Download Possible Solutions from the Cognitive Neuroscience of Emotion

Possible Solutions from the
Cognitive Neuroscience of
Emotion
David Sander
Geneva Emotion Research Group
University of Geneva
A role for CN in designing
emotion-oriented systems?
Levels of analyses in CN
Problems, and CN directions
Artificial emotions
Recognition of facial expression
What is CN?
The emergence of a discipline
Cognitive Neuroscience Institute (Dartmouth): 1982
Journal of Cognitive Neuroscience: 1988
Cognitive Neuroscience Society: 1993
Institute of Cognitive Neuroscience (London): 1996
« the
task of cognitive neuroscience is to map
the information-processing structure of the human mind
and to discover how this computational organization is
implemented in the physical organization of the brain »
Tooby & Cosmides (2000)
Levels of analyses in CN
The perils of sitting on a one-legged stool
(Kosslyn & Intrilligator, 1992)
Only one
paradigmatic
leg
a “stability
peril” for the
model
Informationprocessing model
B
E
H
A
V
I
O
R
Many Psychological
models are “sitting” only
on a behavioral account
Levels of analyses in CN
The perils of sitting on a one-legged stool
(Kosslyn & Intrilligator, 1992)
Only one
paradigmatic
leg
a “stability
peril” for the
model
Informationprocessing model
B
R
A
I
N
Many Neurobiological
and some
neuropsychological
models are “sitting” only
on a brain account
Levels of analyses in CN
The perils of sitting on a one-legged stool
(Kosslyn & Intrilligator, 1992)
Only one
paradigmatic
leg
a “stability
peril” for the
model
Informationprocessing model
C
O
M
P
U
T
A
T
I
O
N
Many Artificial
Intelligence models are
“sitting” only on a
computational account
The advantage of sitting on a three-legged stool
Three
paradigmatic
legs
more stability
for the model
Informationprocessing model
C
O
M
P
U
T
A
T
I
O
N
B
E
H
A
V
I
O
R
B
R
A
I
N
Ideal CN
models are
“sitting” on
behavioral,
brain, and
computational
accounts
Cognitive Neuroscience Triangle
Behavior
Computation
Analyses
Models
Brain
Neural Activity
(Neurophysiology)
Areas & Connections
(Neuroanatomy)
Problems, and CN directions: Problem 1
Emotion-oriented system, but...
...oriented towards which level?
Behavioral
An artificial
behaviorally believable
output response given
a natural input,
whatever the
plausibility of the
architecture
Other (?)
Computational
(or representational)
Neural
Problem 1
Natural Processes versus Artificilal Efficiency
Is it important to know how the human brain computes emotion in
order to develop a “humaine” emotion-oriented system?
Appraisal of a threat,
Autonomic activity,
Withdrawing,
Expression, and
Feeling of being afraid
Behavioral plausible output:
“humaine”
Autonomic activity,
emotion-oriented
Withdrawing,
system
Expression of fear.
Problem 1
Emotion-oriented system, but...
...oriented towards which level?
Behavioral
An artificial
behaviorally believable
output response given
a natural input,
whatever the
plausibility of the
architecture
CN is useless
Computational
(or representational)
An artificial system that
is constrained by the
functional architecture
designed by CN results
Problem 2
Selecting the functional architecture to be
implemented in an artificial emotion system
i. Dissociation of emotional processes
ii. Implementation of emotional
processes in the brain
iii. Time course of emotional processes
Problem 2: selecting the functional architecture
Three main approaches:
Basic Emotions Approach
Dimensional Approach
Systems-level Approach
Problem 2: selecting the functional architecture
CN and Basic Emotions
Most of the past Cognitive Neuroscience researches
on emotion focused on the attempt to find specific
brain regions implementing discrete basic emotions:
“The various classes of emotion are mediated by
separate neural systems (...)” (LeDoux, 1996)
Problem 2: selecting the functional architecture
CN and Basic Emotions
Öhman & Mineka (2001):
«The amygdala is a fear module…
Basica!y, the fear module is a device for activating
defensive behaviour and associated psychophysiological
responses and emotional feelings to threatening stimuli.»
Panksepp (2003)
237-239
Problem 2: selecting the functional architecture
CN and the Dimensional Approach
Some recent Cognitive Neuroscience researches were
interested in dissociating the dimensions of Valence
and Intensity (Anderson et al., 2003; Small et al.,
2003).
(!! Intensity≠ Activation !!)
Valence versus Intensity
Anderson et al. (2003), Nature Neuroscience
Problem 2: selecting the functional architecture
CN at the systems-level
Some CN researchers take into consideration the
complexity of emotion by parsing its subcomponents
at the systems-level and, sometimes, by attempting to
model the interactions between the proposed
processes:
Action tendencies (e.g., Davidson)
Somatic signals (e.g., Damasio)
Feeling (e.g., Lane)
Action tendencies (e.g., Davidson, 1995)
Perception/Production
distinction between perception of
the emotional value of a stimulus
versus the production of
expressive behavior
Anterior activation
asymmetry model
Left anterior region
associated with approachrelated emotions
Right anterior region
associated with withdrawalrelated emotions
Somatic signals (e.g., Damasio, 1998)
A critical function of somaticrelated signals and their
integration with the other
brain signals.
Feeling
Feeling as an integration of some emotional signals
The conscious experience is integrated via a
convergence zone that could be the Anterior Cingulate
and/or the Medial Prefrontal Cortex (Reiman. 1997;
Lane, 2000).
The subjective feeling is integrated via the
synchronization of other components (Scherer, 2003).
Binding through synchronization was proposed for the
visual system for example.
Appraisal Theory
Relevant (e.g., unpleasant, goal obstructive),
Difficult to cope with
Event
Appraisal Processes
Emotional Expression
Autonomic activation
Action Tendencies
“Withdrawal”
Subjective Feeling
“I am a$aid”
Cognitive Neuroscience of Appraisal Processes
High level
exteroceptive
processing
Sensory
cortices
Sensory
Thalamus
Coarse
exteroceptive
processing
Integrative
cortices
Intrinsic
pleasantness
Motivational
bases (reward)
Relevance
detection
Sander & Scherer, in prep.
Goal
representation
Hippo OFC
Ventral
Striatum
Amy
Normative
Significance
DLPFC
Con
d e p text
end
enc
e
Event
Implication
Action
tendency
MPFC
ACC
Regulation,
coping
Somatosensoryrelated cortical Somatic maps
and subcortical
structures
Emotional
expression
Neuroendocrine/Autonomic/Somatic NS
Body state
Problem 3
Recognition of facial expression
(from Haxby et al., 2000)
Colliculus-pulvinar-amygdala
Pathway
Visual Cortex
LGB: Lateral Geniculate Body
V1
SC: Superior Colliculus
V1: Primary Visual Cortex
Pulvinar
Amygdala
SC
LGB
Retina
Recognition of a facial
expression of fear
Stimulus  120 ms:
Fast early processing of highly
relevant events
A, amygdala; FFA, fusiform face area;
INS, insula; O, orbitofrontalcortex;
SC, superior colliculus; SCx, striate
cortex; SS, somatosensorycortex; STG,
superior temporal gyrus; T, thalamus.
From Adolphs (2002).
Current Opinion in Neurobiology
170 ms:
- detailed perception;
- emotional reaction involving the
body
> 300ms:
Conceptual knowledge of the
emotion signaled by the face
Emotion-oriented system, but...
...oriented towards which level?
Behavioral
An artificial
behaviorally believable
output response given
a natural input,
whatever the
plausibility of the
architecture
CN is useless
Computational
(or representational)
Neural
An artificial system that
is constrained by the
An artificial system that
functional architecture is constrained by the
designed by CN results functional architecture
and natural neural
networks properties
CN can help
CN can help
Problem 4
Multimodal integration
Timing: Results suggest that audio-visual emotional binding is
early in time (110 ms post-stimulus)
Integrative structure
Amygdala response
to congruent fearful
voices and faces
Dolan et al. (2001)
->Test of multimodal emotion display in ECA using brain-imaging
Problem 5
Influence of dynamism in the facial
expression on perceived emotion
Emotion morphs depicted expression changes of ‘getting
scared’ or ‘getting angry’ in real-time.
Brain regions implicated in processing facial affect, including
the amygdala and fusiform gyrus, showed greater responses to
dynamic versus static emotional expressions.
Labar et al. (2003)
Conclusion
Cognitive Neuroscience can help to find solutions for emotionoriented systems mainly if they are focused on the
computational, and/or the neural levels.
Artificial emotions: A decisive choice between:
as many systems as emotions
different systems for approach-related versus withdrawalrelated emotions
a system for intensity, a system for valence (but, only feeling)
a system for each emotional component
Recognition of emotional expression: Modeling two pathways
(one for coarse and fast processing, and one for detailed proc.).
A computational model of emotional processes would benefit
from modeling other closely related cognitive processes, such as
attention.