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Chapter Six
Memory & Connectionist Models
Memory is central to any cognitive system. Consider what it would be like to get into
your car tomorrow and realize your body no longer knew the correct motor movements
for driving. As this realization hits you, you realize that you do not remember the steps it
would take to start the car (e.g., the fact that to start a car, you need to put the key in
the ignition switch and turn it), and then it hits you that you do not have a recollection of
any events from the past where you have driven a car. It is as if you have never been in
a car before. In fact, you are no longer sure the car you are now sitting in is actually
yours. As this example suggests, memory is at the heart of any intelligent cognitive
system. Without memory the cognitive system is stuck in the here and now; there is no
repository of knowledge to guide intelligent action or make sense of the world. Without
memory, every action requires figuring out the motor movements necessary to achieve
said desired action. Without memory the world has no continuity, it is as if one lives only
in the moment, and there is no possibility of a sense of self. Without memory how could
you answer the question, “Am I the sort of person who would get into someone else’s
car and try to steal it?” In addition to pointing to the centrality of memory to cognition,
this example also makes it apparent that many types of information must be stored in
memory. Motor skills, facts, and events from our lives, for example, are important types
of information. One major question for scientists who study human memory is the extent
to which evolution has given us distinct memory systems for different types of
information.
This chapter:
•
•
•
•
•
•
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Defines a memory system.
Discusses the distinction between the systems and process approach to
studying memory.
Explores an early information-processing model of memory: The early 3-store
modal memory model.
Discusses how the STM conceptualization of immediate memory in the 3-store
modal memory model was upgraded to the working memory (WM)
conceptualization.
Discusses the major long-term memory systems of the brain.
Briefly presents 2 memory process distinctions as examples of the process
approach to memory.
Discusses connectionist network models and how these might be used in
models of memory.
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Outline of Topics
I. How Do Cognitive Scientists Study Memory?
II. The Systems Approach: The Landmark Example of H.M.
III. The 3-store Modal Model of Memory
A. Sensory Stores
B. Short-term Memory (STM)
C. Long-term Memory (LTM)
D. Are STM and LTM Separable Memory Subsystems?
IV. Immediate Memory Reconsidered as Working Memory (WM)
A. Original Baddeley & Hitch (1974) model
B. Summary of the WM Approach
C. Future of the WM Approach
V. Long-term Memory (LTM) Systems
A. Declarative (Explicit) Memory
B. Nondeclarative (Implicit) Memory
VI. The Process Perspective
A. Recollection & Familiarity
VII. Connectionist Models
References
Glossary
I. How Do Cognitive Scientists Study Memory?
A memory system has 3 important tasks to perform:
Encoding, storage, and retrieval. Information from the
world
must
be
acquired
and
represented.
Representations must be consolidated into a durable
enough form to fit the desired retention period (e.g.,
fractions of a second, seconds, minutes, or years). Once
encoded, information must then be stored and
maintained so that it may be retrieved. At the time of
retrieval, efficient processes for effective retrieval will be
important. Moreover, it is important to understand how
different retrieval problems will emphasize different sets
of retrieval processes, and how that information is
encoded will also have important implications for what sort of retrieval processes will be
most effective.
Central to the cognitive approach is the development of models of memory. One
influential theory is the 3-store modal model (Neath & Surprenant, 2003) that consists of
a sequential series of locations dedicated to storage of representations for increasing
lengths of time as information passes from one store to the next. More recent models
involve representation of information distributed across numerous neural-like
computational units that both process and store information. This family of models is
referred to as neural network or connectionist models. We discuss some of the
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influential traditional memory models, and we present some of the basic concepts of
connectionist models.
Historically, memory researchers have chosen to adopt one of two major research
strategies, one with a focus on the processes of memory and the other with a focus on
memory systems of the brain. The process approach concentrates on the complex
processes used during encoding and retrieval. For example, the processes of encoding
and retrieval are likely to be quite different if participants are to perform a free recall
task, where they are given a blank sheet of paper and must recall all of the studied
items without any help, versus a recognition task, where they are presented with an
item and must simply recognize it as having been part of the study set. Another way of
approaching memory is to study the memory systems of the brain under the assumption
that evolution has yielded multiple memory systems to deal with multiple types of
information in the environment. We explore each of these perspectives as well as briefly
discuss some of the major theoretical approaches to memory developed by cognitive
scientists.
“Chimp vs Human – Memory Test”
II. The Systems Approach: The Landmark Example of H.M.
The modern era in research on memory systems
was initiated in 1953 when a 27 year-old epilepsy
patient known as H.M. underwent a surgical
procedure that involved bilateral (i.e., both sides)
removal of the medial temporal lobes, including
most of each hippocampus, the surrounding
anterior medial temporal cortex, and each amygdala
(see Figure 1, also consult the Cognitive
Neuroscience chapter). This radical step had
become necessary as his epileptic seizures had
stopped responding to drug treatment, and had
become life threatening in severity. While the
surgery greatly improved the epileptic seizures
experienced by H.M., it became almost immediately
apparent during his postoperative recovery in the
hospital that he was now suffering from a severe
amnesia (Milner, 2005). However, even though he
lost some of his ability to recall events and facts that
he learned during the month prior to his surgery, he did not have the sort of retrograde
or backward looking amnesia depicted in television soap operas and other popular
media where a character might lose the ability to remember events or facts (e.g., their
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name) from the period prior to the injury. By contrast, H.M.’s memory for the events of
his life, for example, childhood events, or the car he drove and the house he lived in
prior to the surgery, were basically intact. What he had lost was the ability to form new
long-term memories. He could not recall the names or faces of hospital staff, even if
they had just been talking to him and stepped out of the room for a few minutes. He had
forgotten the way to the bathroom and could not relearn this information. From this point
on HM was forced to live a sheltered life in the hospital, and later in an assisted care
setting. He watched TV regularly, but was unable to learn all but a small collection of
new facts and new words that came into use in the English language since his
operation. His ability to remember life events since his operation was almost nonexistent. In other words, H.M. had a severe anterograde, or forward looking, amnesia.
“H.M.”
In the years that followed, H.M. was to
become the most tested neuropsychology
patient in history, beginning with Brenda
Milner’s groundbreaking work to bring this
surprising case to the attention of the scientific
world (Milner, 2005). While H.M. was not the
first patient to be reported with memory
problems following removal of brain tissue, his
case marks the modern study of memory
systems of the brain because his impairment
stood in such stark contrast to his preserved
function in several major cognitive areas. While
his memory impairment was surprisingly
severe in terms of the loss of ability to encode
new events and most new facts in long-term
memory, extensive testing demonstrated preserved performance on intelligence tests,
as well as tests of perception and reasoning. In fact, H.M. was able to converse
normally with others in his daily life. H.M. was also able to hold small amounts of
information in memory for a brief period; for example, he was able to remember a 3-digit
number for 15 minutes at one point. It was also the case that H.M. could remember
many facts and events from his life prior to the surgery, indicating that the hippocampus
and surrounding medial temporal lobe was probably not the storage system for longterm representations, but rather involved in acquisition and encoding. Finally, when
tested for the ability to learn motor skills he showed a normal ability. When presented
with these tasks by the same researcher at a later date, even though he could not
remember having learned the task previously, or having previously met the psychologist
who was now testing him again, he showed a normal shortened time to relearn the task
indicating preserved motor learning from the previous session. Because of this distinct
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pattern of deficit and preserved function, H.M. became the standard which other
amnesic patients were to be compared against.
To learn more about H.M., and to hear researchers who interviewed him, go the
National Public Radio website and click on the “Listen” button. As H.M.’s own words
attest, he was sometimes concerned about the feeling he had that he was simply living
in the moment, but then, his mental world would move on and he would process a new
set of events:
Right now, I’m wondering. Have I done or said anything amiss? You see, at this
moment everything looks clear to me, but what happened just before? That’s what
worries me. It’s like waking from a dream; I just don’t remember. (Milner, 1966)
Figure 1. Horizontal T2-weighted MRI section from H. M. The bright
signal areas indicate the extent of the anterior medial temporal lobe
removal.
III. The 3-store Modal Memory Model
One contribution of the cognitive approach to memory is the proposal of distinct
memory systems that operate at different levels of time scale. Figure 2 presents a
combined information processing view of memory that sums up research from the ‘60s
and ‘70s and clearly distinguishes 3 subsystems of memory that operate at increasingly
long time scales (for more information consult Healy & McNamara, 1996). The term
modal is used to indicate that this model is representative of a number of theories of the
time. There is an emphasis on the use of verbal items (i.e., words, letters, digits) and
memory is viewed somewhat passively as a location to store items. Sensory input to the
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cognitive system is briefly held in a sensory form (i.e., no perceptual identification or
semantic meaning analysis). Information that is attended is moved into short-term
memory (STM), which can be thought of as containing the contents of current conscious
awareness. Finally, portions of STM may be moved to more durable storage in longterm memory (LTM).
A. Sensory Stores.
Sperling (1960) studied the operation
of the visual sensory store. He had
participants view briefly flashed arrays of
letters (0.05 s) and used a partial report
procedure where participants listened to a
tone to tell them which part of the letter
array to report. This partial report
procedure was necessary because when
participants would attempt to report all the
letters in the array, they would complain
that they had the sense that they
perceived all of the letters momentarily
but that the letters would fade from
awareness very quickly. Figure 3
presents
the
results
of
this
groundbreaking study, indicating a high percent of letters in the display were available
when the letter array was presented at the same time as the tone. However, as the
delay between the letter array and the tone is increased, the percent of items available
for report decays to the point that it is nearly the same as when participants are asked
to report all items in the letter array. Successive research studies have confirmed this
finding, and have demonstrated that the rate of decay of information in the visual
sensory store depends on viewing conditions (e.g., size and font of letters, spacing of
letters, contrast of letters with background of display), but the primary finding is the
visual sensory store holds visual information available for further processing for
fractions of a second to a couple of seconds. Moreover, the representations in visual
sensory memory appear to be coded in a pre-categorical sensory form. For example,
participants can perform the partial report task easily, if they select which letters in the
array to report based on visual aspects of the display such as location (e.g. row), or
color (e.g., just the red items). However, performance is greatly impaired if they are
asked to partially report based on category (e.g., just the letters, or just the digits). Other
researchers have provided evidence for other sensory stores (e.g., auditory, touch). The
visual and auditory sensory registers are by far the most extensively studied.
B. Short-term Memory (STM).
STM is a limited capacity system for holding information in an active state on the
order of seconds. Most theorists during the modal model’s formative period held the
view that items attended to both entered consciousness and were selected for encoding
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in STM (Smith & Kosslyn, 2007). A time-dependent decay of that activity level of
representation was thought to lead to forgetting unless active rehearsal of items in STM
was employed to keep representations from decaying before they could be moved to a
more stable representational form in a longer-term store. The prototypical real-world
task that motivates understanding of the usefulness of STM is the example of a friend
telling you to call a certain phone number. Most people faced with this task will find
themselves mentally rehearsing the number as they pull out their cell phone, power it
up, and dial. STM is proposed as an immediate memory system for holding
information briefly for a few seconds for immediate use, or alternatively, as a system for
extending the contents of consciousness. Most of the research used verbal memory
items, and so it is no surprise that STM was thought of as representing information in a
verbal form. Other representations (such as visual and semantic) were discussed from
time to time, but STM was mostly discussed as a system using verbal representations.
Moreover, STM was seen as a unitary system even in those cases where multiple types
of representation were proposed. For example, much was made of the finding that STM
memory tasks would yield far more rhyme errors (confusions based on how a word
sounds) than synonym errors (confusions based on the meaning of a word).
STM is viewed as a limited capacity system that can reliably hold a certain number of
items, with capacity varying for different types of items and for different individuals
(Neath & Surprenant, 2003). To save space, items can be combined into functional
chunks, depending upon personal experience. For example, you might combine the
ordered list of 3 letters, F-B-I, into the single informational chunk FBI (Federal Bureau of
Investigation) based on your personal experience as an American, whereas someone
from another country might not do so and would have to deal with the list as 3 separate
verbal items. The way that psychologists determine the capacity of STM is by testing
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with an STM span test where items are presented one-by-one and immediately
repeated back in order. The most commonly used STM span test is the digit span test.
A varying number of randomly ordered digits are auditorily presented, and the person
being tested immediately repeats the digits back in order. What tends to happen is
participants will perform at a very high level, virtually error free, until the number of digits
presented crosses some threshold and they will suddenly produce frequent errors. This
threshold number is the digit span of the individual, or the number of random digits that
can be reliably repeated without error. A variety of STM span tests have been devised
using different materials (e.g., spoken words, visual words, visual shapes, spoken digits
repeated backwards), with the same basic testing procedure. The normal range for
single syllable items that do not encourage chunking (e.g., digit span in English), is
typically about 7±2 items, but the increased difficulty of non-verbal or multi-syllable
materials typically yield lower spans.
C. Long-term Memory (LTM).
By contrast, LTM is a large
capacity
system
for
holding
information in a more stable, but
less active state, than STM. STM is
proposed as the gateway to LTM
(Neath & Surprenant, 2003). Items
in STM are encoded in LTM using 2
types of rehearsal processes, rote
rehearsal
(repetition)
and
elaborative rehearsal where the
meaning of the item is associated
with meaningful information already
in LTM. The classical research on
LTM
focused
on
semantic
representations for verbal materials.
Studies of LTM using word lists find less sensitivity to rhyme confusions than synonym
confusions, the opposite of STM with the same materials. This suggested to
researchers of the time that, at least for verbal items, we tend to represent information
according to the meaningfulness of the items presented. We say the capacity of LTM is
large, but we really do not know how large, and it will be extremely difficult to find out.
We do know that information can be forgotten, but this may be due to a problem with
retrieval processes, or the information may not have been durably encoded in the first
place, rather than any capacity limitation. It is important to note that psychologists have
developed span tasks to measure the capacity of STM, but no widely accepted tests
exist for the capacity of LTM.
D. Are STM and LTM Separable Subsystems?
The standard story, based on many early studies that at first appeared to yield
clearly interpretable results, was that STM and LTM form what is known as a double-
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dissociation pattern indicating separable systems for STM and LTM. One important set
of evidence comes from studies of the serial position curve during free recall of lists of
randomly ordered words. In this procedure, relatively long list of words (in the 15-25
range is common) are verbally presented at a set rate, and upon completion,
participants are signaled to either recall as many words as they can immediately, or
after a brief period of a simple distraction task (e.g. counting backwards from 100 by
3’s). When recall for each word as a function of the word’s position in the list (e.g.,
percent recall for the 1st versus the 10th versus the 20th word in the list). Figure 4
presents stylized results that summarize typical results.
“STM and LTM”
The standard serial position curve has a region of increased recall for the first few
items of the list, called the primacy effect, which is thought to reflect improved encoding
of the first few words into LTM. The standard serial position curve also has a region of
improved recall at the end of the list, called the recency effect (these are the items most
recently presented), which is thought to reflect direct retrieval from the words active in
STM. Manipulation of the rate of presentation of the words in the list usually affects the
primacy portion (LTM) of the serial position curve, leaving the recency portion (STM) of
the curve relatively unaffected. The opposite holds for the addition of a distracting verbal
task where the recency portion (STM) of the curve is typically far more affected than the
primacy portion (LTM). The fact that both experimental manipulations primarily affect
one portion of the curve leaving the other relatively unaffected is a sort of doubledissociation pattern.
A more traditional double-dissociation involving neurological patients with mirror
image patterns of memory deficits has also been reported. Patient H.M., with an
impaired ability to encode new events and most new facts into LTM due to medial
temporal lobe surgery, shows a digit span in the normal range (i.e., normal STM) but
greatly impaired LTM for word lists. Converse patients, E.E. and K.F., have been
reported with inferior parietal and superior temporal lobe damage who produce LTM test
scores in the normal range compared to control subjects, but greatly impaired digit and
word spans (STM). These and similar results reported in the literature made the
STM/LTM distinction a strong one for many early researchers, but newer research
(Neath & Surprenant, 2003) and a new wave of theoretical models (Cowan, 1995) are
calling the strict separation of STM and LTM into question. One promising proposal
(Ranganath & Blumenfeld, 2005) is that the same neural networks that hold information
active are involved in storage in LTM. If this proposal is correct, then STM and LTM are
more connected than previously believed. In the future, this will most likely be an
increasingly active area of research.
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Sensory Input
Sensory Stores
Visual
Auditory
Touch
...
Attention
Short-Term Memory (STM)
Long-Term Memory (LTM)
Figure 2. 3-store modal model of memory. Sensory stores briefly hold
sensory information for further processing. Information attended to is
moved to a limited capacity short-term memory (STM) and can remain
available as long as it is maintained in an active state. Some of the
information in STM may be moved to a more durable storage system,
long-term memory (LTM).
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Percent Reported Letters
100
80
60
40
20
0
-0.2
0
0.2 0.4 0.6 0.8
1
1.2
Delay of tone (seconds)
Figure 3. Percent of letters reported from a letter array using a partial
report procedure. A tone indicates which row in the letter array to
report. The tone is offset in time, falling either slightly before,
simultaneous with, or delayed from the onset of the letter array on the
screen. The height of the bar on the right indicates the percent report
when participants must report all letters (i.e., whole report instead of
partial report). Adapted from Sperling (1960). [Sperling, G. (1960). The
information available in brief visual presentations. Psychological
Monographs: General and Applied, 74, 1-28.
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Percent Recall
100
Standard Presentation
Distraction
Faster Presentation
Primacy-LTM
Recency-STM
0
1
20
Word Position in List
Figure 4. Stylized results of studies of the serial position curve, free
recall as a function of word position in a list of items presented one-byone. In the standard presentation condition, there is greater recall for
the first few words presented, called the recency effect, this is thought
to be due to greater encoding into LTM in relation to words in the
middle of the list. Also, there is greater recall for the last few items,
presumably due to direct retrieval of active words in STM. Manipulation
of presentation rate and distraction (e.g., counting out loud) between
the last word and commencement of recall selectively reduce the
primacy (LTM) and recency (STM) portions of the curve, respectively.
This suggests separable STM and LTM systems.
Serial position curve online demonstration. Dr. Timothy Bender at Missouri State
University has posted an online demonstration of the serial position curve, along with
manipulations intended to reduce the primacy and recency effects (see Figure 4). The
reader is encouraged to go to the Web page and click on and run the Serial Position
Curve demo.
IV. Immediate Memory Reconsidered as Working Memory (WM)
Besides extending the contents of consciousness, what do we need from our
immediate memory system? One major purpose of such a system might be to support
complex cognitive processing involved in such tasks as reading, reasoning, problem
solving, and decision making. Consider the following example. You have just finished a
meal at a restaurant and you are presented with the bill. You want to leave a 15% tip,
and so you close your eyes and compute the tip in your head. You begin by figuring
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10% of the bill and temporarily storing that amount. Then, you halve the 10% subtotal to
get 5% of the original bill. Finally, you add the current 5% result to the 10% value you
had been holding in memory to get the 15% tip. This example highlights immediate
memory as having an active role in providing a mental workspace in support of ongoing
cognitive processes. We can contrast this example with the example we used to
motivate STM, the task of holding a phone number active in immediate memory until we
are ready to dial the number on our cell phone. We now see that STM proposes an
immediate memory system that is
a location for storing information
for a brief period until the
information is to be used. STM
does not stress the need to hold
information while performing
concurrent
processing
is
performed, and is therefore
thought of as a passive memory
system. An alternative approach,
called the working memory
approach, instead views the
immediate memory system as
providing storage concurrent with
ongoing
active
cognitive
processing.
Over the years, researchers have identified 2 more important weaknesses of the
STM model of immediate memory (Smith & Kosslyn, 2007). One problem is that the 3store modal model is a serial processing model, and STM is proposed as the gateway
to LTM. As we discussed earlier during the discussion of the double-dissociation of STM
and LTM systems, patients have been identified who show quite impaired digit and word
spans on traditional STM span tests, while showing normal performance on tests of
LTM. It is difficult to see how this could be the case if STM is the gateway to LTM.
Another problem with the STM approach is its reliance on verbal codes. While alternate
representation schemes for STM have been proposed, such proposals were relatively
rare and failed to propose separable subsystems for different representational codes.
Evidence from studies of dual-task performance has accumulated over the years
supporting, for example, separable immediate memory systems for verbal and visual
information. Logie et al. (1990, as reported in Baddeley
et al., 2009) provide an influential example.
The logic behind the dual-task methodology is that if
two tasks use the same immediate memory system,
then performance on one of the tasks should be
interfered with when the other task is also performed.
Logie et al. had participants perform either a verbal
(spoken consonant) span task or a visual (checkerboard
pattern location) span task. While performing one of
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these span tasks to determine the capacity of immediate memory for verbal or visual
information, participants either performed a second visual or verbal task. Figure 5
presents the visual and verbal span scores when these span tasks were performed
concurrently with a second interfering task. Scores are presented as a percent of the
score they earned on these span tasks when performed in isolation. As can be seen,
immediate memory spans were high (about 80% of the span for each span task in
isolation) when performed with a second interfering task that was from the opposite
modality (e.g., visual span with verbal secondary). However, memory spans were much
lower (30-40%) when the span tests were performed in conjunction with a secondary
task from the same modality (e.g., visual span with visual secondary). This pattern of
results has been observed in a range of studies and has been taken as strong evidence
for separable verbal and visual immediate memory subsystems.
A. Original Baddeley & Hitch (1974) WM Model
Because of evidence such as that presented in Figure 5 suggesting a need for
separate visual and verbal subsystems, and because they believed that the true
purpose of the immediate memory system was to provide a mental workspace in
support of complex cognition, Baddeley and colleagues (Baddeley et al., 2009;
Baddeley & Hitch, 1974) have proposed a modified theory called working memory.
They envisioned a working memory system with independent modality-specific modules
for holding verbal and visual information in an active state: phonological loop and
visuospatial sketchpad (see Figure 6). A central executive subsystem provides control
and coordination functions.
Phonological loop. The phonological loop is made up of 2 parts. The phonological
store can hold memory representations (in a phonological form that represents what a
word sounds like) in an active state for a few seconds before they fade, and the
articulatory rehearsal process that is analogous to the silent inner speech that most
people experience when they read. To be refreshed, phonological representations are
retrieved from the phonological store and re-articulated using silent inner speech. The
working memory model predicts,
correctly, that items that take longer to
speak aloud (overt articulation) are more
prone to being lost from the
phonological loop (Baddeley, 2003). The
phonological loop can be seen as a
more detailed and flexible version of the
verbal STM proposed in the modal
model.
Visuospatial sketchpad. Imagine that you are playing a game of checkers with your
young niece who suddenly knocks the board over. In attempting to recreate the game,
you close your eyes and imagine the game board with the pieces arranged as they were
just before being upended. The ability to create and manipulate a visual image is
thought to be a central function of the visuospatial sketchpad. Baddeley and colleagues
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(Baddeley et al., 2009; see also Neath & Surprenant, 2003) have proposed that the
visuospatial sketchpad has a refreshing mechanism (called the visual scribe). Recent
evidence suggests that the same neural systems that allow us to move the focus of
visual attention around the external visual world allow us to covertly move our mental
focus of attention around the imagined visual world, acting to refresh locations and
shapes in the visuospatial sketchpad (Smith & Kosslyn, 2007). There is also
accumulating evidence for 2 separable working memory systems, one for visual shape
information, and the other for spatial location information. For example, Kohler et al.
(1995) had participants view displays with pictures of common everyday objects in
particular positions in the visual field. After a brief delay, a second display appeared
which might contain the same objects in the same locations, or some objects might be
shifted in location, or the locations may be as before, but some of the object shapes are
changed. Participants had to either detect changes in location or changes in object
identities while a functional PET scan was conducted. The results indicated that shape
change detection following a brief delay depended on temporal lobe areas responsible
for object identification, and that object location change detection following a brief delay
depended on parietal areas responsible for keeping track of the location of objects.
Whether these subsystems for shape and spatial location use the same neural areas for
refreshing visual and spatial information is a matter of much current debate.
Central executive. The central executive can be thought of as a cognitive controller
that (a) selects which modality-specific subsystem is used for storage, (b) controls the
timing of input to each subsystem, (c) integrates and coordinates between subsystems,
and (d) provides the cognitive control mechanisms for access to the information in the
subsystems for use in complex cognitive processing (Smith & Kosslyn, 2007).
“Central Executive”
Working memory span tests. Just as in the
STM model it was designed to supplant, the WM
model is comprised of limited capacity
subsystems. There are 2 major differences,
however, as the WM model allows for the
phonological loop and visuospatial sketchpad to
work somewhat independently, with only partially
interfering processes (see Figure 5). As discussed
earlier, researchers using dual-task methodology
have found that span (capacity) of each
subsystem is only modestly affected by an
interference task from the opposite modality (e.g.,
visual secondary task and verbal span test). A
second major difference with the STM model is
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the WM attempts to explain immediate memory performance in the presence of
concurrent cognitive processing. It is therefore a major requirement of WM span tasks,
as opposed to STM span tasks, that the newer WM span task require storage for brief
intervals during concurrent performance of a processing task. Figure 7 presents a
typical WM span task. Participants must hold a growing list of words in WM as they are
presented with a concurrent processing task of verifying the correctness of simple
arithmetic problems. Their WM span is the number of words they can reliably recall in
order without error. By contrast, the common digit span (STM span) task only requires
storing digits for immediate recall, there is no concurrent processing load. It should be of
little surprise that WM span tasks are more difficult, and the normal range of spans on a
typical WM span task is less than that for the typical STM span task. Also, in keeping
with the fact that the WM model is designed to provide a more realistic explanation of
how we use our immediate memory abilities to provide temporary storage to support
complex cognitive processing tasks (e.g., reasoning, decision making, reading) a
common finding has been that scores on tests of complex cognition (e.g., reading
comprehension) are typically more strongly related to WM spans than they are to STM
spans. That is differences in scores for 2 individuals on a difficult reading
comprehension test are better predicted by their differences on WM span tasks (e.g.,
Figure 7) than by their scores on a STM span task (e.g., digit span). This is presumably
because the WM span tasks better capture how WM is used when we actually use it
during a complex cognitive task such as reading.
STM and WM span test demonstrations online. Dr. Timothy Bender at Missouri
State University has posted versions of the digit span and operations span tests
commonly used to assess immediate memory capacity from an STM (digit span) or a
WM (operations span) perspective. The reader is encouraged to go to the Web page
with these demos and to click on and run the digit span and operations span demos.
B. Summary of the Working Memory Approach.
The working memory model proposed by Baddeley and colleagues (Baddeley, 2003;
Baddeley et al., 2009) handles the 3 shortcomings of the STM model discussed earlier.
The great strength of the WM approach is that it better captures our need to use our
immediate memory system to support ongoing complex cognition. WM is a
fundamentally active system that must store information in the presence of concurrent
processing, in comparison to the more passive STM view that only requires passive
storage of information for a brief interval until it is needed. The WM approach also
provides for a wider variety of codes (e.g., visual, spatial, verbal) that are implemented
in modality-specific subsystems that explain the results of dual-task studies indicating
minimal interference across code modalities (e.g., visual vs. verbal). Moreover, the WM
model does not propose a strict serial order of processing as does the STM model of
the 3-store modal memory model. In the modal model, STM is seen as the gateway to
memory, but the discovery of patients with neural damage leading to drastically
impaired STM span (e.g., trouble repeating back more than 2 digits in the digit span
task), but relatively preserved ability to encode information into LTM have called into
question the assumption of a short-term store that acts as a gateway to LTM. The WM
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approach leads to a more flexible approach to interactions between the immediate
memory system and LTM than does the older STM approach.
C. The Future of the Working Memory Approach.
Baddeley (see Baddeley, 2003, for a readable review) has recently made some
serious upgrades to his WM model, which, in its original form, was published in
Baddeley and Hitch (1974). Recent neuropsychological evidence from patients with
neural deficits, and from new brain imaging research on typical individuals, has led to an
accumulation of evidence for a working memory space for conscious awareness
(Dehaene & Naccache, 2001). For example, patients have been identified who have a
severe amnesia that keeps them from encoding new facts and events into LTM, but with
an ability to immediately repeat extended sections of text back that far exceed the
phonological loop or visuospatial sketchpad. Another weakness has been that the
original version of the WM model did not contain explicit mechanisms for chunking
(e.g., one person might combine F-B-I into a single integrated representation FBI to
save space in a limited capacity WM system, whereas another person might represent
this information as a sequence of 3 distinct representations). Chunking in immediate
memory is well-documented and an important source of individual differences in
memory performance. Accordingly, Baddeley (see Baddeley, 2003) has recently
proposed the addition of a module, the episodic buffer that acts as a mental workspace
for conscious awareness and uses complex integrated multimodal representations that
can support chunking processes. As depicted in Figure 7, the expanded WM model is
more explicit about the ways that the WM subsystems interact with LTM. Note the
bidirectional arrows between sections of LTM and the phonological loop, episodic
buffer, and visuospatial sketchpad, denote strong bidirectional communication between
WM and LTM. Baddeley (2003) notes that one important pathway to the development of
the working memory approach to memory is to explore emotional and motivational
control over the processing goals of WM. Moreover, researchers interested in identifying
the networks of brain regions that support cognitive control processes have recently
proposed that lateral frontal brain areas long thought to be involved in the active
maintenance of representations in WM might also be involved in representing the
current processing goals of WM (e.g., Braver et al., 2002). This new focus on the factors
that drive, form, and represent the goals of the immediate memory system is a
promising area for research activity in the near future.
“Working Memory Approach”
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Percent Single Task Span
100
75
50
25
Verbal Span
Visual Span
0
Verbal
Visual
Secondary Interference Task
Figure 5. Performance on visual and verbal span tasks while
performing a secondary visual or verbal interference task. Performance
presented as a percent of span task performance when performed in
isolation. Greatest interference when the secondary interference task is
in same modality (e.g., visual span with visual secondary). Supports
proposal of a working memory system for immediate memory that has
separable subsystems for visual and verbal storage. Adapted from
Logie et al. (1990). [Logie, R.H., Zucco, G.M., & Baddelely, A.D. (1990).
Interference with visual short-term memory. Acta Psychologica, 75,
55-74.]
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Phonological
Loop
Central
Executive
Visuospatial
Sketchpad
Figure 6. Working memory, as proposed by Baddeley & Hitch (1974).
Is (2 × 6) + 3 = 14?
Is (8 × 3) - 9 = 15?
Is (3 × 4) + 7 = 19?
Is (5 × 3) - 2 = 16?
Table
Car
Tree
Rock
Figure 7. Example working memory span task. Participants must
process simple arithmetic problems while holding a list of words in
memory. Each line of the test is presented in isolation, and the
participant makes a yes/no response, before moving to the next item on
a new page (or computer screen). The list of to-be-remembered words
grows as each arithmetic problem is verified. The test ends with
immediate ordered recall of the words.
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Figure 8. The expanded WM model. Addition of an episodic buffer that
supports complex integrated multimodal representations allows for
chunking processes. The episodic buffer is proposed to act as a mental
workspace for conscious awareness. Also, the nature of WM-LTM
interactions have been made more specific. From Baddeley (2003),
Figure 5.
V. Long-term Memory (LTM) Systems
Researchers who study the memory systems of the
brain tend to take an evolutionary perspective. On this
view, the brain has evolved memory systems to deal
with different types of information. Consider the
question of what you had for breakfast this morning.
That deep fried pressed potato patty may have been
particularly flavorful and recalling the event of biting
into it, how it tasted and smelled, is making you
hungry. Consider the factual knowledge you have of
what that deep-fried potato patty from this well-known
fast food restaurant is called. You can easily recall the name of the patty, but you have
absolutely no recollection of when or where you first learned this factoid. It is as if the
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representation of the factual knowledge has somehow been separated from the events
you experienced when you first learned this information. Finally, consider teaching your
young niece to tie her shoes. At first you try to explain it verbally, but you get confused,
and suddenly you realize that words cannot capture your
true knowledge of the procedures used to tie a shoe. You
choose instead to demonstrate for her, first by tying one of
the shoes yourself, and then working on copying the
sequence of movements you have made by holding her
hands and guiding her movements. It is as if you do not have
full conscious access to the representations of the motor
movements required to tie a shoe.
As these examples suggest, memory researchers have
identified memory systems in the brain for encoding, storing,
and retrieving major types of information, most notably,
events, facts, and procedures. Figure 9 presents the
taxonomy of long-term memory systems in the brain. Two
important types of learned information, classical conditioning and non-associative
learning, have been excluded to improve clarity and to concentrate on those systems of
greatest interest to cognitive scientists. The LTM taxonomy proposes 2 major
subdivisions in LTM, declarative and nondeclarative memory (Smith & Kosslyn, 2007;
Squire, 2004).
A. Declarative (Explicit) Memory
Declarative memory refers to those memory systems that store facts and knowledge
about events in our lives in a form that is explicitly available for conscious retrieval,
hence the alternative term, explicit memory. By contrast, nondeclarative (implicit)
memories are stored in such a way as to not be directly accessible to consciousness via
explicit volitional retrieval processes. Rather, such memories are only indirectly
available. The fact that we have such memories is apparent in how the knowledge
representations influence our behaviors (e.g., being able to tie a shoe without conscious
access directly to the representation of this knowledge). It is as if nondeclarative
(implicit) knowledge is coded in a way that does not lend itself to verbal, visual, or
semantic (or other) codes used by the conscious mind.
Patients, such as H.M. who was discussed in an
earlier subsection of the present chapter, with
damage to the medial temporal lobes, including the
hippocampi, will typically present with an amnesic
syndrome that is marked by a decreased ability to
encode new facts and events into LTM. In the case
of H.M., there was a near total loss of the ability to
recall meeting and having a conversation with
people, and a major loss in the ability to learn new
facts (e.g., the location of the bathroom) following
203
his surgery. However, he could learn motor movement skills at a normal rate (e.g., using
a pointer to follow a moving object). Moreover, even though he would deny having met
an experimenter who was making a return visit, and he would deny having learned the
task previously, when tested on a previously learned motor movement task he would
learn the task faster a second time, just as a nonamnesic individual would. H.M. could
not remember the objects, places, or people he experienced and would rapidly forget
having a conversation with a person once they left the room and he shifted his attention
to something else. It was as if he was living totally in the moment. This form of amnesia
is known as anterograde amnesia, meaning a forward-looking amnesia for learning new
information. In fact, H.M. also showed preservation of another type of nondeclarative
memory effect, priming (see Figure 9, see also nondeclarative subsection below).
Eventually,
a
picture
emerged where patients with
damage to the hippocampus
and
associated
medial
temporal lobe cortex exhibit
impaired ability to encode
new facts and events, but
have a relatively preserved
ability to learn and relearn
motor skills, and to exhibit
priming effects (see section
on nondeclarative memory
for
an
explanation
of
priming). This has been
taken as strong evidence of
separable
declarative
(explicit) and nondeclarative
(implicit) memory systems. The declarative system employs the hippocampus and
medial temporal lobe as the gateway to memory representations that are explicitly
available to volitional retrieval into conscious awareness. Nondeclarative
representations, by contrast, are only indirectly available. We observe the influence of
nondeclarative learning as an implicit influence on behaviors (e.g., a child learns to tie
her shoes or ride a bike).
Episodic
(event)
memory.
Declarative memory can be further
fractionated into an episodic system for
representing the events of life in an
integrated multiomodal (i.e., visual,
auditory, verbal, semantic, touch, smell)
code. This leads to the question of where
episodic representations are actually
stored. Even though we have strong
evidence that the hippocampus and
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associated medial temporal lobe is the gateway to episodic memory, it is now believed
that actual storage is distributed throughout the neocortex. We can better understand
this principle by considering how encoding and retrieval might be performed. As
depicted in Figure 10, encoding of an event commences with a pattern of cortical
activity during perception of the event. Visual areas process visual aspects of the event,
auditory areas process auditory aspects of the event, and so on. The pattern of cortical
activation leads to a pattern of activation in the hippocampus that is then bound together
into an episode. Understanding how this binding of distributed representations is
thought to occur in the hippocampus is referred to as the binding problem. The binding
problem is an important focus of memory research. From this perspective, retrieval is
seen as a process of reactivation of the distributed bound representations in the cortex
of the various aspects of the event (e.g., visual, auditory, etc.). Retrieval begins with
volitional free recall processes, or with an object in the environment called a memory
cue that acts as a trigger (e.g., seeing an old friend may trigger retrieval of a past event
involving that friend). The bound episode representation in the hippocampus is activated
and acts as a key to unlock or reactivate the distributed representation of aspects of the
event in the cortex. Many researchers now believe that with time, and with repeated
retrievals, the representations in the cortex are somehow bound together in a more
direct fashion such that reactivation of the cortical representations is no longer
dependent upon the hippocampus. This process of forming a more durable bound
representation in the cortex is referred to as memory consolidation.
“Binding Problem”
Recent functional brain imaging research has confirmed the distributed nature of
storage of episodic information. Wheeler et al. (2000) used functional MRI (fMRI)
technology to scan for areas active during episodic retrieval. Participants learned a set
of pictures and sounds. On the 3rd day of the study, they were scanned during both
perceptual processing of the pictures and sounds and during episodic retrieval. The
results are depicted in Figure 11, and indicate that perceptual processing of pictures
activated widespread visual areas and perceptual processing of sounds activated
widespread auditory areas. What is of most importance was the fact that the activations
during retrieval of both visual and auditory items results in differential activation of a
subset of the same areas that had been activated during perceptual processing of those
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same items. These results suggest that episodic representations are stored in a
distributed manner in the cortical areas that are activated during perception. Current
research in functional brain imaging and memory is focusing on the specific role of the
hippocampi and associated medial temporal areas during encoding and retrieval.
Semantic (fact) memory. Much of what we
have discussed above regarding episodic
memory is applicable to semantic memory.
Semantic and episodic memory systems are
not as easily separable as are declarative
(explicit) and nondeclarative (implicit) memory.
For example, remembering events from a
childhood birthday may also require use of
meaningful concepts such as cake, balloons,
and clowns. Evidence from amnesic patients
points to a semantic system that depends on binding in the hippocampus and
associated medial temporal lobe areas just as the episodic system. What seems unique
about semantic memory is the apparent dissociation of factual knowledge from the
events surrounding learning those facts. Moreover, semantic memory is clearly
organized by meaning. As opposed to a dictionary, which stores items organized in
alphabetical order, we store conceptual items organized by meaning, with meaningfully
related items being more directly connected.
Before examining an important model of semantic memory, we must first consider
the contents of this system. A common approach has been to concentrate on concepts
and propositions (concepts and propositions have been covered in the Knowledge
Representation chapter) as representation types. A concept can be thought of as a
representation of some thing, event, or idea that includes all the information necessary
to allow categorization. For
example, the concept dog would
include a representation of the
knowledge that drives our ability
to categorize animals as dogs
versus non-dogs. A proposition is
a statement regarding concepts
that has a certain truth-value (e.g.,
my dog does not bite). It is worth
noting that the vast majority of
research
on
concepts
has
concentrated on concrete object
categories (e.g., dog, chair), and
far less research has examined
abstract concepts (e.g., war, love),
and we shall accordingly use
concrete concepts to motivate our
discussion.
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An important class of semantic memory models (e.g., Collins & Loftus, 1975), the
spreading activation semantic network, concentrates on how concepts are related in
semantic memory (see Figure 12). In this class of models, concepts are represented by
nodes and relationships between concepts by connections in a graphical network.
Concepts can be semantically related (e.g., dogs and cats are both mammals), or they
can be associatively related (e.g., salt-pepper) by virtue of co-occurrence in the
environment. Concepts can be more closely or more distantly related. Processing a
concept, for example, seeing a picture of a dog, leads to that concept being activated. A
common assumption is that activation of a concept above some threshold level brings it
into conscious awareness. Moreover, as a concept becomes increasingly activated,
activation can automatically spread to related concepts along the connections between
the conceptual nodes in the network. This partial activation of related concepts, typically
below the threshold for conscious awareness, may help prepare us for meaningful
information that may be needed in the immediate future. For example, a friend may be
talking about a visit to the doctor, and then jump to a statement about something a
nurse did. Having activation spread automatically from doctor, when that concept is
processed, to the related concept nurse, may help you process the word nurse more
efficiently, supporting language comprehension.
This type of effect, where prior processing of a related concept facilitates (what
psychologists call semantic priming) processing of a current concept is quite common.
Literally hundreds of studies have demonstrated that semantically and associatively
related words will prime each other, an experimental effect referred to as semantic
priming. For example, if you are given the task of naming words one at a time as they
flash on a computer screen, you are quite likely to name the word nurse more quickly
following the related word doctor, than some unrelated word (e.g., rock). A spreading
activation network provides a straightforward explanation for the fact that semantic
priming effects are so common. We begin by assuming the representation for a word
contains conceptual knowledge regarding the meaning of the word, in addition to
spelling and pronunciation information. When the first word is presented and identified,
the underlying concept node is activated above threshold and the word is named.
Spreading activation also automatically spreads to related concepts. If the next word is
associatively or semantically related, there is a good chance that the concept node was
partially activated and this partial activation facilitates (or primes the pump of word
recognition) naming of the second word.
Spreading activation network models also explain the false recognition effect. The
false recognition effect is best considered by a concrete example.
False recall and recognition demonstration. Dr. Timothy Bender at Missouri State
University has posted a version of the false memory task explained below. Before
reading further, the reader is encouraged to go to the Web page with the demo, click on
and run the False Recall and False Recognition demo.
207
Here is a list of study items: sugar, sour, bitter, candy, tooth, taste, nice,
chocolate, cake, eat pie, honey, soda.
Now, without looking back at the study list, was the word taste candy on
the list? How about the word sweet?
Researchers (e.g., Roediger & McDermott, 1995) have found that participants will
often incorrectly recognize strongly related items that were not on the study list (e.g.,
sweet) at about the same rate as they will correctly recognize items that were on the list
(e.g., candy). This strong false recognition effect is predicted by spreading activation
network models. The idea is the strongly associated item not on the study list (e.g.,
sweet) is partially activated during study of the related items on the list and when this
item is later presented during the recognition memory test, the partial activation of the
concept node for that word is mistaken for a recent presentation of the item.
Spreading activation network models are limited in how they can represent complex
factual knowledge of the type that can be represented in a verbal sentence. Some
models allow for different types of links between concepts. For example, the concepts
dog and animal might be connected with an IS-A link to represent the fact that a dog is
an animal. Or we might use a HAS link to represent the fact that a dog has fur.
However, such modeling schemes have difficulty representing a complex pattern of
factual knowledge. To deal with this problem, researchers use a type of network called
a propositional semantic network that combines properties of networks and
propositions (see the Knowledge Representation chapter for more on propositions). A
proposition is the smallest factual statement that has a truth-value. The concepts dog
and animal do not have an associated truth-value, they are not true or false in and of
themselves, but the propositional statement a dog is an animal can be seen to be either
true or false.
“Propositional Semantic Network”
In a propositional semantic network, a proposition is represented as a node that is
linked to concepts. Perhaps the simplest proposition is one that contains an agent that
performs some action, a relation that defines the action, and an object to which the
action is directed. Consider the proposition Lassie is a dog, depicted in Figure 13a. The
proposition is represented as a node with the agent Lassie, the object dog, and the
208
relationship IS-A. The true value of the propositional semantic network is its ability to
handle complex inter-relationships between propositions. Figure 13b presents a sample
network representing a common plot from the classic television show that followed the
adventures of Lassie the dog and her owner Timmy. The network connects inter-related
propositions that point to the same concept. For example, in Figure 13b, the
propositions Lassie finds Timmy and Lassie runs home are connected by both sharing
Lassie as the agent. The network as a whole represents the plotline where Timmy falls
into a mineshaft and Lassie finds him and runs home to “tell” his parents resulting in
Lassie becoming a hero. With the addition of some additional forms for the proposition
nodes, a propositional semantic network can be constructed to capture the meaning of
just about any ideas that can be expressed verbally as a sentence.
Exactly how conceptual knowledge is stored in the brain is controversial. Patients
with damage to lateral prefrontal cortex often have trouble retrieving words and
semantic information, suggesting a general role for this region in retrieval from semantic
memory (Martin & Chao, 2001). Patients with damage to the temporal lobes often have
trouble with object identification and categorization, and in answering questions about
properties and categories of objects, suggesting that this region is important in
representing object-specific information. Beyond these generalities lie many different
theoretical proposals and conflicting empirical evidence. However, a recent string of
functional brain imaging studies can explain a lot of the evidence. What is emerging is
evidence supporting the view that a wide range of
cortical
systems
involved
in
modality-specific
representation of objects and events during perception
are reactivated when we think about categories. For
example, Chao and Martin (2000) had participants view
pictures of tools, places, animals, and faces and found
that tools differentially activated motor planning areas
associated with grasping, and also motor-visual
integration areas (see Figure 14). Subsequent studies
(Simmons et al., 2005) have also found that tools also
activate visual shape processing areas (ventral occipitotemporal cortex), and motion
processing areas that represent the resultant motion when the tool is used (middle
temporal gyrus). Simmons et al. (2005) compared viewing of pictures of food to pictures
of locations and found food-selective activation in gustatory processing areas (right
insula/operculum and the left orbitofrontal cortex), as well as visual shape processing
areas. Taken together, these, and many other
recent, findings suggest that conceptual knowledge
is widely distributed and retrieval involves
reactivation of many modality-specific areas
responsible for perception of objects and events.
From an evolutionary perspective, it may be that
humans have recruited sensorimotor processing
areas for representation of conceptual knowledge,
grounding such knowledge in sensorimotor
experience with the world.
209
Figure 9. Long-term memory systems and associated neural areas.
See text for explanation. Nondeclarative memory systems associated
with classical conditioning and sensory habituation are excluded as
they are out of the scope of the present chapter.
210
(a) Encoding
(b) Retrieval
Hippocampus
Hippocampus
Hippocampus
Binding
Figure 10. The role of the hippocampus as the gateway to episodic
memory. (a) During encoding a pattern of cortical areas are activated
during perceptual processing of an event, this results in a pattern of
activation in the hippocampus, a binding process packages the pattern
of activation in the hippocampus into a functional unit. Understanding
the binding problem is a central focus of memory systems research. (b)
Retrieval works in reverse with volitional retrieval processes, or a cue
from the environment, leading to reactivation of the bound episodic
pattern in the hippocampus, which acts as a key to reactivate the
cortical areas originally involved in perceiving the event that now store
the representations of the event.
211
Figure 11. Participants studied a set of 20 pictures and 20 sounds
across 2 days. They were scanned using fMRI during perceptual
presentation, and during a final recall test on the 3rd day. Functional
activations are displayed against a group structural scan (horizontal
sections at various levels). Left panels (a, c and e) depict areas of
differential activity for pictures (green in c) and for sounds (orange in e)
during perceptual processing. Right panels (b, d and f) depict
differential activations for pictures (green in d) and sounds (orange in f)
during retrieval. Black arrows point to ventral temporal lobe (fusiform
gyrus), and also areas of occipital and parietal cortex (d) associated
with retrieval of visual items. Black arrow points to superior temporal
lobe associated with retrieval of auditory items (f). Retrieval activates a
subset of the cortex active during perceptual processing. From Wheeler
et al., 2000.
212
Figure 12. An example of a portion of a hypothetical spreading
activation semantic network that connects semantically and
associatively associated concepts. Concepts that are semantically
interrelated are presented in a common color. Associative connections,
e.g. red and fire truck, based on co-occurrence of the concepts, use
concepts of differing colors. The distance between concepts indicates
strength of relationship. Processing a concept increases the activation
level of the concept. Activation is proposed to spread along connections
to other concepts.
213
(a)
IS-A
Relation
Lassie
Agent
Dog
Object
(b)
Home
Agent
Relation
Lassie
Object
Agent
Relation
Object
RUNS
IS-A
FINDS
Timmy
Agent
Relation
Agent
Relation
Object
FALLS INTO
Object
Hero
Mine Shaft
Figure 13. An example of a simple propositional semantic network
using agent-relation-object proposition nodes to represent related
factual statements. (a) Lassie is a dog, (b) Timmy fell into a mineshaft,
Lassie finds him, Lassie runs home, and Lassie is a hero.
214
Figure 14. Participants viewed pictures of objects from the categories
of tools, places, animals, and faces. Differential tool-related activity was
observed in a grasp planning area in left ventral premotor cortex, and a
motion integration area in left posterior parietal cortex (colored pixels
superimposed over transverse MRI structural sections). Charts on left
depict percent MRI signal change as a function of item category in the 2
tool-selective active regions. From Chao, L., & Martin, A. (2000).
NeuroImage, 12, 478–484.
B. Nondeclarative (Implicit) Memory
Nondeclarative memory systems operate outside of awareness. We are not aware of
the influence of nondeclarative representations on our behavior, and we cannot
consciously inspect the contents of these systems. Nondeclarative systems are
qualitatively distinct from the declarative systems of episodic and semantic memory that
we have discussed thus far. They support motor skill learning, behavioral habits, and a
perceptual memory system that facilitates repeated perceptual processing of an object.
There are also additional systems that support classical conditioning and nonassociative sensory learning that we shall not cover here as they are outside the scope
of interest for a memory chapter from a cognitive science perspective.
“Nondeclarative Memory Systems”
215
Priming effects in nondeclarative (implicit) memory.
Dr. Timothy Bender at Missouri State University has posted
demonstration versions of implicit priming tasks online. The
reader is encouraged to go to the Web page with these
demos and to click on and run the (a) Implicit Memory
Priming and Word Stems, (b) the Implicit Memory Priming
and Anagrams, and (c) the Implicit Memory and Word
Fragments demos.
Perceptual Priming. Perceptual priming is a
nondeclarative memory effect that demonstrates the
important distinction between nondeclarative and declarative
memory systems (Baddeley et al., 2009; Neath & Surprenant, 2003; Smith & Kosslyn,
2007). It allows our perceptual system to be unconsciously influenced by our previous
experiences by directly facilitating identification of objects and events that repeat or are
structurally similar to prior stimuli. Perceptual priming depends on the amount of
perceptual overlap, within a particular modality, between successive perceptual events.
Prior visual presentation of a word will prime (facilitate) processing of the same word
repeated in visual form, but does not do
much for a spoken presentation due to low
perceptual overlap across visual and auditory
presentations (e.g., Jacoby & Dallas, 1981).
One task used to measure perceptual
priming is the perceptual identification task
where participants view words on a study list,
then view extremely briefly flashed test words
presented so briefly that they can only
identify a few of the test words. However,
when a briefly flashed test word was
previously studied on the study list, the
probability of correct identification is boosted.
This facilitation effect is a form of perceptual
priming, and participants are typically unaware that it is happening. Somehow neural
representations of the prior experience with the same written words are available to
influence current perceptual processing of the same visual shape. Another typical task
is the word fragment completion task. During this procedure participants read some
words. They are often given some meaningless task to do with the words, such as
decide as quickly as they can if the word is an animal or a tool. They will then be given a
distractor task, followed by the word fragment completion task. By ordering the tasks
this way participants are typically unaware of any connection whatsoever between the
first and the last tasks. Consider the situation where the word horse was on the previous
word list for half the participants but not for the other half. Then imagine that participants
are to fill in the blanks with the first words that come into mind: H _ _ _ _. Perceptual
priming is the observed boost in the percent completion of the word with horse for the
group that was presented with horse on the earlier word list, as opposed to the percent
for those without this prior perceptual experience.
216
“Perceptual Priming”
Amnesic patients with damage to the hippocampus and associated medial temporal
lobes, e.g., H.M. (discussed in the Introduction to the present chapter) have impaired
declarative memory such that they have trouble encoding new episodic and semantic
representations. But, they have relatively preserved nondeclarative memory
performance such that they produce perceptual priming effects on the perceptual
identification and word stem completion tasks in the normal range. Amnesics such as
H.M. also perform in the normal range of skill learning tasks. Perceptual priming is
thought to be directly supported by most areas of the neocortex that perform perceptual
processing.
Procedural (Skill) Memory. Procedural memory
refers to representations of the motor movements
needed for skilled performance in a task. It also
refers to learned behavioral patterns (habits).
Humans have a remarkable ability to become experts
at a wide variety of motor skills such as golf, typing,
or even walking and talking at the same time.
Laboratory skill learning tasks typically require a
continuous movement sequence that can be
measured for accuracy. For example, using a joystick
to move a mouse on a computer screen and to keep
the mouse on top of a moving dot. Learning occurs
as the total amount of measured error declines with practice. Another example task
would be the serial reaction time task. Imagine that you have 4 lights in front of you,
when each light is lit up you are to press the associated button. Lights are lit in quick
succession and you must struggle to keep up. The sequence of lights is long and
appears to be randomly ordered. Unbeknownst to you there is a hidden sequence of 8
lights, 13412312, that repeats periodically. Typical skill learning for the sequence would
result in improved speed of performance of the hidden embedded brief sequence with
repetition. Medial temporal lobe amnesics will typically perform in the normal range in
terms of a similar improvement with practice on hidden sequences (Gazzaniga et al.,
2009). Another related nondeclarative memory effect is the stimulus-response habits
(also referred to as operant conditioning) that reflect gradual development of motor
movement sequences triggered by specific stimulus events (e.g., catching a ball that is
unexpectedly thrown at you). Skill learning and habit formation are thought to depend
on the function of the basal ganglia. It is worth noting that some skills also involve the
cerebellum, and early on in skill learning motor control processes supported by frontal
cortex are also important.
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“Procedural (Skill) Memory”
VI. Process Perspective
The process perspective on human memory focuses on the processes of encoding
and retrieval, and how these processes depend on the information being encoded, the
type of retrieval task, and the match between processes at time of encoding and
retrieval.
A. Recollection & Familiarity
Imagine that a friend is curious about a party you went to last night that she or he
missed. Your friend asks if you met anyone new, and after a moment you tell them
about the events surrounding meeting and talking with a new person. You tell your
friend what the person looked like, was wearing, what their voice sounded like, and
what they said to you. In doing so you are exhibiting a sort of memory retrieval process
referred to as recollection, an effortful search for the consciously available aspects of
episodic long-term memory. The memory task you faced was a form of recall. You were
asked about a particular party, and you performed a strategic search for all information
in event memory related to this party.
Now imagine that instead your friend shows you a picture of a person on their cell
phone taken at some other event and asks you if this person was also at last night’s
party. Again you perform a conscious recollection of events from the night before. You
do not retrieve any explicit recollection of seeing the person in the picture, but you have
a strong feeling of familiarity when you look at the person’s face. This feeling is so
strong that you feel that you must have seen the person at the party and you tell your
friend that you believe the person pictured was at the party. However, just then you
realize that you saw the person in the picture in line at the local coffee shop yesterday.
You now have some doubt as to whether this person was at last night’s party.
Familiarity is a retrieval process that is sensitive to having recently been perceptually
exposed to an item, but the aspects of the memory context are not accessible by the
familiarity process, leading to the possibility of bias in recognition memory (Neath &
Surprenant, 2003).
This second memory task was a form of recognition memory, where you are
presented with an item and must indicate if it was part of a studied set of items or not.
Many researchers have proposed that recognition depends on 2 types of retrieval
processes, recollection and familiarity.
218
In a classic study, Brown et al. (1977) had participants view 10 individuals in-person.
Participants were told that each of these 10 individuals had just committed a crime.
After a 90-minute delay, participants viewed 15 mug shots and identified those they
thought were in the previous criminal group. Five of the mug shots were of individuals in
the criminal group viewed earlier, and 10 were new innocent individuals.
After a week delay, participants then viewed lineups of individuals. Most of the
individuals were new, i.e., not included in the criminal group, or the innocent mug shot
group. During this lineup participants correctly identified 65% of the criminals who had
been in the mug shots as being criminals. However, they also identified 20% of the
innocent mug shot individuals as being criminals they had seen in-person prior to the
mug shots. But only 8% of brand new individuals only appearing in the line-up were
identified as criminals. This is an example of the strength of bias that familiarity can
have on recognition memory. The line-up is a recognition task, and presumably
innocent individuals from the mug shot seemed familiar enough at time of line-up to be
falsely recognized as being in criminal group. One reason for this may be that familiarity
is not sensitive to context. That is, familiarity processes are sensitive to having seen the
individual recently, but the source information of exactly in which context, criminal group
or mug shots, is not accessible by the familiarity processes.
One of the great successes of cognitive science in terms of influencing public policy
was the U.S. Justice Department’s changing of the guidelines for appropriate in-person
and mug shot line-up procedures in accordance with many of the recommendations of
cognitive science researchers studying recognition accuracy and bias in eyewitnesses
(see Wells et al., 2000, for a discussion of this successful effort on the part of cognitive
scientists). Much of the procedural changes have to do with the fact that recognition
memory depends on the 2 retrieval processes of recollection and familiarity.
B. Transfer Appropriate Processing
Another example of the process approach to the study of human memory is the idea
of transfer appropriate processing, a theory that encoding processes are most
effective when matched to the type of processing at time of retrieval (Neath &
Surprenant, 2003). In a classic study Morris et al. (1977) had participants judge whether
a test word fit meaningfully into a sentence (e.g., the train had a silver engine), or
whether the test word rhymed with another word (e.g., eagle rhymes with legal).
Participants were then given a surprise memory test. They were given either a standard
recognition test (e.g., was train presented earlier?), or a rhyme recognition test (e.g.,
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was there a word that rhymed with beagle? Correct answer, “yes”: eagle). The
interesting result was that correct memory performance was 20-25% greater when the
encoding (rhyme versus identification) task and the retrieval task (rhyme versus
identification) matched.
VII. Connectionist Models
Learn More About Connectionist Models Online
Connectionist models are network models that
attempt to model cognitive processes using
interconnected networks of simplified neuron-like
computational units (Dawson, 2005). The units
(also called nodes) send and receive signals
analogous to the neural communication in the
brain. Inputs model the post-synaptic potentials
(PSPs, see the Cognitive Neuroscience chapter).
The strength of an input depends on the strength
of the output signal from the sending unit
combined multiplicatively with the strength of the
connection between the units (called a
connection weight). Connection weights can be positive to model an excitatory PSP in
a real neuron (i.e., makes the unit more likely to send an output signal), or negative to
model an inhibitory PSP (i.e., makes the unit less likely to send an output signal). The
weights vary in magnitude (usually between -1 and +1) to model synaptic plasticity in
real neurons where learning due to experience can result in a strengthening or
weakening of synapses between neurons. Figure 15 presents an example neuron called
a threshold logic unit (TLU, also called a McCulloch-Pitts unit after early researchers
who proposed such units in the 1940’s). On the left of Figure 15 are the inputs, meant to
model dendrites in neurons, the inputs are summed in the middle, and on the right is the
threshold portion of the unit. If the sum of the weighted inputs, called the activation
level of the unit, equals or exceeds the threshold setting (T in diagram), an output signal
is sent. For the TLU the output is a +1 if the threshold is met by the activation level, and
0 otherwise.
Operation of a TLU. Example inputs and outputs of an example TLU with 2 input
and 1 output connection(s) is presented in Figure 16. In panel A only one input signal is
received, but it is weighted at +1 (excitatory connection) and the unit’s activation level
reaches threshold and an output signal is sent. By contrast, panel B depicts what
220
happens when the other input connection, with an inhibitory weight (-1), becomes
active. The additional weighted inhibitory input drives down the activation level of the
unit so that threshold is no longer met, and the output is withheld (output signal of 0).
Learn More About TLUs (also called McCulloch-Pitts units) Online
Simple Associative Memory Network. We can build a simple memory system
using TLUs of the type depicted in Figure 16. Figure 17 depicts a simple pattern
associator that consists of 2 layers of TLUs. The cue layer accepts input signals and
sends a pattern of signals to the output layer, which, in turn sends a pattern of output
signals. Units producing an output (+1 output) are depicted as filled circles, and units
that are not active enough to meet the threshold for sending a signal (i.e., an off neuron
with an output of 0) are depicted as open circles. By adjusting the weights of the
connections between the cue layer and the output layer, the network learns to associate
specific cue patterns with specific output patterns. The system acts as an associative
memory that takes a memory cue or hint and retrieves an associated pattern. For
example, we could define the pattern of on and off units in the cue layer depicted in
Figure 17 as representing the concept SALT, and we could define the output pattern of
on and off units depicted in Figure 17 as representing the concept PEPPER. The
pattern associator could then be seen to have stored the association between SALT
and PEPPER in the pattern of weights of the connections between cue and output
layers. Every time the cue pattern representing SALT is presented, the output
representation of PEPPER will be output.
“Simple Associative Memory
Network”
Learning Algorithms. One of
the great strengths of connectionist
modeling is the connection weights
that determine the memory storage
of the network do not have to be
adjusted by hand. Algorithms have
been devised that allow the network
to learn via incremental adjustments
to the connection weights as the
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network is exposed to a set of stimulus input patterns. One way to classify learning
algorithms is the distinction between supervised and unsupervised learning algorithms.
Supervised learning algorithms that work during a special learning phase where the
network is taken offline and the operation of the network is in a learning mode only. This
allows repeated cycling through the set of training input patterns, evaluation of the
deviation of the observed output pattern in comparison to the correct output pattern, and
the output error is used to incrementally change the connection weights to minimize the
error for that input pattern the next time it is presented. With repeated cycling through
the input and output patterns, the connection weight eventually approach an optimal
setting to produce as many of the desired output patterns for each input pattern. In
some cases, the network architecture may not allow complete learning of the inputoutput pairs and the network will need to be revised. An unsupervised learning
algorithm, works online. Connection weights are incrementally changed as the network
experiences as set of inputs. However, the network is not explicitly given a set of correct
output patterns to produce for a given set of input patterns. Instead, the system must
learn the informational structure of a set of inputs and learn to respond differentially to
inputs of different types in much the same way that a human child learns to classify
dogs and cats into different categories by direct experience with the world.
Distributed Representation. Note that by defining a particular pattern of on and off
units to stand in for a concept, this pattern is acting as a symbol in much the same way
the word salt stands in for the concept SALT. Use of a pattern of activity across a set of
units to represent information is known as a distributed representation scheme. A
local representation scheme could be used by
having each individual unit represent a concept as
in the semantic network models discussed in the
semantic memory subsection of the present
chapter. For example, we could have defined the
first unit on the left of each layer of the network
depicted in Figure 17 as representing the concept
SALT, and the second unit as representing the
concept PEPPER. We then would have taught the
network to turn on only the second-from-left output
unit representing PEPPER when the cue layer has only the left unit representing SALT
turned on via input. This would be a local representation scheme. Each unit is the
equivalent of a symbolic representation of a concept.
Subsymbolic Representation. One of the strengths of the connectionist approach
to modeling is the flexibility that distributed representation schemes allow. Distributed
representations are often defined as patterns of signaling across a layer of units. We
can be restrictive in our modeling of cognition and require that for a particular concept,
e.g., SALT, to be said to be active in the system, the exact pattern of on and off units
must be observed. However, it is often the case that learning algorithms that attempt to
find the optimal pattern of connection weights that will maximize learning result in input
patterns that lead to output patterns that only partially correct. For example, imagine
that the network depicted in Figure 17 learns to pair the input pattern for the concept
222
SALT with an output pattern that is partially correct for PEPPER in that only 3 of the 4
output units have the correct on-off output signaling values for the concept PEPPER.
Use of distributed representation schemes that allow patterns of activity across units
that partially match the defined pattern for representing information is known as a
subsymbolic representation scheme. A symbolic representation is traditionally an allor-none proposition. For example the word salt represents the concept SALT, but the
partial match sald, is a non-words that does symbolically represent the concept SALT
even though it partially matches the symbol salt. From this analysis we see that written
words are all-or-none symbols representing conceptual information.
However, perhaps the same exact pattern of neurons in the brain do not activate
each and every time we see the word salt. Perhaps the brain allows patterns of neural
activity that are close partial matches to some prototypical activity to represent the
concept SALT. In other words, perhaps the brain has a subsymbolic level of
representation based on groups of patterns of neural activation. There has been much
argument in cognitive science regarding whether subsymbolic distributed representation
in connectionist models is truly different enough from the traditional symbolic
representations to result in a truly distinct approach to cognitive science. Regardless of
where one stands on this issue, subsymbolic distributed representation schemes that
allow partial pattern matches across sets of units to have a representational role play an
important part in connectionist models of cognition.
Weakness of Simple Pattern Associator. Simple pattern associators such as the
one depicted in Figure 17 are quite limited in their ability to learn sets of associated
patterns. There are certain types of associations that just cannot be learned by such a
simple network. That is, optimal sets of connection weights do not exist for many sets of
input patterns the modeler would like the network to learn. Because of these limitations,
a more powerful network architecture is required.
Connectionist Units as Logic Gates. Traditional symbolic computation is based on
the idea of formal operations on symbolic representations. The ability of a cognitive
system to implement the formal operations of logic is an important aspect of this
traditional form of computation. One question of interest regarding the individual TLUs is
how computationally powerful they are in the traditional sense of computation. Of critical
interest is the ability of the TLU to produce transformations of the input signals to output
signals that are equivalent to the operations of formal logic. For example, it is easy to
create a TLU that performs the formal operation of OR on 2 inputs. The logical
operation of OR, with 2 inputs, returns a value of True if either of the inputs is True, and
also if both inputs are True, but returns a value of False if both inputs are False. To
create a 2-input TLU equivalent to OR, we simple define 0 as indicating a False input or
output, and 1 indicating a True input or output. We quickly find that by setting the
weights of both the input connections to +1, and setting the threshold of the single
output unit to 1, the result is an OR-equivalent TLU. Table 1 presents the truth table for
this TLU. As depicted in Table 1, the output is +1 except when both the inputs are 0,
and then the output is 0. This pattern is equivalent to the truth table for the logical
operation of OR.
223
This analysis quickly runs into a problem once we get to the logical operation of
XOR, exclusive OR, where the output should be True if either of the inputs is True, but
not if both are inputs are True. If both inputs are True, or if both inputs are False, the
output should be False for the XOR operation. It turns out our simple TLU is not
powerful enough to implement this critical logical operation. Historically, when
researchers realized that networks that have the basic structure depicted in Figure 17,
with just 2 layers of units, could not learn the structure of many sets of input patterns,
and could not implement the XOR operation of logic, they realized that the basic
network architecture for connectionist models of cognition had to change. The result
was the 3-layer feedforward architecture of Figure 18.
The 3-Layer Feedforward
Network and Beyond. As
depicted in Figure the standard
network architecture that is
powerful enough to learn the
structure of most sets of input
patterns, can solve the XOR
problem, and for which a
powerful learning algorithm has
been discovered is the 3-layer
feedforward network depicted in
Figure 18. Signals flow only from
lower layers to upper layers,
hence the term feedforward, and
there are no lateral connections
between units within the same layer, and there are no feedback connections of a higher
layer to a lower layer. This simplicity of design allows for application of a powerful
learning algorithm for adjusting the connection weights. Common enhancements are to
add lateral and feedback connections to the architecture. Another common
enhancement is to upgrade the TLU so that it produces a continuous graded output
between a maximum and a minimum. This models the idea of frequency coding in
neurons (see the Cognitive Neuroscience chapter) where neurons send different signals
to each other by varying the frequency or rate of firing action potentials between a
minimum baseline rate of firing and a maximum physically possible. Added
computational flexibility is gained by allowing units to have a continuous graded output
rather than the binary 0 or +1 of the TLU.
224
Σ
T
Figure 15. A standard threshold logic unit (TLU). The unit accepts 2
input signals on the inputs on the left. Each input is weighted (often -1
to +1), and the activation level of the unit is determined by the weighted
sum (i.e., each input 0 or +1 multiplied by the weight of its connection)
as symbolized by the summation sign in the center. The activation level
is compared to the threshold (T), and if the activation level meets or
exceeds the threshold, a signal (+1) is sent out to all other units this unit
is connected to.
A
0
-1
+1
+1
Σ = (-1⋅ 0) +
(1⋅1) = 1
1
+1
Σ = (-1⋅ 1) +
(1⋅1) = 0
1
0
B
+1
-1
+1
+1
Figure 16. Example inputs and outputs for an example TLU with 2 input
and 1 output connection(s). Panel A: Only one of the input connections
has an input signal (+1), the other input connection has a non-input
signal (0). Each signal is multiplied by the weight of its connection, and
the products are summed (middle of panel A). The resultant activation
level (+1) meets the threshold (+1) and an output signal is sent on the
single output connection. Panel B: Both inputs signals are now at +1,
and the inhibitory weight (-1) now comes into play and drives the
activation level (weighted sum of inputs) down to 0, which, does not
meet the threshold, and no signal is sent on the output connection.
225
Output Layer
Cue Layer
Inputs
Figure 17. A simple pattern associator built from TLUs. Darkened units
indicate units active above threshold that are currently producing an
output of +1, open units have an output of 0. The associator acts as a
cued memory system by applying an input pattern to the bottom (cue)
layer, and signals are then sent to activate a previously-learned
associated pattern on the upper (output) layer. Weights of connections
determine the learned associations between cues and outputs. Note
that weights of connections and thresholds of units are not depicted to
keep figure simple.
Figure 18. The improved network architecture that results from adding
a middle layer to the 2-layer simple pattern associator depicted in
Figure 17. All signals flow from bottom to top along unidirectional output
connections. Inputs are applied to the input layer and signals are sent
to the middle layers, referred to as the hidden layer, and from there, to
the output layer. Note that there are now 2 sets of connection weights,
for the middle and output layers, that determine learning. This
architecture is referred to as a 3-layer feedforward only network
because the only connections are from lower layers to higher layers in
the network. There are no connections between units within a layer and
no feedback connections to lower layers. Addition of lateral and
feedback connections is commonly used to create a more complex
network.
226
Table 1
Truth Table for an OR-equivalent 2-input TLU with Weights of +1 on Both Inputs and a
Threshold of +1.
Input 1
0
0
1
1
Input 2
0
1
0
1
Activation
0
1
1
2
Output
0
1
1
1
References
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Baddeley, A., Eysenck, M.W., & Anderson, M.A. (2009). Memory. Psychology Press.
Braver, T.S., Cohen, J.D., & Barch, D.M. (2002). The role of the prefrontal cortex in
normal and disordered cognitive control: A cognitive neuroscience perspective. In
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Brown, E., Deffenbacher, K., & Sturgill, W. (1977). Memory for Faces and the
Circumstances of Encounter. Journal of Applied Psychology, 62, 311-318.
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Dawson, M.R.W. (2005). Connectionism: A hands on approach. Oxford, UK: Blackwell.
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consciousness: basic evidence and a workspace framework. Cognition 79, 1–37.
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Further Reading
Recommended Memory Textbooks
Baddeley, A., Eysenck, M.W., & Anderson, M.A. (2009). Memory.
Psychology Press.
Neath, I., & Surprenant, A.M. (2003). Human memory, 2nd Ed., Belmont,
CA: Wadsworth.
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Scientists (including a Nobel laureate) Write About Memory
Kandel, E. (2006). The search for memory: The emergence of a new
science of mind.
Norton.
Schacter, D.L. (2001). The seven sins of memory: How the mind forgets
and remembers. Houghton Mifflin Harcourt.
Squire, L., & Kandel, E. (2008). Memory: From mind to molecules. Roberts
and Company Publishers.
Recommended Memory Review Articles
Baddeley, A. (2003). Working memory: Looking back and looking forward.
Nature Reviews Neuroscience, 4, 829-839.
Squire, L.R. (2004). Memory systems of the brain: A brief history and
current perspective. Neurobiology of Learning and Memory, 82, 171-177.
General Introduction to Connectionist Modeling
Dawson, M.R.W. (2005). Connectionism: A hands on approach. Oxford,
UK: Blackwell.
Connectionist Modeling Applied to Semantic Memory
Rogers, T.T., & McClelland, J.S. (2004). Semantic cognition: A parallel
distributed processing approach. Cambridge, MA: MIT Press.
Glossary
3-Layer Feedforward Network
A 3-layer network formed by taking the simple pattern associator and adding a third
hidden layer of units between the input and output layers. A powerful architecture
that can learn a wide range of pattern associations.
Activation Level
A value for a connectionist network unit that is based on the sum of its weighted
inputs and that is compared to a threshold to determine if the unit will produce an
output signal.
Amnesia
Loss of memory function.
Binding Problem
The problem of binding distributed representations in the brain into a packaged
memory representation.
Central Executive
The cognitive control module in Baddeley’s WM theory.
229
Chunking (STM)
A STM memory phenomena where verbal items (e.g., the letter sequence I-B-M)
are combined into a single representation chunk (e.g., IBM).
Connection Weight
The strength of a connection between 2 units in a connectionist network.
Connectionist Model
A network model that uses neural-like interconnected units that send and receive
signals to model cognitive processes.
Consolidation
A process of making memory representations increasingly stable. Thought to take
place primarily during sleep.
Declarative (Explicit) Memory
A memory system that allows conscious access to its contents via volitional
(explicit) retrieval processes.
Digit Span Test
A common STM span task that involves immediate ordered recall of randomly
presented digits.
Encoding, Storage, and Retrieval
The 3 cognitive processes that define a memory system. A memory system
acquires information and forms an appropriate code (encoding), the code is
extended in time (storage), and the code must be accessible to retrieval processes.
Episodic Memory
An explicit (declarative) memory system for events.
False Recognition
A memory effect where a list of related words is presenting that is missing a word
that is related to all of the words on the list. Later, during a word recognition test,
participants will often indicate the missing word was presented.
Familiarity versus Recollection
An idea from the process approach to memory that retrieval during recognition
memory tasks for events involves 2 processes. Recollection is similar to the
retrieval used for recall tasks, an effortful strategic search results in retrieval of
details of the event, e.g., what a person’s voice sounded like when you met them.
Familiarity is a feeling of having recently seen the item on the recognition memory
test that has become separated from information regarding the source of the
familiarity, e.g., thinking you have met a person before when you have seen them at
the local coffee shop but not met them.
230
Free Recall
A type of memory test where no clues (called memory cues) regarding the correct
answer are provided.
Immediate Memory
A generic term for a memory system that allows people to immediately repeat
information back.
Local versus Distributed Representation
Connectionist models that represent words or concepts using single units are said
to use a local representation scheme, and models that use a pattern of activation
across a set of units to represent a word or concept are said to use a distributed
representation scheme.
Long-term Memory (LTM)
A virtually unlimited capacity system for storage of information on timescales of
minutes to years. Part of the modal memory model.
Modal Memory Model
The 3-store memory model that is a combination of the most common aspects of
the dominant information processing memory theories of the 60’s and 70’s.
Nondeclarative (Implicit) Memory
A memory system that does not allow conscious access to its contents. Retrieval is
only implicitly apparent in its effects on behavior.
Operations Span Test
A common WM span task that involves immediate ordered recall of sequentially
presented words while concurrently solving simple arithmetic problems.
Perceptual Priming System
An implicit (nondeclarative) memory system for maintaining perceptually processed
shapes in a partially active state to facilitate processing the same shape if it repeats.
Perceptual Priming
An implicit (nondeclarative) memory effect where a part or all of a shape is repeated
and identification is facilitated (primed) for the second presentation.
Phonological Loop
The mental workspace module for temporary storage and processing of verbal
information in Baddeley’s WM theory.
Procedural Memory
An implicit (nondeclarative) memory system for motor skills, behavioral habits, and
cognitive skills.
231
Propositional Network
A complex formulation of a semantic network that represents propositions (see
Knowledge Representation chapter) as networks.
Recognition
A type of memory test where correct and incorrect answers are provided and the
correct answers are to be chosen and the incorrect answers avoided.
Retrograde & Anterograde Amnesia
An amnesic syndrome that involves selective loss of memory for information
learned before the onset of amnesia (retrograde, backward looking), or after the
onset of the amnesia (anterograde, forward looking).
Semantic Memory
An explicit (declarative) memory system for facts.
Semantic Network
A network model of semantic memory that represents concepts as nodes or units,
and relationships between concepts as connections.
Semantic Priming
A memory testing effect where a word is processed faster if presented immediately
following a semantically (e.g., dog-cat) or associatively (e.g., salt-pepper) related
word. Thought to be due to the operation of automatic spreading activation in
semantic memory.
Sensory Store
Part of the modal memory model that extends sensory information for brief intervals.
Serial Position Curve
A curve obtained by graphing the percent correct free recall of words from a long
word lists graphed as a function of each word’s position of presentation in the word
list.
Short-term Memory (STM)
A limited capacity system for storing information for several seconds until it is
needed. Part of the modal memory model.
Simple Pattern Associator
A 2-layer network that can associate patterns across its input and output layers.
This architecture is limited in terms of the associations it can learn.
Spreading Activation Theory
The idea that concepts in semantic memory are activated when processed, and that
some activation will spread to related concepts resulting in their partial activation
thus facilitating subsequent processing of related concepts (e.g., semantic priming).
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STM Span Test
A test for measuring the capacity of STM to passively store information.
Subsymbolic Representation
Distributed representation schemes have to deal with how to represent close, but
not perfect, representation patterns across a set of units. The use of similar patterns
in representation is known as subsymbolic representation.
Supervised and Unsupervised Learning Algorithms
Learning algorithms adjust the connection weights to reflect connectionist network
learning. A supervised learning algorithm trains the network using pairs of input
signal and correct/desired output signal patterns. An unsupervised learning
algorithm does not have access to correct/desired output patterns, but rather, trains
the network learn the structure of a set of input signal patterns.
Threshold Logic Unit (TLU)
A simple unit that can only send binary (0, 1) signals, and which typically has input
connection weights limited to the continuous range (-1 to +1).
Threshold
Criteria met by the activation level of a connectionist network unit in order to
produce an output signal.
Transfer Appropriate Processing
An idea from the processing approach to memory that encoding processes work
best when matched to the type of retrieval that will be performed later.
Unit
A neural-like unit that sums input signals to determine its activation level and sends
signals to other connected units in a connectionist network if its activation level
meets a threshold.
Visuospatial Scratchpad
The mental workspace module for temporary storage and processing of visual and
spatial information in Baddeley’s WM theory.
WM Span Test
A test for measuring the capacity of the WM system to store information while
performing concurrent cognitive processing.
Working Memory (WM)
A limited capacity system that acts as a mental workspace for both temporary
storage and concurrent cognitive processing.
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