COMMEMORATIVE LECTURE
Neural mechanisms of cognitive memory
Yasushi Miyashita
Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan(Received for publication on March 5, 2004)

Key words: memory, vision, cognition, cerebral cortex, functional imaging

engrams are activated, highlighting two different retrieval processes, ‘active’ and ‘automatic’. Finally, I willshow you our recent attempts to clarify the neural basisof a metamemory system, which supervises the retrieval
process and exerts cognitive control.

It is my great honor to be here for the award of the2003 Keio Medical Science Prize. I express my gratitude
to Keio University and Dr. Mitsunada Sakaguchi.
Knowledge or experiences are voluntarily recalledfrom memory by reactivation of their neural representations in the cerebral association cortex. Fourquestions are central in understanding this process: (1)Where in the brain is the mnemonic representation of
objects localized? And how is it organized? (2) How is
the representation created and what is the morphological and molecular basis of neural circuit reorganization?
(3) Which neural circuits enable the reactivation of therepresentation in memory retrieval? (4) What is theneural basis of cognitive control in memory retrieval
and in meta-memory judgment?
Clinical studies in humans have suggested that longterm declarative memory is stored in the neocorticalassociation area that is also engaged in sensory perception (for review, see Miyashita, 1993,1 Miyashita &Hayashi, 20002). When electrical stimulation was applied via cortical surface electrodes placed on the temporal lobe during neurosurgery under local anesthesia,
epileptic patients sometimes reported recollection of
past perceptual experiences (Penﬁeld & Perot, 1963),3
suggesting that artiﬁcial electrical input to the putative memory storehouse might reactivate the ‘‘brain’s
record of auditory and visual experience’’. Recent experimental studies in humans and non-human primates
moved beyond those classical clinical observations andrevealed clearer and more solid views on the issues,particularly regarding the memory system of visualobjects. In this lecture, I will ﬁrst describe how memory
engrams are organized in the cerebral cortex, focusing on the neural representation of semantically linked
symbols/objects. I will then discuss how memory

Representation of Visual Objects: Organizing
Memory Engrams
Neuronal correlates of associative long-term memory
were ﬁrst reported in the monkey inferior temporal(IT) cortex by Miyashita (1988)4 and Sakai and Miyashita (1991).5 Their single-unit recordings identiﬁed
two mnemonic properties of IT neurons: 1) that ITneurons can acquire stimulus selectivity through learning in adulthood; and 2) that their activity can link representations of temporally associated but geometricallyunrelated stimuli. How these memory neurons function
as basic elements of semantic networks, and how semantic networks are created through interaction among
multiple representations in temporal cortical areas, is
now ﬁrmly established (Miyashita and Chang, 1988,6
Miyashita and Hayashi, 20002). In that regard, investigation of the neural basis of semantic networks has
been greatly facilitated by reducing complex associative networks to elementary associative links between
two objects, and then asking what are the neural mechanisms underlying such elementary associative links(Miyashita, 1993).1 The pair-association (PA) memorytask is the best-known neuropsychological test withwhich to tap the memory of such an elementary pairwise associative relation (e.g. WMS-R, Wechsler,
1987).7 In the following section, we discuss how investigations using PA task revealed the neuronal machinery of associative memory in the IT cortex.

Dr. Miyashita is the recipient of the 2003 Keio Medical Science Prize.
Presented at the Keio Medical Science Prize Symposium, December 4, 2003.
Reprint requests to: Dr. Yasushi Miyashita, Department of Physiology, The University of Tokyo School of Medicine, 7-3-1, Hongo, Bunkyo-ku,
Tokyo 113-0033, Japan59

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Miyashita Y: Mechanisms of cognitive memory

Fig. 1 A. Lateral view of a macaque brain. TE is located at the ﬁnal
processing stage of the ventral visual pathway. A36 is thought to be apart of the medial temporal lobe system. V4, visual area 4; TEO, area
TEO. B. Coronal cross section indicated by the vertical line on thelateral view in panel A. The black and gray areas indicate the locations of the recoding sites in TE and A36, respectively. C. Sequence of
events in one trial of the PA task. Fixation points and cue stimuli were
presented at the center of a video monitor. Choice stimuli were presented randomly in two of four positions on the video monitor.

Forward processing of pair-association memory
The IT cortex contains two cytoarchitectonically distinct but mutually interconnected areas: area TE (TE)and area 36 (A36) (Fig. 1A, 1B). TE is a unimodal
neocortex and located at the ﬁnal stage of the ventral
visual pathway, which processes object vision (Miyashita, 1993).1 A36, on the other hand, is a limbic polymodal association area and a component of the medial
temporal lobe memory system, which is involved in theformation of declarative memory (Higuchi and Miyashita, 1996).8 Naya et al. (2003)9 found that association
between the representations of different but semantically linked objects proceeds from TE to A36. To dothis, they trained monkeys to perform the PA memorytask in which meaningless computer-generated pictures were sorted randomly into pairs. We refer to eachmember of a pair as a paired associate. The monkeys
were trained to memorize combinations of paired associates, which could not be predicted otherwise. In eachtrial of the task, a cue stimulus was presented, and themonkey was rewarded when he/she chose the paired
associate of the cue (Fig. 1C). After training, extracellular spike discharges were recorded from single
neurons in TE and A36.
A total of 2368 neurons were recorded from A36(510 neurons) and TE (1858 neurons) in the threemonkeys performing the PA task. Of those, 475 neu-

Fig. 2 Stimulus-selective responses to both paired associates. Data
from a representative A36 neuron. A. Raster displays and PSTHs
in trials where the preferred stimulus ( preferred, thick black) or its
paired associate ( pair, thick gray) served as a cue. PSTH and the trials
were temporally aligned at the cue onset. The black lines indicate
responses to the preferred cue stimulus ( preferred, thick black) or its
paired associate ( pair, thick gray). The thin black line denotes theaveraged responses in the other trials (other). The horizontal gray bar
indicates the cue presentation period. B. Mean discharge rates during
the cue period (60–320 ms from the cue onset) for each cue presentation. Twelve pairs of stimuli are labeled on the abscissa. The open
and ﬁlled bars in pair 1, for example, refer to the responses to stimulus 1 and 10 , respectively. Error bars denote SEM. (Reproduce fromNaya Y, et al: J Neurosci 2003; 23: 2861–2871, Copyright : (2003),
with permission from Society for Neuroscience)rons (85 neurons in A36 and 390 neurons in TE)
showed responses to the cue presentation for at least
one stimulus among the 24 learned stimuli (visually responsive cells). Out of them, 423 neurons (76 neuronsin A36 and 347 neurons in TE) showed signiﬁcant( p < 0:01, ANOVA) stimulus selectivity during the cue
period (60–320 ms from cue onset) (cue-selective cells).
The responses of a representative cue-selective neuronin A36 are shown in Figures 2A and 2B. Note that one
stimulus elicited the strongest response from this neu-

Keio J Med 2004; 53 (2): 59–68

ron during the cue period (Fig. 2A, thick black; Fig.
2B, ﬁlled bar in pair 4). This neuron was also activatedwhen the paired associate of the preferred stimulus waspresented (Fig. 2A, thick gray; Fig. 2B open bar in pair4). In contrast to the robust responses to this stimulus
pair, this neuron responded only negligibly when stimulus from any of the other pairs were presented ascue stimuli (Fig. 2A, thin black, the averaged responses
to the other 22 stimuli; Fig. 2B). This type of neuron,
which is found in the IT cortex and is referred to asa ‘pair-coding neuron’, selectively responds to bothpaired associates (pair-coding response). This property
indicates that memory storage is organized such thatsingle neurons can code both paired associates in the
PA task.
The pair-coding responses in A36 with those in TE
were compared by examining the distributions of the
response amplitude for the pair trials. It was found thatthe distribution were signiﬁcantly shifted toward positive values in both areas (A36, median ¼ 0:27; TE,
median ¼ 0:03; p < 0:001 in both areas, Wilcoxon’s
signed-rank test), with the distribution for the A36
neurons shifted to signiﬁcantly higher values than thatfor the TE neurons ( p < 0:001, Kolmogorov-Smirnov
test). Thus, in addition to the preferred stimulus, neurons in both A36 and TE responded selectively to thepaired associate of the preferred stimulus, and the
response was more prominent in A36 than in TE. In
addition, the percentage of pair-coding neurons among
the cue-selective neurons was signiﬁcantly higher in
A36 (33%) than in TE (4.9%) ( p < 0:001, w 2 test)(Naya et al., 2003).9 This means that although neurons
in both areas acquire stimulus selectivity through associative learning, the effect is engraved more intensely
on the neuronal representation in A36 than in TE. The
dramatic increase in the percentage of pair-coding neurons that one sees by going from TE to A36 indicatesthat the association between representations of paired
associates proceeds forward through this anatomicalhierarchy of the IT cortex.
Circuit reorganization during formation of the pairassociation memory
It has been long hypothesized that memory engramsof declarative knowledge, as exempliﬁed by the emergence of pair-coding neurons, develop with a structural and functional reorganization of neural circuitsin the cerebral association cortices (Miyashita Y, 1993,1
Miyashita & Hayashi, 20002). This reorganization ofneural circuits would be accomplished through a cellular program of gene expression leading to increased
protein synthesis and then to alteration of synaptic
connections. To date, this hypothetical framework has
been primarily investigated in invertebrates and lower

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mammals, in which it is difﬁcult to examine the organization of semantic memory. Still, the hypothesis asapplied to semantic memory has been tested in a seriesof molecular biological studies carried out in monkeys(Tokuyama et al., 2000,10 200211), showing that upregulation of mRNA encoding proteins thought to be
involved in structural reorganization occurred duringformation of the pair-association memory. In this seriesof studies, the RT-PCR mRNA quantitation was combined with three unique experimental strategies. The
ﬁrst strategy entailed the use of split-brain monkeys,
which were prepared by transecting the anterior commissure and the entire extent of the corpus callosum(Hasegawa et al., 1998).12 The fact that there was nointerhemispheric transfer of mnemonic engrams in thispreparation (Hasegawa et al., 1998)12 enabled us tocompare mRNA expression between the left and righthemispheres within individual monkeys (Fig. 3A),
thereby eliminating genetic and cognitive variations between individuals. The second strategy entailed the useof a visual discrimination (VD) task, rather than a notask condition, as the control (Fig. 3A). This enabled the
motivational and attentional states in the two hemispheres, as well as the input of visual stimuli, to be appropriately balanced. The third strategy entailed trainingmonkeys to ﬁrst learn a ‘rule’ or ‘strategy’ component of
the tasks using training stimulus sets, after which a test
stimulus set was introduced for new learning of the declarative components of the task. Before the learning
process with the test stimulus was complete, the animals
were perfused, and expression of mRNA in the brains
was evaluated. This enabled investigation of gene expression during formation of associative memory but
not during formation of procedural memory.
Using the approach described above, it was foundthat expression of mRNA encoding Brain Derived
Neurotrophic Factor (BDNF) was signiﬁcantly higher
in A36 of the PA hemisphere than in the VD hemisphere ( p < 0:05) (Fig. 3B). In the early visual cortex(e.g., V1 or V4), by contrast, expression of BDNF
mRNA did not differ in the two hemispheres (V1,p > 0:60; V4, p > 0:87), indicating that the increased
expression of BDNF mRNA level in A36 did not reﬂect
a difference in the amount of visual input. The RT-PCR
analysis also showed that expression of the mRNA
encoding trkB, a speciﬁc receptor for BDNF, wasslightly increased in A36 of the PA hemisphere, though
the increase did not reach statistical signiﬁcance. The
expression of the mRNA encoding the immediate-earlygene zif 268 was also selectively upregulated in A36
during formation of PA memory (Tokuyama et al.,2002).11 On the other hand, expression of a ‘housekeeping gene’, b-actin, did not differ between the twohemispheres in any of the cortical areas examined.
The spatial distribution of the BDNF mRNA was62

Miyashita Y: Mechanisms of cognitive memory

Fig. 3 A. Visual memory tasks for split-brain monkeys. Stimulus conﬁguration for two visual memory tasks in different visual hemiﬁelds. Each
split-brain monkey was trained to perform both a pair-association (PA) task and a visual discrimination (VD) task. During visual ﬁxation, visual
stimuli for the PA task were presented to one visual hemiﬁeld, while visual stimuli for the VD task were presented in the other hemiﬁeld. Using
this conﬁguration, split-brain monkeys did the PA task with one hemisphere (PA hemisphere) and the VD task with the other hemisphere (VD
hemisphere). B. Intra-animal comparison of levels of expression of BDNF, trkB, and -actin mRNA in the PA and VD hemispheres. In eachcortical area of each monkey, the mRNA expression ratio was calculated by dividing the level mRNA expression in the PA (experimental)
hemisphere by that in the VD (control) hemisphere. The mRNA expression ratios were then averaged across animals and plotted for theindicated ﬁve cortical areas (mean G s.e.m.). Expression of BDNF mRNA was signiﬁcantly higher in A36 of the PA hemisphere than A36 of the
VD hemisphere. An asterisk indicates the signiﬁcant difference between the PA and VD hemispheres ( p < 0:05). (Reproduce from Tokuyama
W, et al: Nat Neurosci 2000; 3: 1134–1142, Copyright : (2000), with permission from Society for Nature Publishing Group)

visualized using in situ hybridization (Fig. 4). Notably,
BDNF mRNA-positive cells accumulated as a ‘‘patchy’’cluster in A36 of the PA hemisphere, but not in thesame area of the VD hemisphere, which suggests that
upregulation of BDNF expression is associated withneurons located within the patches in A36. Thesepatches were most prominent in layers V/VI, but werealso observed in layers II/III (Fig. 4C), extending for atleast 0.4 mm along the anterior–posterior axis. In contrast to the PA hemisphere, the VD hemisphere contained only scattered BDNF mRNA-positive cells inlayers V/VI of A36 (Fig. 4B and D). And when themagnitude of the local increase of BDNF mRNA
expression was estimated by grain-counting analysis(framed areas, Fig. 4A and B), it was found that signiﬁcantly more neurons in the PA hemisphere
expressed detectable levels of BDNF mRNA than in
the VD hemisphere [9:1 G 0:7% in the PA hemispherevs. 3:7 G 0:6% in the VD hemisphere (w 2 ¼ 72:4,
p < 0:001)]. In area 35 of the PA hemisphere, expression of BDNF mRNA also seemed slightly stronger
than in the VD hemisphere, but there were no differences in the patterns of BDNF mRNA expression in
any other regions of the PA and VD hemispheres.
BDNF is thought to mediate activity-dependent
synaptic plasticity, even in mature nervous systems.
Consistent with that idea, its expression is regulated bychanges in neuronal activity. Moreover, since zif 268 encodes a transcription factor, its expression could function
as a trigger for a cascade of gene activation that leads tothe cellular events underlying neuronal reorganization.
Thus, analysis of the formation of PA memory has provided the ﬁrst evidence supporting the hypothesis that
BDNF contributes to the reorganization of neural networks, and that perhaps this reorganization is initiatedby zif 268, which triggers a cascade of gene activation.

Keio J Med 2004; 53 (2): 59–68

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Fig. 4 In situ hybridization of BDNF mRNA. A–D. Distribution of BDNF mRNA in the IT gyrus of the PA (A) and VD (B) hemispheres.
BDNF mRNA accumulated in a patch in A36 of the PA hemisphere (framed area), but not in A36 of the VD hemisphere. The framed areas in(A) and (B) are enlarged in (C) and (D), respectively. BDNF mRNA-positive cells were observed in layers V/VI and in layers II/III of the PA
hemisphere (C); the image of the cell marked by the arrow are deﬁned in (E–G). En, entorhinal cortex; 35, area 35; 36, area 36; TE, area TE; rs,
rhinal sulcus. Arrowheads mark the boundaries between different cortical areas. E–G. BDNF mRNA-positive cells in layers II/III of the PA
hemisphere. The cell marked by an arrow in (C) is enlarged and shown in darkﬁeld (E), bringht ﬁeld (F), and brightﬁeld with epi-illumination(G). Silver grains were concentrated around lightly Nissl-stained neuronal nuclei. Cortical layers of A36 are indicated along margin of (D).
Scale bars, 1 mm (A, B), 250 mm (C, D), 50 mm (E–G). (Reproduce from Tokuyama W, et al: Nat Neurosci 2000; 3: 1134–1142, Copyright :(2000), with permission from Society for Nature Publishing Group)

The location of the focal patch expressing BDNF
approximates the location of aggregates of pair-coding
neurons detected by single-unit recording (referred to asa ‘hotspot’) (Naya et al., 2003,9 Yoshida et al., 200313).
A combined anatomical-physiological analysis recently
showed that structural reorganization does indeed occur at hotspots and that the ﬁber terminals of pictureselective neurons in TE are retracted from out of thehotspot in A36 but remain to project within the hotspot(Yoshida et al., 2003).13 We therefore suggest that
BDNF expression may induce axonal and synaptic
reorganization in the hotspot in A36, and that such reorganization of local networks is detected electrophysiologically as a change in neuronal stimulus selectivity,
typically as the emergence of pair-coding neurons.

active retrieval (Miyashita & Hayashi, 2000,2 Petrides,
200014). In the previous section, we discussed visual
associative memory stored in the IT cortex. It follows
that such long-term memory could then be retrieved
from the IT cortex by either of these two processes. In
this section, we will suggest that whether retrieval is
automatic or active depends on whether the retrieval
signal is created within the network of the IT cortex
or runs from the prefrontal (PF) cortex to the IT cortex. We begin by introducing a study that showed thatthe automatic memory retrieval signal ﬂows backward
through the IT cortex, from A36 to TE.

Activation of Memory Engrams: ‘Active’ vs.
‘Automatic’ Retrieval

The theory of semantic network visualizes a retrievalof an item as activation of a corresponding node at the
network. The neural correlate of such a node-activation
was ﬁrst reported in the pair-association task by Sakai
and Miyashita (1991).5 They found a group of IT neurons that showed an activation related to the retrievalof the paired associates from a cue stimulus. The response is referred to as pair-recall response. Using a

There appears to be two types of memory retrieval
processes, automatic and active. You know that sometimes one needs no effort to recall and at other times
one has to strive toward a successful recall. We refer tothe former as automatic retrieval and to the latter as

Automatic retrieval signal: backward spread of memory
retrieval signal in the inferior temporal cortex

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Miyashita Y: Mechanisms of cognitive memory

Fig. 5 Neuronal activity related to memory retrieval, as shown by a single cell in A36 (A) and in TE (B). For the raster displays [(A) and (B)]and PSTH, responses were temporally aligned at the cue onset in trials where the preferred stimulus (upper panel) or its paired associate (lower
panel) served as a cue. The black lines indicate responses to the preferred cue stimulus (upper panel) or its paired associate (lower panel); thegray lines indicate mean responses to all 24 stimuli. (C and D) Temporal dynamics of averaged PRI(t) for the population of stimulus-selective
neurons (A36, n ¼ 45; TE, n ¼ 69). Mean values of PRI(t) were plotted every 100 ms for A36 (C) and TE (D) neurons (ﬁlled circle, total; open
diamond, monkey 1; open square, monkey 2; open triangle, monkey 3). Thick lines [in (C) and (D), respectively] indicate the best-ﬁt Weibull
functions for the population-averaged PRI(t) in the two areas. Thin lines indicate the same but for the neurons whose PRI(t) increased above
the 5% signiﬁcance level. (Reproduce from Naya Y, et al: Science 2001; 291: 661–664, Erratum in 2001; 291: 1703, Copyright : (2001), withpermission from Society for the American Association for the Advancement of Science)

modiﬁed PA task (PA with a color switch task), Nayaet al. (1996)15 showed that this pair-recall response indeed corresponded to the recall of the visual image insubject’s mind, since IT neurons started to ﬁre just after
a color switch that signaled the necessity and timing of
memory retrieval during its delay period and the IT
neurons also stopped to ﬁre just after another color
switch that signaled retrieval of other memorized items.
Such change of neuronal discharge did not occur whena color switch signaled a simple maintenance of shortterm memory. Therefore, this type of delay activity
in the IT cortex represents the internal target that is
retrieved from long-term memory. Recently, it wasreported that this target-related activity is transmitted
backward, from A36 to TE, as illustrated below (Nayaet al., 2001).16
The representative responses of a TE neuron and an

A36 neuron showing the target-related activity speciﬁed by a cue stimulus (Sakai and Miyashita, 1991,5
Naya et al., 199615) are shown in Figure 5A and 5B.
One stimulus elicited the strongest response during thecue period and the response continued into the delay
period (upper panel ). In the trial when the paired associate of this preferred stimulus was presented as acue, the neurons in both A36 and TE exhibited thehighest delay activity among the stimuli (lower panel ).
This type of activity is referred to as target-related. The
onset of the target-related activity of the TE neuron
was later than that of the A36 neuron (lower panel ).
The time course of the target-related delay activityof each neuron was examined by considering the
responses to all cue stimuli. The partial correlation
coefﬁcients of instantaneous ﬁring rates at time tfor each cue stimulus were calculated with the visualKeio J Med 2004; 53 (2): 59–68

Fig. 6 The posterior-split paradigm. A. Bottom-up condition. Visual
stimuli (cue and choice pictures) were presented in the hemiﬁeld
contralateral to the recording site (‘electrode’) in the IT cortex. The
monkeys were trained to memorize visual stimulus-stimulus associations among 20 cue and 5 choice pictures. In each trial, while themonkey held a lever and maintained ﬁxation, cue and choice pictures
were sequentially presented parafoveally with a delay. If the choice
picture was the correct one associated with the cue, the monkey had
to release the lever. Bottom-up sensory signals (black arrow) would
be detected in this condition. B. Top-down condition. The protocol is
the same as in A, but the cue was presented in the hemiﬁeld ipsilateral
to the recording site; the choice was presented contralaterally. In
posterior-split-brain monkeys, the sensory signal does not reach visualareas in the opposite hemisphere. In this condition, only top-down
signals (gray arrow) could activate IT neurons through feedback connections from the prefrontal cortex.

responses to its paired associate (pair-recall index,
PRI). The time courses of the average PRI(t) across the
population of stimulus-selective neurons signiﬁcantly
differed in A36 and TE (repeated-measures ANOVA,
p < 0:0001) (Fig. 5C, 5D). The PRI(t) for the A36
neurons began to increase during the cue period anddeveloped with a rapid time course. The PRI(t) for
the TE neurons, by contrast, increased slowly andreached a plateau in the middle of the delay period.
To summarize this section, memory-retrieval signals
appeared ﬁrst in A36, after which TE neurons were
gradually recruited to represent the sought target. Thus
mnemonic information that was extracted from longterm storage, spread backward, from A36 to TE.
Top-down signaling appears when active retrieval isrequired
A clinical case study helps us to highlight the active
retrieval in humans and provides a clue to an experi-65

mental model with which to investigate active retrieval(Sidtis et al., 1981).17 In that study, an epileptic patient
who had undergone posterior callosotomy was given
a word in his left visual ﬁeld. He could never readthe name of it directly, although he claimed to ‘see’
its image in his mind. He was eventually able toanswer the name using inferential strategies based on
his mental image. His limited ability suggests that his
right hemisphere was transmitting to his left hemisphere semantic information about the stimulus but not
the actual stimulus. After the callosum was completely
sectioned, semantic information was no longer transferred from his right hemisphere to left. Hasegawa et al.
(1998)12 combined this posterior-split-brain paradigm
with the associative memory task in monkeys. In theposterior-split-brain monkey, in which the posterior
corpus callosum and the anterior commissure are sectioned, the cortex receives bottom-up visual information only from the contralateral visual ﬁeld (Fig. 6A).
With this paradigm, long-term memory acquired
through stimulus-stimulus association does not transfer
interhemispherically via the anterior corpus callosum;
nonetheless, when the visual cue was presented to
one hemisphere, the anterior callosum could instruct
the other hemisphere to retrieve the correct stimulus
speciﬁed by the cue. Thus, although visual long-term
memory is stored in the temporal cortex, memory retrieval is under the executive control of the PF cortex.
Direct proof of the existence of top-down signal fromthe PF cortex to the temporal cortex and of its contribution to the active retrieval process was provided bysingle-unit recordings from the posterior-split-brain
preparation (Tomita et al., 1999).18 In this protocol, inferior temporal neurons in one hemisphere (‘electrode’,Fig. 6A) are to be activated by bottom-up visual inputs
when an object is presented in the visual hemiﬁeld
contralateral to the recording site. When the object ispresented in the ipsilateral hemiﬁeld, however, theseneurons should not be able to receive bottom-up visual
inputs. Any neural activation should therefore reﬂect
top-down inputs from the PF cortex (Fig. 6B). It was
found that a considerable number of IT neurons did
indeed receive top-down signals from the PF cortex; theactivity of one such IT neuron is shown in Figure 7. This
neuron was not only activated by contralateral presentation of stimuli (Fig. 7, bottom-up response, black), but
was also activated by ipsilateral presentation of stimuli(Fig. 7, top-down response, gray). In these neurons,
which showed stimulus-selectivity in both top-down
and bottom-up responses, it was found that the latency
was signiﬁcantly longer in the top-down condition ( p <0:001). These top-down responses were abolished after
transection of the remaining anterior corpus callosum.
The partial split-brain studies in human and monkeys
reveal the events occurring during the active retrieval

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Miyashita Y: Mechanisms of cognitive memorythat area 47/12 is strongly connected with the inferotemporal cortical region. Thus we suggest that thesource of the top-down signaling reported by Tomita
et al. (1999)18 may be a homologue of BA47/12 (seealso Ohbayashi et al., 2003).23 The process of active
retrieval could be involved where memory traces areaccessed through inferential strategies, either implicit
or explicit, which then raises the question of how suchinferential processes are cognitively controlled.
Metamemory: Cognitive Control of Memory System

Fig. 7 Activity of a single IT cell in the top-down condition (topdown, gray; bottom-up, black). Raster displays and PSTH were temporally aligned at the cue onset. In the PSTH, thick lines show
responses to the preferred cue, whereas thin lines show responses to anull cue. The onset of the top-down response (arrowhead) was later
than that of the bottom-up response (double arrowhead). (Reproduce
from Tomita H, et al: Nature 1999; 401: 699–703, Copyright : (1999),
with permission from Society for Nature Publishing Group)

process, which are indicative of purely top-down signaling.
Imaging studies carried out in humans have further
conﬁrmed that the PF cortex play a key role in theactive retrieval process. Activation of the PF cortex
during memory retrieval is widely observed in functional neuro-imaging studies employing a variety ofpsychological paradigms and test modalities, including recognition tests, word-stem tasks, word-fragmenttasks, paired associates tasks, free recall and recency
judgment (Miyashita & Hayashi, 2000,2 Cadret et al.,2001,19 Fletcher et al., 1998,20 Konishi et al., 2002,21
Wagner et al., 199822). Among these studies, Fletcher
et al. (1998)20 found that the right dorsal PF cortex(BA9/46) is strongly activated when monitoring
demands were emphasized, while the right ventral PF
cortex (BA45) showed greater activation when externalcueing was emphasized. Wagner et al. (1998)22 suggested that right PF activation reﬂect retrieval attempt
including initiation of retrieval search or evaluationof the products of retrieval. In addition, Cadret et al.
(2001)19 attempted to identify more focal areas thatwere speciﬁcally related to active retrieval by matching
the retrieval and control conditions in terms of depth of
encoding, decision-making and postretrieval monitoring. They found activity related to active retrieval to
be selectively increased within area 47/12 in the ventrolateral PF cortex. Earlier anatomical studies showed

In daily life, you might have experienced a situationin which, although you were unable to answer a question in a free recall, you were sure you could haveanswered correctly if it was a multiple-choice question.
In fact, you accessed related items in your semantic
network, since there is a positive correlation between
the objective score in the recognition test and the degree of a subjective feeling whether you knew theanswer or not. Metamemory refers to knowledge about
one’s memory capabilities and knowledge about strategies that can aid memory (Kikyo, Ohki & Miyashita,2002).24 Metamemory requires execution of extensive
retrieval process and, at the same time, supervises
the retrieval process. Kikyo et al. (2002)24 successfully
identiﬁed brain areas related to a metamemory system
in humans using a ‘‘feeling-of-knowing’’ (FOK) paradigm, which is a well-established tool for assessing
metamemory system (Fig. 8A). The FOK is a subjective
sense of knowing a word before recalling it – i.e., asense that ‘‘I know that I know it’’. Based on the recalljudgment-recognition (RJR) paradigm, subjects in thisexperiment were asked to recall word answers forgeneral-information questions during fMRI scans. After the scans, the subjects wrote their answers to therecalled questions and were instructed to judge theirdegree of FOK to the non-recalled questions on athree-point scale, where 3 ¼ hI deﬁnitely could recall
the answer if given hints or more timei; 2 ¼ hI probably would recognize the answeri; 1 ¼ hI deﬁnitely did
not knowi. Event-related fMRI with a parametricanalysis showed stronger activity in the bilateral IFGs
(BA 47), left MFG (BA 46/9, BA 10) and ACC/SMA(BA 32/24/6) when people have a greater FOK (Fig.
8B). These activation areas are referred to here as the
FOK regions. Among these FOK regions, subregionsin the bilateral IFGs (BA 47) were not recruited forsuccessful recall processes, suggesting a speciﬁc roleof these regions in human metamemory system. One of
the FOK regions was located in the anterior portion of
the left MFG, BA10. This area was regarded as a partof the memory areas in the anterior prefrontal cortex(AFC) in some literatures and was related to retrieval
strategy and/or ‘‘third level of executive control’’.

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Fig. 8 A. Experimental procedures of the Feeling-of-Knowing (FOK) trials. Subjects were required to recall word answers to general-information questions during fMRI scans. By pressing buttons, they indicated whether they recalled the target words or not. Then, outside thescanner, they judged their degree of FOK to the nonrecalled questions on a scale of three for the non-recalled questions, or they wrote theiranswers to the recalled questions. Each trial was sorted into trial type (Recalled, FOK3, FOK2, and FOK1) according to the participant’s
judgment and was subjected to event-related fMRI analysis. B. Regions involved in FOK and/or successful recall. A subset of the FOK regions
was also activated in successful recall (c, d, and e). A subset of the FOK regions in the bilateral IFGs was not activated in successful recall (a andb). A small subset of the FOK region in the left IFG was also activated in successful recall, but its cluster size did not reach the signiﬁcant, given
the correction for the whole brain multiple comparisons. (Reproduce from Kikyo H, et al: Neuron 2002; 36: 177–186, Copyright : (2002), withpermission from Society for Elsevier Science)

Future Perspective
The extensive investigations described in this chapter
have been revealing a whole picture of the semantic
memory system. There are, however, still pieces missingthat will be needed to make the picture complete. For
example, in the metamemory system, how are the active
retrieval subsystem and its cognitive control subsystem
integrated?; How does each component of the identiﬁed distributed metamemory network in the PF cortex
differentially support those subsystems?; Is there any
speciﬁc molecular/cellular basis for those differentialfunctional subsystems? It is anticipated that a numberof missing pieces will be found when the gap between
the information provided by invasive studies carried out
with monkeys and that provided by non-invasive human imaging studies is ﬁlled (Miyashita and Hayashi,
2000).2 Most of the detailed knowledge of the anatomy,

function and cellular basis of the cortex has come fromstudies in monkeys (Miyashita, 1993).1 With that asbackground, using the same methods to study humans
and monkeys would advance our understanding of theneural organization of higher order cognitive function.
For example, fMRI is a method that may bridge thisgap by enabling direct comparison of the functional organization of the brains of monkeys and humans. Usingthat approach, Nakahara et al. (2001)25 observed that,when subjects performed a high-level cognitive task,
transient activation related to cognitive set shifting
occurred in focal regions of the prefrontal cortex in bothmonkeys and humans, and that these functional homologues were located in cytoarchitectonically equivalentregions in the posterior part of ventrolateral prefrontal
cortex. Recently, we successfully extended these comparative neuroimaging studies into a higher spatial
resolution one with a high-ﬁeld (4.7T) MRI system

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Miyashita Y: Mechanisms of cognitive memory(Koyama et al., 2004).26 Such comparative imaging between humans and macaques has the potential to provide signiﬁcant new insight into the evolution of cognition in primates.
Finally, I hope these integrative approaches facilitate
further advancement of systems neuroscience regarding
cognition and also encourage young students and postdoctoral students who have just started their scientiﬁccareer towards the investigation of the human mind.
Acknowledgements: I would like to gratefully acknowledge the helpof my collaborators, Kuniyoshi Sakai, Hiroyuki Okuno, Wataru
Tokuyama, Yuji Naya, Kiyoshi Nakahara and Isao Hasegawa. Many
more students than I can name individually have my grateful thanks
and acknowledgement for their assistance in these researches.

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