortex (VLPFC) is a source of topdown inhibitory modulation that suppresses the cortical patterns of
competing memories.
Voxels diagnostic of competitor activation are suppressed
The evidence for cortical pattern suppression described thus far could
arise because of at least two factors: competitor patterns become
noisier or inhibition truly suppresses diagnostic features of the competitor (that is, the ‘hat’ voxels). We hypothesized that the latter would
be the case17 and sought to isolate voxels diagnostic of a given target
or competitor. We first used item-specific linear pattern classifiers to
isolate voxels that most reliably distinguished individual targets or
competitors from their respective control items during the sensory
pattern localizer. In a second step, we computed changes in average signal strength of the 10% of voxels in our ventral visual cortex
mask that were most diagnostic for each target and competitor,
as determined by linear weights of the trained classifiers (Online
Methods and Supplementary Fig. 3).
Having identified diagnostic voxels for each target and competitor,
we extracted average activation (t values) and tested whether activity in
those voxels was enhanced for targets and suppressed for competitors
(Fig. 6). Unexpectedly, target voxel activity showed no positive linear
trend across repetitions (F1,23 = 0.47, P = 0.500) and no significant
above-baseline activation on the final repetition (t23 = 0.80, P = 0.216).
However, consistent with our inhibition hypothesis, voxels diagnostic
of the competitor showed a significant linear decrease across repetitions (F1,23 = 5.48, P = 0.028) and significant below-baseline suppression (t23 = 2.10, P = 0.023). A significantly negative competitor
slope (P = 0.028) was obtained only in the 10% most diagnostic voxels
Figure 5 Relationship between prefrontal activity and cortical suppression
of competing memories. (a) Left and right VLPFC showed stronger
univariate activity (P < 0.001) during early (first half) than during late
(second half) selective recall repetitions. (b) The univariate decrease
across repetitions in both regions predicted the slope of cortical pattern
suppression (ML estimates) in ventral visual cortex (VVC), with larger
prefrontal decreases associated with more negative-going slopes of
competitor suppression. *P < 0.05. (c) Whole-brain regression showing
areas that, across participants, significantly correlate with the slope of
competitor suppression (red) and the slope of target reactivation (black) in
ventral visual cortex. Both contrasts are shown at P < 0.001 (uncorrected).
(d) Cortical pattern suppression as a function of PFC engagement,
splitting the sample into participants with high and low PFC engagement.
Participants with high PFC engagement showed a significant (P < 0.05)
difference in the slope of competitor suppression, and in the level of
competitor suppression on the fourth (final) retrieval trial. Error bars in b
and d represent s.e.m. across participants for each single measure.
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Categorical target reactivation without competitor suppression
To underscore the advantages of our item-unique analyses, we conducted two categorical analyses that assessed whether patterns during
selective retrieval showed reactivation of the target or competitor
categories. For the similarity analysis (Fig. 7), we calculated a template
for each category (for example, a face template) on the basis of baseline pictures from the localizer. Categorical similarity was assessed by
computing the correlation between the pattern observed during each
retrieval trial and the template of that trial’s target category, its competing category and its non-involved (categorical baseline) category.
Ventral visual cortex, but not hippocampus, showed strong evidence for categorical target activation (main effect target versus baseline in ventral visual cortex: F1,23 = 29.79, P < 0.001; hippocampus:
F1,23 = 0.96, P = 0.338) that did not reliably change with repetition
(interaction with repetition in ventral visual cortex: F3,69 = 1.60,
P = 0.196; hippocampus: F3,69 = 0.43, P = 0.732; Fig. 7). We observed
similar results with a categorical analysis on the basis of linear
machine learning algorithms (Fig. 7 and Online Methods): classification of the target category across recall trials was above chance
in ventral visual cortex (t23 = 4.88, P < 0.001) and the hippocampus
(t23 = 2.38, P = 0.013), and showed stable categorical reactivation
a
b
0.15
Similarity (R)
Similarity (R)
Similarity with the categorical templates
0.10
0.05
0
0.02
0.01
*
0
*
*
*
*
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Difference R
0.02
0.01
0
0.01
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0
–0.01
*
*
*
*
55.0
45.0
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Repetition
Target category
Competitor category
6
Classifier accuracy
Categorical reactivation (classifier based)
65.0
Average t value
“Hat” voxels
0.07
*
*
0
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1st 2nd 3rd 4th
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across repetitions, with no linear trend (ventral visual cortex:
F1,23 = 0.11, P = 0.750; hippocampus: F1,23 = 0.65, P = 0.428). This
high above-chance categorical similarity/classification mirrors
classification responses collected during the selective retrieval
phase, which were accurate from the first repetition (Supplementary
Fig. 4). Notably, participants’ classification responses during selective
retrieval, similar to the classifier output itself, are only diagnostic as to
the accuracy of the category retrieved, not the specific item.
Despite strong target activation, categorical patterns did not detect
competitor suppression. Activation of competitor categories did not
significantly differ from baseline in either ROI (main effect of competitor versus baseline in ventral visual cortex: F1,23 = 0.63, P = 0.437;
hippocampus: F1,23 = 3.80, P = 0.064), and showed no interaction with
repetition (ventral visual cortex: F3,69 = 1.43, P = 0.240; hippocampus:
F3,69 = 2.50, P = 0.067). The linear classifier analysis confirmed this
pattern, showing a trend toward above-chance classification of the competitor category when averaged across repetitions in ventral visual cortex
(t23 = 2.00, ptwo-tailed = 0.057), but not in the hippocampus (t23 = 0.59,
ptwo-tailed = 0.561). Classification performance showed no linear decrease
across repetitions (ventral visual cortex: F1,23 = 0.21, P = 0.651; hippocampus: F1,23 = 1.00, P = 0.328). Finally, no relationships were found
between activation of competitor categories and forgetting (correlation
between forgetting and average activation of the competitor category
across subjects: R = 0.01, P = 0.520; same correlation within subjects:
B = 0.47, P = 0.312; correlation with slope of categorical competitor
activation across subjects: R = 0.16, P = 0.774; same correlation within
subjects: B = 1.5, P = 0.411). These results suggest that the inhibitory
mechanism underlying adaptive forgetting suppresses features of individual competing memories, rather than global categorical patterns.
0.01
Categorical reactivation (similarity based)
Classifier accuracy
© 2015 Nature America, Inc. All rights reserved.
(Supplementary Fig. 3). These findings suggest that cortical pattern
suppression is at least partly driven by reduced activity in voxels
that contribute strongly to representing competing memories.
Average signal in 10%
most diagnostic voxels
“Marilyn” voxels
Average t value
Figure 6 Activation in diagnostic voxels for individual targets and
competitors across repetitions. Diagnostic voxels were determined from
item-specific linear classifiers that were trained to distinguish a given
target and a given competitor picture from all same-category baseline
items. Based on the weights of these classifiers, we investigated average
BOLD signal changes in the 10% most diagnostic voxels of each target
(black) and competitor (red). Diagnostic target voxels showed abovebaseline activation on the second and third repetitions (upper right).
Notably, on average, competitor voxels showed a significant linear
decrease in activation across the four recall repetitions, and a significant
below-baseline suppression effect at the final repetition (lower right).
*P < 0.05. Line plots show mean o s.e.m. across subjects.
65.0
*
55.0
45.0
1st 2nd 3rd 4th
Repetition
Non-involved category
Figure 7 Categorical activation of targets and competitors. Results
from the categorical multivariate analyses in ventral visual cortex (a)
and hippocampus (b). The upper line plots show raw similarity (Pearson
correlation) values between selective recall patterns and the canonical
template of the target category (black solid), the canonical template of
the competing category (red solid) and the canonical template of the
currently non-involved category (gray dashed), averaged across trials and
participants. The middle plots show the same measures transformed
into differences in categorical activation relative to the category that
was not involved on a given trial. The lower row shows the results from
a complementary categorical analysis using linear pattern classifiers
(SVMs), with plotted means reflecting classifier accuracy in determining
the target and competitor category (against the baseline, non-involved
category). Both approaches converge in indicating highly significant
categorical target reactivation in ventral visual cortex (but not the
hippocampus), with no reliable change over repetitions. No significant
below baseline suppression of the competitor’s category was evident.
All measures plotted as mean o s.e.m. (across subjects). *P < 0.05.
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DISCUSSION
Remembering does not merely reawaken memories of the past, it has
a darker side that induces forgetting of other experiences that interfere
with retrieval, dynamically altering which aspects of our past remain
accessible. Remembering, quite simply, causes forgetting. It has been
hypothesized that this adaptive forgetting process is caused by an inhibitory control mechanism that suppresses distraction from competing
memories1,3–5. Five key findings indicate that we have, to the best of our
knowledge for the first time, isolated the hypothesized adaptive forgetting mechanism and shown it to be implemented by the suppression of
distributed neocortical patterns that represent competing memories.
First, selective retrieval caused forgetting of competing memories.
When we repeatedly cued participants to retrieve target items, competing
memories were recognized less well later on than baseline items (Fig. 1d).
This effect occurred for images of faces, objects or scenes, indicating
a domain-general process. Forgetting was observed on a forcedchoice recognition test that displayed the putatively inhibited visual
item, reducing memory search demands. Observing below-baseline
forgetting even though our test provided potent, vivid, item-unique
cues indicates that retrieval disrupts the sensory features of competing
memories17,18—a possibility that is compatible with an adaptive forgetting process that suppresses visual cortical patterns underlying those
memories. Notably, forgetting was predicted by the tendency of competitors to interfere, as reflected by how often participants mistakenly
selected the competitor’s category during selective retrieval trials. This
tendency of competitors to intrude reduced gradually over retrieval
trials (Fig. 1c), consistent with an active suppression process. Taken
together, these findings exhibit the hallmarks indicating a role of inhibitory control in retrieval-induced forgetting, supporting the possibility
that we succeeded in eliciting the putative adaptive forgetting process.
Second, during the four selective retrievals, cortical pattern indices
revealed that competing memories were measurably reactivated and
then progressively suppressed (Fig. 3). Our reactivation index measures how much the activation pattern elicited by the cue resembled the
perceptual template for the associated target or competitor memories
and provides an objective neural standard for quantifying the retrieval
of individual memories. Gradual suppression of competing patterns is
expected on the basis of the hypothesized inhibitory control mechanism thought to underlie adaptive forgetting.
It was essential to consider whether the decline in competitor
activation over target retrievals might reflect processes other than
cortical pattern suppression. For example, participants may grow
efficient at reinstating the target over repeated retrievals, reducing
the chances of reactivating competitors. Alternatively, an associative
unlearning mechanism, in which target retrievals punish competing
associations, may make the cue less likely to reactivate competitors 1.
Both alternatives predict, however, that the competitor’s activation
should simply approach the level observed for baseline memories, and
never decline below baseline because, even if cue-competitor associations were unlearned entirely (or, alternatively, if the cue became
perfectly efficient at eliciting the target), the cue should merely fail
to reactivate the competitor; it should be as if the competitor is unassociated with the cue, similar to baseline items. Inhibition, however,
predicts that competitors are actively inhibited and that their cortical
traces will be suppressed below the activity observed for baseline
items. This prediction was confirmed. This third key finding—below
baseline pattern suppression—provides encouraging and distinctive
support for the hypothesized inhibition mechanism.
Even if inhibition caused pattern suppression, this finding does not
establish the relevance of these reductions to adaptive forgetting. Our
fourth and fifth findings support an active forgetting interpretation
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and establish important characteristics of cortical pattern suppression
First, if inhibitory control reduced mnemonic activation by acting on
cortical sites representing competitors, this putative footprint of inhibition should be predicted by activation in prefrontal regions implicated
in inhibitory control. Such a finding would distinguish an adaptive
mechanism that acts during goal-directed retrieval from other, incidental mechanisms that may weaken memories. For example, reactivating memories briefly during tasks unrelated to retrieval16,19,20 may
strengthen or weaken the reawakened memories depending on how
active they become. This forgetting is predicted by a computational
model of inhibition21 and is proposed to not require control by the
prefrontal cortex. In contrast, we found that the engagement of midventrolateral prefrontal regions previously linked to adaptive forgetting5,10,11 predicted pattern suppression in ventral visual cortex both
across and within participants, with more robust VLPFC engagement
predicting greater pattern suppression (Fig. 5b–d). This fourth finding
supports a contribution of mid-VLPFC to a top-down control signal
that suppresses competition in visual cortex.
Fifth, if reduced competitor activation in ventral visual cortex
is relevant to adaptive forgetting, it should predict forgetting. This
relationship was observed: participants showing the strongest
average reduction in competitor activation showed the most forgetting
(Fig. 4), and, even within participants, those individual memories
showing the steepest suppression slope were most likely to be
forgotten. These relationships support the possibility that cortical
pattern suppression is instrumental in adaptive forgetting.
Taken together, these five findings provide strong and specific
support for the hypothesized cortical pattern suppression process and
for its role in producing adaptive forgetting in the human brain. Our
findings suggest further properties of pattern suppression that may
prove important if corroborated. For instance, our canonical pattern
tracking approach allowed us to investigate how inhibition modulates cortical traces. Does inhibition target the unique cortical pattern
causing interference (the hat pattern), or the global representation of
the competing category (an object pattern)? Several findings favor
an item-specific suppression mechanism. First, pattern suppression
for individual items was driven, in part, by downregulated activity in
voxels distinguishing a competitor from other members of its category
and from the target (Fig. 6). These findings are expected based on
models of memory inhibition17, according to which inhibition targets
features representing a competitor that do not overlap with those representing the target. Second, despite robust categorical reactivation of
targets, the competitor’s category showed no evidence of suppression.
Consistent with previous studies7,8, categorical patterns even showed
a trend in the opposite direction, with early retrievals showing coactivation of the competitor’s and the target’s categories. Thus, although
categorical activations can reveal competition, our results indicate that
the brain’s adaptive response to resolving competition, inhibition, suppresses a competitor’s diagnostic features, distinguishing it from other
exemplars of its category and from the memory being retrieved.
A second interesting observation is that hippocampal patterns
exhibited weaker evidence for pattern suppression, despite robust
target reactivation. Weaker competitor suppression may be relevant
to computational models of hippocampal-neocortical processing,
assuming that the hippocampus, in contrast with neocortex, uses
sparse coding and efficiently separates overlapping patterns22,23.
If the neocortical components of a distributed memory are more disrupted by competition23,24, it may be functional for inhibitory control
to target neocortical areas to suppress interference. These speculations
about the selectivity of pattern suppression to neocortex must remain
tentative, awaiting further confirmation.
7
© 2015 Nature America, Inc. All rights reserved.
ARTICLES
The proposed top-down mechanism that supports selective retrieval
by suppressing competing memories parallels mechanisms believed
to support visual selective attention and visual working memory25–30.
Selective attention enhances targets and suppresses distracting information, a pattern demonstrated from single neurons up to electroencephalographic and blood oxygen level–dependent (BOLD) activity31–34, and
such adaptive modulations of sensory regions are believed to be driven
by lateral prefrontal cortex34,35. Recent studies have suggested a causal
role of the inferior frontal junction in exerting this top-down influence34.
This frontal area overlaps with regions implicated in resolving
mnemonic competition in previous work5,10,11 and by our results. By
showing a relationship between prefrontal activity and competitor
suppression, our findings reinforce theoretical parallels between the
mechanisms the brain uses to resolve mnemonic competition on the
one hand, and sensory competition on the other hand28, building a
theoretical bridge spanning attention and long-term memory.
Studying the neural basis of forgetting has proven challenging
because the substrate of episodic memories (the engram) has been
difficult to pinpoint in brain activity. By capitalizing on the relation
between perception and memory, we detected neural activity sensitive to the activation of individual memories. This canonical pattern
tracking approach provided a unique window into the invisible neurocognitive processes triggered when a reminder recapitulates several
competing memories in neocortex. Notably, we were able to track
dynamic changes in the activity of individual memories during selective
retrieval, as competition was resolved. In doing so, we established
clear evidence for cortical pattern suppression as a key mechanism
of adaptive forgetting in the human brain. More broadly, this work
converges with a growing literature showing that forgetting often
serves an adaptive function2,36; it establishes how, by simply using
our memory system via selective retrieval, we adapt the landscape of
memory to the demands of mental life.
METHODS
Methods and any associated references are available in the online
version of the paper.
Note: Any Supplementary Information and Source Data files are available in the
online version of the paper.
ACKNOWLEDGMENTS
We thank B. Staresina and S. Hanslmayr for commenting on previous versions
of the manuscript. This work was supported by a fellowship from the German
Research Foundation (WI-3784/1-1) awarded to M.W. and by UK Medical
Research Council grant MC-A060-5PR00 awarded to M.C.A.
AUTHOR CONTRIBUTIONS
M.W. and M.C.A. designed the experiment, with important contributions
by I.C. and N.K. M.W. conducted the experiment. M.W., A.A. and I.C. analyzed the
data. All authors contributed to the analysis approach and to data interpretation.
M.W. and M.C.A. wrote the manuscript.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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ONLINE METHODS
© 2015 Nature America, Inc. All rights reserved.
Participants. 24 healthy participants (20 female) aged 20–32 years (mean 24.2
years) were recruited from the MRC Cognition and Brain Sciences Unit volunteer
panel. They all had normal or corrected-to-normal vision and reported no history
of neurological or psychiatric disease. The experiment was approved by, and
conducted in accordance with requirements of, the Cambridge Psychology Research
Ethics Committee (CPREC), including the requirement of written informed
consent from each participant before the beginning of the experiment.
Materials. The word material used as verbal cues consisted of 72 English words
drawn from the MRC linguistic database (http://www.psych.rl.ac.uk/). Words
were selected on the basis of having relatively low imageabilty (mean = 571.3,
s.d. = 37.3) and concreteness (mean = 545.1, s.d. = 54.6) ratings such that they
would not elicit concrete mental images by themselves when presented to participants in the scanner. Pictures were 144 photographs of well-known faces,
well-known scenes, and everyday objects (48 pictures per category) from a range
of in-house databases as well as the internet (including http://cvcl.mit.edu/MM/
exemplarPairs.html)37. All images were converted to black-and-white and scaled
to cover the same visual angle. Note, however, that faces and objects were background stripped and thus contained extensive areas of white background, while
scenes always covered the full angle of the picture. In addition to the materials
used in the main experimental runs, three additional words and six additional
pictures were used for demonstration purposes during practice runs outside the
scanner. The 144 pictures were split into two sets of 72 pictures each (24 per
category). One set was trained together with a cue word as first associates, and the
other set was trained together with the same cue words as second associates. The
two associates linked to the same cue word always came from different categories
(for example, a face and an object; Fig. 1). 54 pictures out of the 72 first associates
(18 per category) later became the to-be-retrieved targets, and 54 pictures out of
the 72 associates later became competitors. The remaining 36 pictures (18 first
associates, 18 second associates) were linked to cue words that never appeared
during the scanned selective retrieval task and thus served as baselines for the
targets and competitors, respectively. Assignment of pictures to conditions was
counterbalanced such that across participants, each picture equally often served
as a target, competitor and baseline item.
Experimental procedure. Familiarization with the pictures, and the training on
word-picture associations was carried out in a separate testing room outside the
scanner. The first task was a familiarization phase, during which participants were
presented with all 144 pictures used in the experiment as well as their corresponding similar lures (used in the visual recognition test, see below), and thus saw a
set of 288 pictures in random order. Each picture appeared alone first; followed
by its verbal label (for example, ‘Charlie Chaplin’) after 1 s, the label remaining on
the screen for another 1.5 s. Participants indicated with a button press whether
they recognized (that is, were familiar with) the face, object or scene shown on
the photograph. In cases in which they indicated that they were unfamiliar with
an item, the same picture was presented to them for a second time at the end of
the familiarization phase.
After familiarization, participants were trained on the first set of 72 wordpicture associations. To facilitate learning, the training was separated
into three blocks, each consisting of an initial learning, a test, and a re-test
cycle for 24 out of the 72 word-picture pairs. At the beginning of each block,
participants were presented with the 24 word-picture pairs for 4.5 s each
(4 + 0.5-s inter-stimulus interval). The word was shown above the picture,
and it was emphasized to participants that they should make an effort to
memorize the picture in as much detail as possible in order to be able to
bring back a vivid mental image of the picture when cued with the word,
later in the scanner. To build strong links between the words and the pictures,
we instructed participants to use a mental imagery strategy, that is, to use
the word and picture in an interactive way (for example, use the cue word to
make the picture move, change color, etc.). This initial learning was followed
by two cycles of test-feedback practice. On each trial, participants first saw a
word (for example, sand) on a blank screen, and were asked to orally provide
the label (or a short description) of the picture they had learned to associate
with this word. Two similar versions of the correct picture associate (the same
versions also used in the later visual recognition test) appeared 3 s later, and
participants had to indicate which of the two pictures they had previously
doi:10.1038/nn.3973
linked with the word. This procedure was again aimed at emphasizing
the encoding of as many visual details as possible.
After finishing training on the first set of pictures (which would become the
targets during later selective retrieval), participants were instructed that they
would now be trained on a second set of associates for each word (which would
become the competitors during selective retrieval), and that later in the scanner
they might need either of the two associates. It was emphasized to participants
that they would be required to retrieve the two associates separately, and should
thus not inter-relate the two pictures associated with the same cue word (that is,
they should not form an integrated mental image). We did so because integration between competing memories has been shown to be a main factor limiting
retrieval-induced forgetting1. In terms of the procedure, training of the second set
of associates (which would later become the competitors) was performed exactly
as for the first set, with the exception that the test-feedback practice involved only
one instead of two cycles. After training of the second set, participants were given
a short practice on the tasks they would perform in the scanner.
During the recall task in the MRI scanner, participants were prompted with
a cue word for 4 s each, followed by a response prompt (“F – O – S – ?”) asking
them to indicate the category of the picture they were currently recalling (fingers 2–5 of the right hand corresponding to “face”, “object”, “scene” and “don’t
know”, respectively). The response prompt was presented for 1.5 s (inter-stimulus
interval = 1 s). Feedback was given as soon as participants pressed a button,
with the correct response option lightening up in green color. We instructed
participants to always press a button while the response prompt was still present
on the screen, because they would miss the feedback when responding too late.
However, responses given during the following inter-stimulus interval were still
included in the data analysis. The selective recall task was followed by a short
(~2 min) period of rest, followed by the final recognition test. In this task, each
trial presented participants with two similar pictures, both of which had been
presented before in the familiarization phase, but only one of which they had
initially been linked with a cue word. Notably, the cue words were not shown
during the final test. The two pictures were presented simultaneously, to the left
and right of the fixation cross, for 3.5 s (inter-stimulus interval = 1 s). Participants
used their right index and middle finger to select the picture they had linked with
a word during training.
The final task conducted in the scanner was a pattern localizer for individual
pictures, conducted to obtain the item-unique sensory templates. During the
localizer, the BOLD activity pattern in response to a subset of 72 of the initially
trained 144 pictures was sampled (only half of the items were sampled due to
time constraints). The subsample of pictures was chosen randomly for each
participant, with the constraint that it had to include 18 target pictures, the 18
corresponding alternative associates from the same word-picture triples, 18 baseline pictures that had been trained as first associates, but were not recalled during
the selective recall task, and the 18 corresponding alternative associates from
the same word-pictures triples. The latter two picture types were used to obtain
baseline templates to compare the targets and competitors, respectively, against.
Each of the sampled pictures was presented 6 times overall. Picture presentation
occurred in the context of a one-back task, where each picture was shown for
1.5 s (inter-stimulus interval = 1 s) and participants were instructed to respond
with their index finger as fast as possible whenever two consecutive items in the
picture sequence were the same.
The sensory templates were sampled at the end of the scanning phase for several reasons. First, the localizer overall lasted for ~25 min, and we did not want to
introduce a delay of this length between study of the word-picture pairs and the
selective retrieval task. Second, and more importantly, one might expect a priori
that the similarity between the recall patterns and the sensory templates would
become higher with increasing temporal proximity between the localizer and the
time at which the templates are sampled. Such an increase could occur simply
because any neural pattern sampled at a given time during scanning would show
a drift toward or away from the localizer patterns depending on how far in time
from the localizer it is sampled. Based on such pattern drifts, recall patterns should
overall become less similar to the sensory templates if the localizer is conducted
before the selective recall phase; and more similar to the templates if the localizer is conducted at the end of the experiment, after selective recall. Because our
main effect of interest in this study was an effect of decreasing similarity across
retrieval repetitions (for the competitors), it was a more conservative approach
to conduct the localizer at the end of the experiment, such as to not risk the effect
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© 2015 Nature America, Inc. All rights reserved.
to be confounded with spurious similarity decreases caused by pattern drifts.
Note that such spurious similarity changes might, according to this reasoning,
have affected the increasing similarity we found with the sensory templates for
target representations. Having said this, we believe that it is unlikely for all our
effects to be caused by spurious correlation through pattern drifts, because of the
use of very well controlled baseline measures. In particular, pattern drifts toward
the ‘template state’ should have affected the similarity with all templates, including
the sensory templates of control items.
However, one might still argue that differences inherent in the localizer templates may affect the overall correlation between the neural patterns during selective retrieval and the different types of templates. We took several measures to
minimize this concern, the results of which are shown in Supplementary Table 2
and Supplementary Figure 5. These analyses showed that the templates did not
significantly differ in signal-to-noise ratio (SNR; computed as mean t-value across
all voxels in the template divided by the s.d.); in informational content as measured by Shannon entropy; or in the degree to which they correlated with other
templates from the same condition (correlationability). Importantly, because the
aim of these analyses was to show no difference between conditions (i.e., between
target templates and their respective baseline templates, and between competitor
templates and their respective baseline templates), Supplementary Table 1 also
reports Bayes factors38 together with the P values, giving an indication of the
strength of evidence in favor of the null hypothesis.
For all tasks conducted in the scanner, event sequences were optimized for
rapid event-related designs using self-programmed MATLAB code, based on a
previously published genetic algorithm39. For the multivoxel pattern localizer,
the output of the algorithm was modified to obtain a reasonably high number of
picture repetitions (11–15% of the trials), as to keep participants engaged in the
one-back task. In each of the scanned tasks (selective retrieval, visual recognition, and the pattern localizer), events were interspersed with null-trials (fixation
periods covering the same period as actual events) corresponding to one-third
of the overall trial number.
fMRI data acquisition and pre-processing. Imaging data were acquired
on a 3-T Siemens Trio scanner using a 32-channel head coil. High-resolution
(1-mm3 isotropic voxels), T1-weighted anatomical scans were acquired at the
beginning of each session using a magnetization-prepared rapid acquisition
gradient echo (MP-RAGE) sequence resulting in 192 sagittal slices. Functional
volumes were obtained in three separate sessions corresponding to the recall
phase (772 volumes), the final picture discrimination test (274 volumes), and
the picture localizer (727 volumes). Functional volumes consisted of 32 axial
slices (3.75-mm slice thickness, 3- × 3-mm in-plane resolution) covering the
full brain, and were acquired using a descending T2*-weighted echo-planar
imaging (EPI) pulse sequence (repetition time = 2.0 s, echo time = 30 ms, flip
angle = 78°). The first five volumes of each session were discarded to allow
for stable tissue magnetization.
SPM8 (http://www.fil.ion.ucl.ac.uk/spm/) was used for pre-processing and univariate analyses. For all analyses, images were slice timed and realigned in space
to the first image of each session, and global effects within each session and voxel
were removed using linear detrending40. All multivariate analyses were conducted
in native (subject) space without normalizing or smoothing the EPI images.
Univariate data analysis. For univariate analyses, EPI images were additionally
normalized (using the segmentation algorithm as implemented in SPM8) and
smoothed with an 8-mm full-width-at half-maximum (FWHM) Gaussian kernel.
Events of interest were modeled as delta (stick) functions and convolved with a
first-order canonical hemodynamic response function (HRF). Button presses
were included in all single-subject models as events of no interest, and the movement parameters from spatial realignment were included as nuisance variables.
For univariate group statistics, single-subject activation maps of each condition
of interest were entered into a within-subject ANOVA using pooled errors. The
main comparison of interest between early and late retrieval trials (Fig. 5a) was
calculated within this ANOVA, and results are reported on an uncorrected p-level
of < 0.001 (minimum extent threshold k = 10 voxels). For the regression analysis reported in Figure 5c, an activation map contrasting early and late retrieval
trials was calculated in each single participants, and entered into a whole-brain,
group-level GLM using multivariate indices of target enhancement and competitor suppression (see below) as linear regressors.
NATURE NEUROSCIENCE
Similarity-based multivariate data analysis. A template-based variant of representational similarity analysis (RSA41,42) was used to assess the degree to which
the neural patterns that were active during recall were similar to the neural
pattern templates obtained from the pattern localizer. To this end, each trial and
repetition during selective retrieval was modeled as a single event (regressor) in a
general linear model by convolving a delta stick function at the onset of the event
with a canonical HRF. For obtaining the sensory templates, the six repetitions of
the same item as visually presented during the pattern localizer were modeled
as one event (regressor). For the item-specific linear pattern classification analysis (Fig. 6), we modeled the six repetitions of each item as separate regressors.
With respect to selective retrieval activity, each retrieval trial was modeled
as a single event (regressor). Overall, this procedure produced 54 (items) × 4
(repetitions) t maps from the selective retrieval task, and 72 t maps from the
pattern localizer. Only the 18 × 4 recall patterns for which item-specific localizer
templates were available were included in the item-specific analysis, whereas all
54 × 4 recall patterns were included in the categorical analysis.
Anatomical regions of interest (ROIs) were built based on the human atlas as
implemented in the WFU pickatlas software (http://fmri.wfubmc.edu/software/
PickAtlas), and back-projected into native space using the inverse normalization
parameters obtained from SPM during segmentation. The large ventral visual cortex ROI was comprised of bilateral inferior occipital lobe, parahippocampal gyrus,
fusiform gyrus, and lingual gyrus (all bilateral and based on AAL definitions).
The hippocampal ROI contained only the bilateral hippocampi, based on the
Talairach Demon’s brodmann areas (dilated by a factor of 2 as this yielded optimal
coverage of our individual subjects’ anatomies). The multivariate patterns used
in the correlation approach were obtained by extracting the raw beta values from
each ROI and in response to each event of interest, converting them to t-values
and finally vectorizing these t values43,44. All similarity-based analyses were based
on a correlation approach, using Pearson correlation as a metric of similarity
between the sensory canonical templates and selective retrieval activity.
For the item-specific RSA analysis, we computed the correlation between each
single selective retrieval trial and the corresponding target template (yielding an
index of target reactivation), and the correlation between the same trial and the
corresponding competitor template (yielding an index of competitor reactivation). To obtain an appropriate baseline for target and competitor reactivation
on each single trial, we computed the correlation between the selective retrieval
pattern and each single baseline template corresponding to the same category
as the target (used as a baseline for item-unique target reactivation), or the
same category as the competitor (used as a baseline for item-unique competitor
reactivation). For the target and competitor baseline measures, correlations were
first computed between the retrieval pattern and each single available baseline
template from the target’s and competitor’s category, respectively. We then used
the average correlation with the baseline templates (as opposed to the correlation
with the average baseline template, which is an important difference) as a measure
of baseline similarity. All further analyses performed on the raw similarity values,
including linear fits, are described in the main text.
For the categorical analysis, we first computed an average face template, an average object template, and an average scene template based on all available baseline
pictures from the pattern localizer task. To assess categorical target enhancement
and competitor suppression, we then correlated each selective retrieval trial with
the categorical template of the current target category (for example, a face), the
categorical template of the current competitor category (for example, an object),
and the average template of the category that was currently not involved as target
or competitor category (for example, a scene). All methods using linear pattern
classifiers are described below.
Repeated measures ANOVAs and t tests were used to test for differences in
multivariate pattern similarity. All t tests were used to test directional hypotheses, and unless indicated otherwise, one-tailed p-values at an alpha threshold
of 0.05 are thus consistently reported throughout the results section. Brain-brain
and brain-behavior relationships were tested both within- and across subjects.
For across-subjects relationships, Spearman correlation coefficients were used.
All within-subject, item-by-item correlations (including logistic regression) were
computed from fixed-effects models in order to increase power to detect a relationship. Empirical P values for the logistic regression analyses were derived
by randomly re-assigning the observed outcome on the final recognition test
(with a value of 0 or 1) across trials, and computing the regression for 10,000 of
these random models. Note that for the correlations between prefrontal cortex
doi:10.1038/nn.3973
© 2015 Nature America, Inc. All rights reserved.
and neural suppression slopes, the same results were obtained when using a
random-effects model; for the logistic regression relating neural suppression
slopes to behavioral outcome on the final test, there were not enough forgotten
trials on an individual subject basis to yield stable beta coefficient estimates.
For reasons of consistency, were therefore report fixed-effect analyses throughout. Before collapsing trials across subjects, outlier trials were identified within
each subject, and rejected according to an absolute deviation from the mean
(with a criterion of 2.0)45.
Classifier-based multivariate analyses. All pattern classification analysis used
linear support vector machines as implemented in the LIBSVM library (http://
www.csie.ntu.edu.tw/~cjlin/libsvm/). For the diagnostic voxels analysis reported
in the main text and in Figure 6, we trained separate binary classifiers, based on
the six repetitions of each item during the sensory pattern localizer, to distinguish an individual target and competitor item from each same-category baseline
item. For example, to derive the linear weights that optimally separate the “hat”
pattern in ventral visual cortex from the pattern elicited by other baseline objects,
six binary classifiers were trained to distinguish the hat from the goggles, the hat
from a chair etc. During this procedure, each voxel is assigned a linear weight (W),
the absolute value of which directly reflects the importance of a feature (voxel) in
discriminating the two classes. We defined the intersection of those voxels that
consistently yielded the 10% highest weights across the separate classifiers for
each competitor/target as the diagnostic voxels for a given target or competitor.
The same procedure was used to determine the diagnostic voxels for each baseline
item, except that here we trained five binary classifiers for each item, separating
this baseline item from all remaining, same-category baseline items.
Having derived these diagnostic voxels for each localizer item, we were
then able to compute the average activity (average t values) of the voxels most
diagnostic for the target and competitor item or a given recall trial during the
selective retrieval task. In order to ensure that the diagnostic target and competitor voxels did not overlap, we also removed the intersection of those two sets of
voxels for this analysis. This rationale was purely theory-driven, as competitor
voxels (features) that overlap with target voxels (features) should not be subject to
inhibition1,17. Finally, to parallel the similarity-based analyses using our template
tracking approach, we subtracted from those average activity estimates on each
retrieval trial the average activity of other voxels that are diagnostic for samecategory baseline items, but not the specific target and competitor items involved
in this trial. The results of this analysis are described in the main text and depicted
in Figure 6 and Supplementary Figure 3.
doi:10.1038/nn.3973
For our categorical classification analysis, we trained binary linear classifiers
purely on the patterns elicited in ventral visual cortex by the baseline items during
the sensory pattern localizer. Three separate classifiers were trained to optimally distinguish faces from scenes, faces from objects, and objects from scenes.
We then tested the accuracy of those classifiers to guess, on each selective retrieval
trial, the category of the target by using the binary classifier representing the target
versus non-involved, baseline category (for example, the face-scene classifier for
the examples shown in Figs. 1 and 2), and to guess the category of the competitor
by using the binary classifier representing the competitor versus non-involved,
baseline category (for example, the object versus scene classifier in the example
shown in Figs. 1 and 2). Note that this way of setting up the analysis automatically builds in the non-involved category, that is, the one category that should not
be elicited by a given cue word, as a baseline on each trial. The results reported
in the main text and in Figure 7 and Supplementary Figure 4 correspond to
the average accuracy, across all 54 retrieval trials, to predict the target and competitor categories, respectively.
A Supplementary Methods Checklist is available.
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