Activity of human hippocampal and amygdala neurons during retrieval of declarative memories

Episodic memories allow us to remember not only that we have seen an item before but also where and when we have seen it (context). Sometimes, we can confidently report that we have seen something (familiarity) but cannot recollect where or when it was seen. Thus, the two components of episodic recall, familiarity and recollection, can be behaviorally dissociated. It is not clear, however, whether these two components of memory are represented separately by distinct brain structures or different populations of neurons in a single anatomical structure. Here, we report that the spiking activity of single neurons in the human hippocampus and amygdala [the medial temporal lobe (MTL)] contain information about both components of memory. We analyzed a class of neurons that changed its firing rate to the second presentation of a previously novel stimulus. We found that the neuronal activity evoked by the presentation of a familiar stimulus (during retrieval) distinguishes stimuli that will be successfully recollected from stimuli that will not be recollected. Importantly, the ability to predict whether a stimulus is familiar is not influenced by whether the stimulus will later be recollected. We thus conclude that human MTL neurons contain information about both components of memory. These data support a continuous strength of memory model of MTL function: the stronger the neuronal response, the better the memory.     

Selective memory retrieval can revive forgotten memories

Humans remember less and less of what was encoded as more and more time passes. Selective retrieval can interrupt such time-dependent forgetting, enhancing recall not only of the retrieved but also of the nonretrieved information. The recall enhancement has been attributed to context retrieval and the idea that selective retrieval reactivates the retrieved item’s temporal context during study, which can facilitate recall of other items that had a similar context at study. However, it is unclear whether context retrieval induces a transient discontinuity in the stream of temporal context only, or a more permanent updating of context that would entail a lasting interruption of time-dependent forgetting. In three experiments, we analyzed time-dependent forgetting of encoded information right after study and after time-lagged selective retrieval. Selective retrieval boosted recall of the nonretrieved information up to the levels observed directly after study. Intriguingly, it also created a restart of time-dependent forgetting that made forgetting after retrieval indistinguishable from forgetting after study and thus induced a reset of the recall process. The results suggest that selective retrieval can revive forgotten memories and cause lasting recall enhancement, effects likely mediated by context retrieval and a permanent updating of temporal context.

People recall much more detail of an event shortly after they observed the event than a few hours or even days later. In fact, recall typically declines rapidly soon after encoding followed by a long, much slower decline in recall performance (1–4). It is important to understand if and how such time-dependent forgetting can be attenuated or even be interrupted. Recent research has demonstrated that memory retrieval can interrupt time-dependent forgetting.When people study a list of items or study some prose passage and, after a longer time interval, selectively retrieve some of the studied information, recall of the other nonretrieved information is often enhanced (5–8). This recall enhancement interrupts time-dependent forgetting of this information, creating a recall level that can even be similar to the recall level shortly after study (Fig. 1). However, it is unclear whether the interruption represents a short-lived or a lasting effect on recall performance. The interruption may be transient in character, with the recall level of the nonretrieved information returning to the original course of forgetting soon after the selective retrieval. But the interruption may also be more permanent in character and, for instance, be accompanied by a restart of time-dependent forgetting. Such restart would make the forgetting after retrieval identical to the original time-dependent forgetting after study. Selective retrieval would thus revive the forgotten memories, induce a reset of recall of these memory contents, and create lasting effects of recall enhancement. It is the primary goal here to examine the time-dependent forgetting of nonretrieved information after selective retrieval and compare it with the time-dependent forgetting after study.Open in a separate windowFig. 1.Hypothetical time-dependent forgetting of studied items is shown before and after intermediate selective retrieval. The left curve represents time-dependent forgetting directly after study before selective retrieval. Selective retrieval interrupts the nonretrieved items’ forgetting and boosts their recall right after the selective retrieval. The two right curves represent hypothetical time-dependent forgetting of the nonretrieved items as time since selective retrieval passes. One curve assumes that the recall boost reflects a transient effect and recall quickly returns to the original course of forgetting. The other curve assumes that selective retrieval creates a restart of time-dependent forgetting that makes the forgetting after retrieval identical to time-dependent forgetting after study.The observed recall enhancement of the nonretrieved information right after selective retrieval has been attributed to context retrieval (7, 8). Temporal context, which reflects external conditions but also an ever-changing internal context state, changes gradually over time (9, 10), and each studied item is associated with the temporal context in which it is shown (11–14). A temporal lag between study and retrieval thus induces context change and makes context during retrieval different from context during study, which can cause forgetting (15). However, retrieval of an item can reactivate the context that was present when that item was studied, and this retrieved context can then serve as a retrieval cue for other items with a similar context at study (16–18). Thus, if retrieved and nonretrieved items share contextual features encoded during study, retrieval can reactivate part of the study context of nonretrieved items and thus facilitate recall of these items.Context retrieval updates context by adding the retrieved study context to the current state of temporal context (13, 14, 19). Such updating effectively shifts the study context closer to the later time-of-test context (20). If lasting, such shift of study context could cause a restart of the forgetting process and make time-dependent forgetting after selective retrieval similar to time-dependent forgetting after study. However, it is unclear whether such context updating is lasting. Another possibility is that the updating reflects a transient effect and study context becomes available for a short time after retrieval only. In such case, the effect would reflect a transient discontinuity in the stream of temporal context only (21) and recall would quickly return to the information’s original course of forgetting after study.Here, results from three experiments are reported aimed at shedding light onto how selective retrieval influences time-dependent forgetting. In each experiment, we compared time-dependent forgetting of studied items when recall was tested after study in the absence of selective retrieval with time-dependent forgetting of retrieved and nonretrieved items when recall was tested after selective retrieval. During selective retrieval, participants retrieved some studied items, thus creating retrieved and nonretrieved items. Both when recall was tested after study and when it was tested after selective retrieval, recall was assessed at different delay intervals, which allowed a comparison of the time-dependent forgetting before and after selective retrieval. Retrieval has recently been found to attenuate time-dependent forgetting of the retrieved information and improve its recall performance (22–24). Our experiments provide a link to this research by permitting a comparison of the time-dependent forgetting of retrieved and nonretrieved information.     

Two kinds of memory signals in neurons of the human hippocampus

Prior studies of the neural representation of episodic memory in the human hippocampus have identified generic memory signals representing the categorical status of test items (novel vs. repeated), whereas other studies have identified item specific memory signals representing individual test items. Here, we report that both kinds of memory signals can be detected in hippocampal neurons in the same experiment. We recorded single-unit activity from four brain regions (hippocampus, amygdala, anterior cingulate, and prefrontal cortex) of epilepsy patients as they completed a continuous recognition task. The generic signal was found in all four brain regions, whereas the item-specific memory signal was detected only in the hippocampus and reflected sparse coding. That is, for the item-specific signal, each hippocampal neuron responded strongly to a small fraction of repeated words, and each repeated word elicited strong responding in a small fraction of neurons. The neural code was sparse, pattern-separated, and limited to the hippocampus, consistent with longstanding computational models. We suggest that the item-specific episodic memory signal in the hippocampus is fundamental, whereas the more widespread generic memory signal is derivative and is likely used by different areas of the brain to perform memory-related functions that do not require item-specific information.

The hippocampus is essential for the formation of declarative (conscious) memory (1, 2), including both episodic memory (memory for events) and semantic memory (factual knowledge). Episodic memories represent the “what, when, and where” information about remembered events (3). Here, we focus on the neural representation of episodic memory for events, specifically words presented and later repeated in a continuous recognition memory format (4).Bilateral hippocampal lesions result in substantial anterograde amnesia for new events, whether memory is tested by recall or recognition (5). By contrast, bilateral lesions to a more anterior medial temporal lobe structure―the amygdala―have no such effect (6). One might therefore expect to find single-unit activity associated with episodic memory in the hippocampus but not in the amygdala. Yet, the earliest single-neuron studies failed to detect hippocampal neurons that fired differentially to recently presented test items vs. novel items. This was true in studies with humans (7, 8) and monkeys (9–11). One early study with monkeys identified a few such neurons in the hippocampus (12), and other studies found them in areas other than the hippocampus (e.g., inferomedial temporal cortex or inferotemporal temporal cortex) (9–11, 13, 14). Overall, this was not the pattern anticipated from lesion studies.Subsequent studies successfully detected some memory-related neural activity (15–17), observing that ∼10% of hippocampal neurons exhibited differential firing rates based on item status, with some firing more for repeated items and others firing more for novel items. Surprisingly, similar “memory-selective” neurons were also reliably detected in the amygdala at approximately the same frequency. Yet, these memory-selective neurons responded differentially to the generic, categorical status of test items (repeated vs. novel) and thus are not episodic memory signals (i.e., signals representing memory for specific events). According to neurocomputational models dating back to Marr (18), episodic memory representations in the hippocampus are supported by sparse neural codes (19–21). If memories for individual items are sparsely coded in largely nonoverlapping (pattern-separated) neural assemblies, it should be possible to find neurons that respond to particular repeated items, rather than to an item’s generic status. Two recent single-unit studies with humans detected such neurons in the hippocampus, but not in the amygdala (22, 23), apparently reflecting sparsely coded episodic memories. In the present study, we tested 1) whether the generic and the item-specific signals coexist in neural firing patterns recorded during the same memory task, and 2) whether the two kinds of signals are present exclusively in the hippocampus or are also evident in other brain regions.During a continuous recognition memory procedure, neurons were simultaneously recorded from four brain regions: hippocampus, amygdala, anterior cingulate cortex, and prefrontal cortex. Altogether, 55 continuous recognition memory sessions were completed by 34 epilepsy patients who had implanted clinical depth electrodes with microwires measuring single-unit activity (SUA) and multiunit activity bilaterally (24). We limited the present analyses to SUA. Words were presented consecutively and repeated once after varying lags; patients judged each word as either “novel” or “repeated.” Thus, repeated words differed from their earlier presentations as novel words only with respect to their combined “what, when, and where” episodic status (3).     

Unique semantic space in the brain of each beholder predicts perceived similarity

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual’s unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual''s perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas.Everyone’s perception of the world is unique. Psychologists and psychotherapists, using methods including questionnaires and free association, have long attempted to peer into an individual’s subjective experiential world. The unique aspects of our experience coexist with a shared experiential component. We can all recognize the objects that surround us and name them in a common language. Consistent with this shared component of experience, there is evidence that visual stimuli are processed similarly in the brains of different individuals (1). However, the unique way in which each of us perceives an object also must arise from brain activity. Is there an individually unique component to our brain representations?Unidimensional aspects of subjective visual percepts, ranging from estimates of object size, color, vividness, and emotional valence, have separately been found to correlate with interindividual variation in both univariate regional-average activation and cortical anatomy (2, 3). However, it remains unclear how a person’s multidimensional subjective percept reflects the multivariate brain-activity pattern that represents a particular object.Functional magnetic resonance imaging (fMRI) studies of object vision have focused largely on commonalities among subjects and category averages across particular stimuli. These studies have revealed regions in human inferior temporal cortex (hIT) that preferentially respond to specific categories (4–9) as well as widely distributed category information (10). More recently, similarity analyses of response patterns to particular stimulus images have revealed exemplar-specific representations (11–14), clustering of response patterns by natural categories (15–17), and reinstatement of neural representations during memory recall (18, 19). These prior studies either tacitly assumed similar representations across individuals or explicitly demonstrated commonalities between individuals and even between species (14, 20–29).Previous studies have shown that the hIT representation has a semantic component (23) and is reflected in perception at the level of group averages (30). Here we tested the hypothesis that an individual’s hIT representation predicts idiosyncrasies in his or her perception of natural objects. Because of hIT’s reciprocal connections to the memory regions of the medial temporal lobe (31), we further predicted that personally meaningful objects elicit individually unique mnemonic associations and are more distinctly represented in each individual.We presented familiar and unfamiliar object images to subjects during fMRI and investigated whether early visual cortex (EVC) and hIT exhibit individually unique representations. We characterized the representational geometry of each region by the dissimilarity matrix of activity patterns elicited by particular object images. This matrix is called the “representational dissimilarity matrix” (RDM) (16). To address whether the detailed representational geometries are reflected in behavior, we tested whether individual idiosyncrasies in similarity judgments can be predicted on the basis of a subject’s brain RDM.     

Neuronal code for extended time in the hippocampus

The time when an event occurs can become part of autobiographical memories. In brain structures that support such memories, a neural code should exist that represents when or how long ago events occurred. Here we describe a neuronal coding mechanism in hippocampus that can be used to represent the recency of an experience over intervals of hours to days. When the same event is repeated after such time periods, the activity patterns of hippocampal CA1 cell populations progressively differ with increasing temporal distances. Coding for space and context is nonetheless preserved. Compared with CA1, the firing patterns of hippocampal CA3 cell populations are highly reproducible, irrespective of the time interval, and thus provide a stable memory code over time. Therefore, the neuronal activity patterns in CA1 but not CA3 include a code that can be used to distinguish between time intervals on an extended scale, consistent with behavioral studies showing that the CA1 area is selectively required for temporal coding over such periods.     

Memory reactivation during rest supports upcoming learning of related content

Although a number of studies have highlighted the importance of offline processes for memory, how these mechanisms influence future learning remains unknown. Participants with established memories for a set of initial face–object associations were scanned during passive rest and during encoding of new related and unrelated pairs of objects. Spontaneous reactivation of established memories and enhanced hippocampal–neocortical functional connectivity during rest was related to better subsequent learning, specifically of related content. Moreover, the degree of functional coupling during rest was predictive of neural engagement during the new learning experience itself. These results suggest that through rest-phase reactivation and hippocampal–neocortical interactions, existing memories may come to facilitate encoding during subsequent related episodes.Numerous empirical studies (1–4) and theoretical accounts (5, 6) highlight the importance of offline processes—such as reinstatement of recent experience and enhanced interregional communication—for episodic memory. It has been proposed that through hippocampal (HPC)–neocortical interactions (6, 7), memories are reactivated during periods of sleep and awake rest. Such reactivation (or “replay”) is thought to support the strengthening and transfer of memory traces from the HPC to neocortical regions for long-term storage, a process termed “consolidation.” The functional significance of reactivation of recent experience for memory has been demonstrated during awake rest using neurophysiological techniques in rodents (2) and, more recently, in humans using pattern information analysis of functional magnetic resonance imaging (fMRI) data (1, 3). For instance, more delay period reactivation has been observed for stimuli that were remembered, relative to those that were forgotten in a subsequent test (3). Moreover, studies have shown that the degree of HPC–neocortical functional coupling during rest periods following learning relates to later memory for the learned content (4).This existing body of work demonstrates that rest-phase neural signatures relate to memory for prior experiences. However, one important quality of memory is that it is inherently prospective (8); that is, memories are formed for maximal utility in future situations. Whereas research shows that rest-phase reactivation impacts memory for the reactivated content itself (1, 3), how this mechanism might be prospectively advantageous remains unknown. In the present study, we turn our attention to this question: How does spontaneous reactivation of established memories and enhanced HPC–neocortical connectivity during rest affect learning during subsequent related episodes?A number of theories underscore the highly interactive nature of episodic memories (9, 10). One prominent view, “interference theory,” highlights that existing knowledge may impair learning of related content. A host of studies confirm this intuition; that is, people often have worse memory for information that is related to their existing memories relative to unrelated information, a phenomenon termed “proactive interference” (11–13). However, this impairment is not universally observed, even in the classic literature; on the contrary, prior knowledge can also be beneficial to new learning under some circumstances (14). For example, one study showed a memory advantage for new responses paired with well-learned old stimuli (i.e., stimuli previously learned with a different response), a phenomenon known as “associative facilitation” (11). Such facilitation may also extend to novel judgments that require the simultaneous consideration of multiple memories (e.g., inferences).Whereas these data and others (15) suggest that strong prior knowledge may facilitate new learning, the neural mechanisms supporting such associative facilitation are not well understood. One possible explanation stems from a perspective known as “integrative encoding,” which describes how new memories are created in relation to existing knowledge (16, 17). Mechanistically, it has been proposed that when newly encountered content overlaps with one’s stored memory representations, the neural patterns associated with that preexisting knowledge may be reactivated in the brain during new learning (18–20). New episodes may then be encoded in the context of these internally generated representations, connecting these related memories. A recent fMRI study suggests that reactivation of existing knowledge during encoding of new, overlapping events may strengthen preexisting memory traces, making the prior knowledge itself less susceptible to interference (18). Reactivation during learning has also been shown to support novel judgments that span experiences (20), consistent with the notion that this mechanism enables the linking of related memories. However, the potential impact of encoding-phase reactivation on the new learning itself has not been addressed. That is, although reactivation has been shown to strengthen both established memories and the connections among discrete experiences, it is as yet undetermined whether this process also facilitates memory formation for the new, related events through integration.We propose that the degree to which memory processes are engaged during offline periods influences whether prior knowledge interferes with or facilitates new encoding. Importantly, interference theory and integrative encoding make opposing predictions for the impact of rest-phase processes on subsequent learning of related events. Both perspectives might predict that memories are strengthened during offline periods; and that stronger memories are more likely to be reactivated during learning of new, related events. However, these perspectives diverge in their predictions for the consequences of that reactivation on new learning. Although interference theory would suggest that rest-phase strengthening of the initially acquired information might lead to more “competition” and thus worse memory for new, related content (21), integrative encoding predicts the opposite. Because stronger memories are more readily reinstated, they are also more likely to be “updated” with new information during subsequent experiences. For this reason, more engagement of rest-phase memory processing might facilitate both the later encoding of related events and novel judgments that span episodes. We sought to adjudicate between these perspectives by investigating the impact of offline reactivation and functional coupling on subsequent encoding of distinct but related experiences.We used a classic interference paradigm (11, 13) in which adult human participants with prior knowledge encoded new, overlapping pairs. We first trained participants (n = 35) on a set of face–object associations (hereafter AB pairs, where “AB” denotes a studied A_face–B_object association) across four study–test repetitions (Fig. 1A, Experimental Procedures, and SI Methods and Results, Memory Task). We then collected fMRI data while participants engaged in passive rest and encoding of both new overlapping (BC) object–object pairs and nonoverlapping (i.e., unrelated; XY) object–object pairs in a single exposure. Importantly, the order of BC and XY learning was counterbalanced across participants. After scanning, participants completed a cued recall test for studied associations (BC and XY) and a surprise test of inferential (AC) relationships. The AC inference test required participants to recall the A_face item that was indirectly related to the C_object cue through their common association with B_object, indexing each individual’s ability to combine remembered associations across episodes. This paradigm enables investigation of the neural mechanisms that modulate how existing memories (AB) impact future learning (BC) and inference (AC), thus improving our fundamental understanding of the interactive nature of real-world memory.Open in a separate windowFig. 1.Experimental procedure and performance. (A) Participants encoded AB pairs in four alternating study–test repetitions during the pretraining phase (blue). Participants then studied new overlapping (BC; orange) and nonoverlapping (XY; green) pairs during fMRI scanning. BC and XY study blocks were interleaved with rest scans (yellow); the encoding order of BC vs. XY was counterbalanced across participants. After scanning, memory for BC and XY pairs (intermixed; orange/green) and AC inferences (pink) was tested using cued recall. (B) AB memory performance as proportion correct on each test block. Line represents the group mean; points show individual participants. (C) Performance for nonoverlapping XY pairs (green), overlapping BC pairs (orange), and AC inferences (pink). Bar heights represent group means; points show individual participants. See also Fig. S1.     

Melatonin and the human hippocampus, a time dependent interplay

Melatonin serves as a signal of darkness and participates in sleep/wake regulation. Animal studies demonstrated effects of melatonin in the hippocampus, particularly suggesting involvement in synaptic plasticity. We used functional magnetic resonance imaging to identify and investigate effects of melatonin in the human hippocampus. Activity in the hippocampal complex during a memory task was examined at 22:00 hr (when endogenous melatonin levels are normally increasing) and compared with 16:00 hr (when endogenous melatonin levels are minimal). The relationship between observed activation patterns and endogenous melatonin was assessed. Finally, the effects of exogenous melatonin administered at 22:00 hr were studied in a double-blind, placebo-controlled crossover manner. Our findings indicate that activation in the left hippocampus at 22:00 hr is significantly reduced compared with afternoon hours compatible with diurnal variation in hippocampal activity. Exogenous melatonin further reduced activation in this region, only in subjects who already crossed the melatonin onset phase at this hour and in correlation with endogenous melatonin levels. As such an effect was not demonstrated with afternoon administration of melatonin, a time depended effect is suggested. Contrary, activation in the left para-hippocampus at 22:00 hr was higher in subjects that crossed the melatonin onset phase. Parahippocampal activation correlated with individual endogenous melatonin levels and was not further affected by exogenous melatonin. These results demonstrate that memory related activation in the hippocampus and para-hippocampus are affected by time of day and melatonin in a differential manner and may implicate the circadian clock and melatonin in human memory processing during the night.     

A role for the hippocampus in encoding simulations of future events

The role of the hippocampus in imagining the future has been of considerable interest. Preferential right hippocampal engagement is observed for imagined future events relative to remembered past events, and patients with hippocampal damage are impaired when imagining detailed future events. However, some patients with hippocampal damage are not impaired at imagining, suggesting that there are conditions in which the hippocampus may not be necessary for episodic simulation. Given the known hippocampal role in memory encoding, the hippocampal activity associated with imagining may reflect the encoding of simulations rather than event construction per se. The present functional (f)MRI study investigated this possibility. Participants imagined future events in response to person, place, and object cues. A postscan cued-recall test probing memory for detail sets classified future events as either successfully encoded or not. A contrast of successfully versus unsuccessfully encoded events revealed anterior and posterior right hippocampal clusters. When imagined events were successfully encoded, both anterior and posterior hippocampus showed common functional connectivity to a network including parahippocampal gyrus, medial parietal and cingulate cortex, and medial prefrontal cortex. However, when encoding was unsuccessful, only the anterior hippocampus, and not the posterior, exhibited this pattern of connectivity. These findings demonstrate that right hippocampal activity observed during future simulation may reflect the encoding of the simulations into memory. This function is not essential for constructing coherent scenarios and may explain why some patients with hippocampal damage are still able to imagine the future.     

Role of the hippocampus in remembering the past and imagining the future

It has been proposed that a core network of brain regions, including the hippocampus, supports both past remembering and future imagining. We investigated the importance of the hippocampus for these functions. Five patients with bilateral hippocampal damage and one patient with large medial temporal lobe lesions were tested for their ability to recount autobiographical episodes from the remote past, the recent past, and to imagine plausible episodes in the near future. The patients with hippocampal damage had intact remote autobiographical memory, modestly impaired recent memory, and an intact ability to imagine the future. The patient with large medial temporal lobe lesions had intact remote memory, markedly impaired recent memory, and also had an intact ability to imagine the future. The findings suggest that the capacity for imagining the future, like the capacity for remembering the remote past, is independent of the hippocampus.     

Functional inactivation of the rat hippocampus disrupts avoidance of a moving object

The hippocampus is well known for its critical involvement in spatial memory and information processing. In this study, we examined the effect of bilateral hippocampal inactivation with tetrodotoxin (TTX) in an "enemy avoidance" task. In this paradigm, a rat foraging on a circular platform (82 cm diameter) is trained to avoid a moving robot in 20-min sessions. Whenever the rat is located within 25 cm of the robot's center, it receives a mild electrical foot shock, which may be repeated until the subject makes an escape response to a safe distance. Seventeen young male Long-Evans rats were implanted with cannulae aimed at the dorsal hippocampus 14 d before the start of the training. After 6 d of training, each rat received a bilateral intrahippocampal infusion of TTX (5 ng in 1 μL) 40 min before the training session on day 7. The inactivation severely impaired avoidance of a moving robot (n = 8). No deficit was observed in a different group of rats (n = 9) that avoided a stable robot that was only displaced once in the middle of the session, showing that the impairment was not due to a deficit in distance estimation, object-reinforcement association, or shock sensitivity. This finding suggests a specific role of the hippocampus in dynamic cognitive processes required for flexible navigation strategies such as continuous updating of information about the position of a moving stimulus.     

Sleep transforms the cerebral trace of declarative memories

After encoding, memory traces are initially fragile and have to be reinforced to become permanent. The initial steps of this process occur at a cellular level within minutes or hours. Besides this rapid synaptic consolidation, systems consolidation occurs within a time frame of days to years. For declarative memory, the latter is presumed to rely on an interaction between different brain regions, in particular the hippocampus and the medial prefrontal cortex (mPFC). Specifically, sleep has been proposed to provide a setting that supports such systems consolidation processes, leading to a transfer and perhaps transformation of memories. Using functional MRI, we show that postlearning sleep enhances hippocampal responses during recall of word pairs 48 h after learning, indicating intrahippocampal memory processing during sleep. At the same time, sleep induces a memory-related functional connectivity between the hippocampus and the mPFC. Six months after learning, memories activated the mPFC more strongly when they were encoded before sleep, showing that sleep leads to long-lasting changes in the representation of memories on a systems level.     

Remembering one year later: role of the amygdala and the medial temporal lobe memory system in retrieving emotional memories

The memory-enhancing effect of emotion can be powerful and long-lasting. Most studies investigating the neural bases of this phenomenon have focused on encoding and early consolidation processes, and hence little is known regarding the contribution of retrieval processes, particularly after lengthy retention intervals. To address this issue, we used event-related functional MRI to measure neural activity during the retrieval of emotional and neutral pictures after a retention interval of 1 yr. Retrieval activity for emotional and neutral pictures was separately analyzed for successfully (hits) vs. unsuccessfully (misses) retrieved items and for responses based on recollection vs. familiarity. Recognition performance was better for emotional than for neutral pictures, and this effect was found only for recollection-based responses. Successful retrieval of emotional pictures elicited greater activity than successful retrieval of neutral pictures in the amygdala, entorhinal cortex, and hippocampus. Moreover, in the amygdala and hippocampus, the emotion effect was greater for recollection than for familiarity, whereas in the entorhinal cortex, it was similar for both forms of retrieval. These findings clarify the role of the amygdala and the medial temporal lobe memory regions in recollection and familiarity of emotional memory after lengthy retention intervals.     

Detecting individual memories through the neural decoding of memory states and past experience

A wealth of neuroscientific evidence indicates that our brains respond differently to previously encountered than to novel stimuli. There has been an upswell of interest in the prospect that functional MRI (fMRI), when coupled with multivariate data analysis techniques, might allow the presence or absence of individual memories to be detected from brain activity patterns. This could have profound implications for forensic investigations and legal proceedings, and thus the merits and limitations of such an approach are in critical need of empirical evaluation. We conducted two experiments to investigate whether neural signatures of recognition memory can be reliably decoded from fMRI data. In Exp. 1, participants were scanned while making explicit recognition judgments for studied and novel faces. Multivoxel pattern analysis (MVPA) revealed a robust ability to classify whether a given face was subjectively experienced as old or new, as well as whether recognition was accompanied by recollection, strong familiarity, or weak familiarity. Moreover, a participant''s subjective mnemonic experiences could be reliably decoded even when the classifier was trained on the brain data from other individuals. In contrast, the ability to classify a face''s objective old/new status, when holding subjective status constant, was severely limited. This important boundary condition was further evidenced in Exp. 2, which demonstrated that mnemonic decoding is poor when memory is indirectly (implicitly) probed. Thus, although subjective memory states can be decoded quite accurately under controlled experimental conditions, fMRI has uncertain utility for objectively detecting an individual''s past experiences.     

Decoding the information structure underlying the neural representation of concepts

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

The capacity for conceptual knowledge is arguably one of the most defining properties of human cognition, and yet it is still unclear how concepts are represented in the brain. Recent developments in functional neuroimaging and computational linguistics have sparked renewed interest in elucidating the information structures and neural circuits underlying concept representation (1–5). Attempts to characterize the representational code for concepts typically involve information structures based on three qualitatively distinct types of information, namely, taxonomic, experiential, and distributional information. As the term implies, a taxonomic information system relies on category membership and intercategory relations. Our tendency to organize objects, events, and experiences into discrete categories has led most authors—dating back at least to Plato (6)—to take taxonomic structure as the central property of conceptual knowledge (7). The taxonomy for concepts is traditionally seen as a hierarchically structured network, with basic-level categories (e.g., “apple,” “orange”) grouped into superordinate categories (e.g., “fruit,” “food”) and subdivided into subordinate categories (e.g., “Gala apple,” “tangerine”) (8). A prominent account in cognitive science maintains that such categories are represented in the mind/brain as purely symbolic entities, whose semantic content and usefulness derive primarily from how they relate to each other (9, 10). Such representations are seen as qualitatively distinct from the sensory-motor processes through which we interact with the world, much like the distinction between software and hardware in digital computers.An experiential representational system, on the other hand, encodes information about the experiences that led to the formation of particular concepts. It is motivated by a view, often referred to as embodied, grounded, or situated semantics, in which concepts arise primarily from generalization over particular experiences, as information originating from the various modality-specific systems (e.g., visual, auditory, tactile, motor, affective) is combined and re-encoded into progressively more schematic representations that are stored in memory. Since, in this view, there is a degree of continuity between conceptual and modality-specific systems, concept representations are thought to reflect the structure of the perceptual, affective, and motor processes involved in those experiences (11–14).Finally, distributional information pertains to statistical patterns of co-occurrence between lexical concepts (i.e., concepts that are widely shared within a population and denoted by a single word) in natural language usage. As is now widely appreciated, these co-occurrence patterns encode a substantial amount of information about word meaning (15–17). Although word co-occurrence patterns primarily encode contextual associations, such as those connecting the words “cow,” “barn,” and “farmer,” semantic similarity information is indirectly encoded since words with similar meanings tend to appear in similar contexts (e.g., “cow” and “horse,” “pencil” and “pen”). This has led some authors to propose that concepts may be represented in the brain, at least in part, in terms of distributional information (15, 18).Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessments of patients with semantic deficits, with results typically interpreted in terms of taxonomic (19–24), experiential (13, 25–34), or distributional (2, 3, 5, 35, 36) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial (23, 37, 38), in part, because their representations of common lexical concepts are strongly intercorrelated. Patterns of word co-occurrence in natural language are driven in part by taxonomic and experiential similarities between the concepts to which they refer, and the taxonomy of natural categories is systematically related to the experiential attributes of the exemplars (39–41). Consequently, the empirical evidence currently available is unable to discriminate between these representational systems.Several computational models of concept representation have been proposed based on these structures. While earlier models relied heavily on hierarchical taxonomic structure (42, 43), more recent proposals have emphasized the role of experiential and/or distributional information (34, 44–46). The model by Chen and colleagues (45), for example, showed that graded taxonomic structure can emerge from the statistical coherent covariation found across experiences and exemplars without explicitly coding such taxonomic information per se. Other models propose that concepts may be formed through the combination of experiential and distributional information (44, 46), suggesting a dual representational code akin to Paivio’s dual coding theory (47).We investigated the relative contribution of each representational system by deriving quantitative predictions from each system for the similarity structure of a large set of concepts and then using representational similarity analysis (RSA) with high-resolution functional MRI (fMRI) to evaluate those predictions. Unlike the more typical cognitive subtraction technique, RSA focuses on the information structure of the pattern of neural responses to a set of stimuli (48). For a given stimulus set (e.g., words), RSA assesses how well the representational similarity structure predicted by a model matches the neural similarity structure observed from fMRI activation patterns (Fig. 1). This allowed us to directly compare, in quantitative terms, predictions derived from the three representational systems.Open in a separate windowFig. 1.Representational similarity analysis. (A) An fMRI activation map was generated for each concept presented in the study, and the activation across voxels was reshaped as a vector. (B) The neural RDM for the stimulus set was generated by computing the dissimilarity between these vectors (1 − correlation) for every pair of concepts. (C) A model-based RDM was computed from each model, and the similarity between each model’s RDM and the neural RDM was evaluated via Spearman correlation. (D) Anatomically defined ROIs. The dashed line indicates the boundary where temporal lobe ROIs were split into anterior and posterior portions (see main text for acronyms). (E) Cortical areas included in the functionally defined semantic network ROI (49).     

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

In daily life, we continuously draw on past experiences to predict the future. Expectation and surprise shape learning across many situations, such as when we discover misinformation in the news, receive feedback on an examination, or make decisions based on past outcomes. When our predictions are incorrect, we must update our mnemonic models of the world to support adaptive behavior. Prediction error is a measure of the discrepancy between expectation and reality; this surprise signal is both evident in brain activity and related to learning (1–6). The brain dynamically reconstructs memories during recall, recreating and revising past experiences based on current information (7). The intuitive idea that surprise governs learning has long shaped our understanding of memory, reward learning, perception, action, and social behavior (2, 8–14). Yet, the neural mechanisms that allow prediction error to update memories remain unknown.Past research has implicated the hippocampus in each of the mnemonic functions required for learning from prediction errors: retrieving memories to make predictions, identifying discrepancies between past and present, and encoding new information (2, 15–20). Functional MRI (fMRI) studies have shown that hippocampal activation increases after predictions are violated; this surprise response has been termed “mismatch detection” (18, 19, 21–23) or “mnemonic prediction error” (20). These past studies have shown that the hippocampus detects mnemonic prediction errors. Several theoretical frameworks have hypothesized that this hippocampal prediction error signal could update memories (17, 20, 24–27), but this crucial link for understanding how we learn from error has not yet been demonstrated.What mechanisms could link hippocampal prediction errors to memory updating? A leading hypothesis is that prediction errors shift the focus of attention and adjust cognitive processing (20, 28–32). After episodes that align with expectations, we should continue generating predictions and shift attention internally, sustaining and reinforcing existing memories. However, after mnemonic prediction errors, we should reset our expectations and shift attention externally, preparing to encode new information and update memories. Consistent with this idea, mnemonic prediction errors have been shown to enhance the hippocampal input pathway that supports encoding, but suppress the output pathway that supports retrieval (20). We propose that surprising events may also change intrinsic hippocampal processing, changing the effect of hippocampal activation on memory outcomes.Neuromodulation may be a critical factor that regulates hippocampal processing and enables memory updating. Currently, there is mixed evidence supporting two hypotheses: acetylcholine or dopamine could act upon the hippocampus to regulate processing after surprising events (24–27, 29, 31, 33, 34). Several models have proposed that acetylcholine from the medial septum (within the basal forebrain) regulates the balance between input and output pathways in the hippocampus (27–29, 35–38), thus allowing stored memories to be compared with perceptual input (31, 38, 39). After prediction errors, acetylcholine release could change hippocampal processing and enhance encoding or memory updating (26, 29, 33, 37, 39). On the other hand, dopamine released from the ventral tegmental area (VTA), if transmitted to the hippocampus, could also modulate hippocampal plasticity after prediction errors. Past studies have shown that the hippocampus and VTA are coactivated after surprising events (40, 41). Other work has shown that coactivation of the hippocampus and VTA predicts memory encoding and integration (42–45). Overall, basal forebrain and VTA neuromodulation are both candidate mechanisms for regulating hippocampal processing and memory updating.In the present study, we used an fMRI task with human participants to examine trial-wise hippocampal responses to prediction errors during narrative videos. During the “encoding phase,” participants viewed 70 full-length videos that featured narrative episodes with salient endings (e.g., a baseball batter hitting a home run) (Fig. 1A). During the “reactivation phase” the following day, participants watched the videos again (Fig. 1B). We elicited mnemonic prediction errors by interrupting half of the videos immediately before the expected narrative ending (e.g., the video ends while the baseball batter is midswing). These surprising interruptions were comparable to the prediction errors employed in prior studies of memory updating (1). Half of the videos were presented in full-length form (Full, as previously seen during the encoding phase) and half were presented in interrupted form (Interrupted, eliciting prediction error).Open in a separate windowFig. 1.Overview of experimental paradigm. (A) During the encoding phase, all videos were presented in full-length form. Here we show example frames depicting a stimulus video. (B) During the reactivation phase, participants viewed the 70 videos again, but half (35 videos) were interrupted to elicit mnemonic prediction error. Participants were cued with the video name, watched the video (Full or Interrupted), and then viewed a fixation screen. The “baseball” video was interrupted when the batter was midswing. fMRI analyses focused on the postvideo fixation periods (red highlighted boxes). Thus, visual and auditory stimulation were matched across Full and Interrupted conditions, allowing us to compare postvideo neural activation while controlling for perceptual input. (C) During the test phase, participants answered structured interview questions about all 70 videos, and were instructed to answer based on their memory of the Full video originally shown during the Encoding phase. Here we show example text illustrating the memory test format and scoring of correct details (our measure of memory preservation) and false memories (our measure of memory updating, because false memories indicate that the memory has been modified). The void response (“I don’t remember”) is not counted as a false memory. (D) Overview of the experiment. All participants completed encoding, reactivation, and test phases of the study. The Delayed group (fMRI participants) completed the test phase 24 h after reactivation, because prior studies have shown that memory updating becomes evident only after a delay (e.g., to permit protein synthesis). The Immediate group completed the test phase immediately after reactivation and was not scanned. The purpose of the Immediate group was to test the behavioral prediction that memory updating required a delay.During the “test phase,” participants completed a memory test in the form of a structured interview (Fig. 1C). On each trial, participants were cued with the name of the video and recalled the narrative. The experimenter then probed for further details with predetermined questions (e.g., “Can you describe the baseball batter’s ethnicity, age range, or clothing?”). Our critical measure of memory updating was “false memories,” because the presence of a false memory indicates that the original memory was changed in some way. Although it can be adaptive to update real-world memories by incorporating relevant new information, we expected that our laboratory paradigm would induce false memories because participants would integrate interfering details across similar episodes (1, 7). Because we were interested in false memories as a measure of memory updating, we instructed participants not to guess and permitted them to skip details they could not recall.Prior research in human and animals has shown that some memory-updating effects only emerge after delays that allow protein synthesis to occur during consolidation and reconsolidation (1, 46–48). Therefore, to test our primary question about the neural correlates of memory updating, fMRI participants completed the encoding, reactivation, and test phases over 3 d, with 24-h between each session (Delayed group, n = 24). In addition, we tested the behavioral prediction that memory updating would require a delay (i.e., because transforming a memory trace requires protein synthesis) by recruiting a separate group of participants who completed the test phase immediately after the reactivation phase on day 2 (Immediate group, n = 24) (Fig. 1D). Delayed group participants completed the reactivation phase while undergoing an fMRI scan, whereas Immediate group participants (n = 24) were not scanned. Our primary fMRI analyses examined the fixation period immediately following the offset of Full and Interrupted videos (postvideo period) (Fig. 1 B, Right) during the reactivation phase in the Delayed group. Importantly, this design compares neural responses to surprising and expected video endings while controlling for visual and auditory input.Our approach allowed us to test several questions set up by the prior literature. First, we used naturalistic video stimuli to examine the effect of mnemonic prediction error on hippocampal activation and episodic memories. Second, to investigate hippocampal processing, we used multivariate analyses to track how episodic representations were sustained or disrupted over time. Third, to test hypotheses about neuromodulatory mechanisms, we related hippocampal activation and memory updating to activation in the basal forebrain and VTA.     

Intolerance of uncertainty modulates brain-to-brain synchrony during politically polarized perception

Political partisans see the world through an ideologically biased lens. What drives political polarization? Although it has been posited that polarization arises because of an inability to tolerate uncertainty and a need to hold predictable beliefs about the world, evidence for this hypothesis remains elusive. We examined the relationship between uncertainty tolerance and political polarization using a combination of brain-to-brain synchrony and intersubject representational similarity analysis, which measured committed liberals’ and conservatives’ (n = 44) subjective interpretation of naturalistic political video material. Shared ideology between participants increased neural synchrony throughout the brain during a polarizing political debate filled with provocative language but not during a neutrally worded news clip on polarized topics or a nonpolitical documentary. During the political debate, neural synchrony in mentalizing and valuation networks was modulated by one’s aversion to uncertainty: Uncertainty-intolerant individuals experienced greater brain-to-brain synchrony with politically like-minded peers and lower synchrony with political opponents—an effect observed for liberals and conservatives alike. Moreover, the greater the neural synchrony between committed partisans, the more likely that two individuals formed similar, polarized attitudes about the debate. These results suggest that uncertainty attitudes gate the shared neural processing of political narratives, thereby fueling polarized attitude formation about hot-button issues.

Countries around the world are experiencing the strain of growing political polarization (1–5). Opposing partisans come to see the world through different eyes. Where one sees the freedom to choose, another sees murder; where one sees the right to protest, another sees violent conduct (6–8). Such a polarized perception of reality hampers bipartisan cooperation and can even undermine the basic principles of democracy (8, 9).How does polarization arise? One popular theory posits that a need to have certain, structured, and stable beliefs about the world drives people toward political extremes (10–13). Rather than seeing the world in nuanced shades of gray, cognitively rigid individuals perceive information in black and white, painting the world in categorical and predictable terms (14)—a view that dovetails with the immutable taxonomy of political ideologues (15–19). The rigid mind is characterized by a trait-like tendency to find unpredictable and uncertain events aversive and threatening (14, 20, 21) and has long been theorized to play an outsized role in shaping polarized perceptions (22–26). Although recent work suggests that uncertainty can impact the evaluation of political candidates (27) and policy positions (28, 29) and is a major factor contributing to political conservatism (30, 31), the link between uncertainty and political polarization remains unclear. Here, we examine whether individual differences in intolerance of uncertainty (IUS) (20, 21) shape how naturalistic political information is processed in the brain at the time of perception. We test the hypothesis that uncertainty-intolerant individuals interpret polarizing political information through an ideologically biased, subjective “lens” that produces clear-cut judgments of the issue at hand (20, 32). We further examine whether the neural fingerprint of these uncertainty-driven polarized perceptions—that is, increased brain-to-brain synchrony between like-minded partisans—predicts the formation of polarized attitudes.We combine two techniques to measure polarized perceptions of political information. First, intersubject correlation [ISC (33)] provides a direct measure of the similarity in subjective interpretations of naturalistic social stimuli (e.g., video narratives) among participants (34, 35). This technique capitalizes on the neural processes triggered by incoming auditory and visual information. If two individuals exhibit similar neural profiles when processing the same incoming information (e.g., synchronized blood oxygen level-dependent [BOLD] responses in functional MRI [fMRI]), they likely have a shared perception and understanding of that information (36–40). Given that ISC offers an established metric to gauge whether individuals are processing information in a similar way, we can use it to test whether two individuals who share the same political ideology also have similar subjective perceptions of political information, which circumvents issues with demand characteristics and explicit self-report (41). Second, to make neural synchrony analyses sensitive to more subtle differences along the ideological continuum than simple left–right groupings and to test for interactive effects between ideology and intolerance to uncertainty, we combine ISC with intersubject representational similarity analysis [IS-RSA (42–44)]. This versatile approach enables us to leverage continuous individual differences and test whether uncertainty attitudes exacerbate the processing of political information in the brain to fuel polarized political attitude formation.Using a combination of targeted online and field recruiting (n = 360), we invited 22 liberals and 22 conservatives to participate in a study on political cognition (Fig. 1A). While undergoing fMRI, participants viewed three types of videos: a neutrally worded news segment on a politically charged topic (abortion; taken from Public Broadcasting Service [PBS] News), an inflammatory debate segment (police brutality and immigration; taken from the 2016 Cable News Network [CNN] Vice-Presidential debate), and a nonpolitical nature video (taken from British Broadcasting Corporation [BBC] Earth; Fig. 1B). Neural data analysis consisted of time locking the fMRI BOLD signal to the onset of the videos and computing voxel-wise time course correlations between each possible pairing of subjects across the entire participant pool, resulting in a “neural synchrony” measurement that indexes shared subjective interpretations of dynamic, naturalistic stimuli (35, 45, 46). We first analyzed behavioral responses to the videos to test whether ideology, IUS, or both predicted similarities in attitude formation about the presented political videos. Next, we analyzed variation in neural synchrony across participant dyads using IS-RSA (Fig. 1D) to test three interrelated hypotheses: 1) shared ideology between subjects will predict brain-to-brain synchrony during the perception of political stimuli, 2) IUS will modulate this neural synchrony in committed partisans, and 3) increasing neural synchrony will predict the subsequent expression of shared polarized attitudes about the political stimuli.Open in a separate windowFig. 1.(A) Participants underwent fMRI and behavioral testing as part of a larger study on political cognition. (B) Participants viewed three videos in a fixed order while undergoing fMRI. (C) Participants were clearly divided on political ideology. (D) Analytical approach. We tested for variation in neural synchrony as a function of ideology and IUS. The statistical map slice is taken from Fig. 2C.     

Memory,scene construction,and the human hippocampus

We evaluated two different perspectives about the function of the human hippocampus–one that emphasizes the importance of memory and another that emphasizes the importance of spatial processing and scene construction. We gave tests of boundary extension, scene construction, and memory to patients with lesions limited to the hippocampus or large lesions of the medial temporal lobe. The patients were intact on all of the spatial tasks and impaired on all of the memory tasks. We discuss earlier studies that associated performance on these spatial tasks to hippocampal function. Our results demonstrate the importance of medial temporal lobe structures for memory and raise doubts about the idea that these structures have a prominent role in spatial cognition.Two traditions of work have influenced discussion about the function of the hippocampus (1). One tradition is based on work with memory-impaired patients and the idea that the hippocampus is important for a particular kind of memory (2, 3). The other tradition is based on work with rodents and the idea that the hippocampus is critical for spatial mapping (4). Its possible role in spatial processing has been recently explored in humans as well (5), and it has been proposed that the human hippocampus is essential for the ability to construct spatially coherent scenes (6, 7).This view of hippocampal function has depended on evidence from two kinds of tasks: boundary extension and scene construction (6, 8). Boundary extension refers to the tendency to reconstruct a scene such that it has a larger background than was actually presented (9). In the Mullally et al. (8) study, memory-impaired patients exhibited boundary extension less strongly than controls. Scene construction refers to the ability to imagine and describe spatially coherent scenes. In two studies, memory-impaired patients made few references to space when visualizing and describing imagined scenes (6, 8).It is unclear how to reconcile such findings with the view that the hippocampus chiefly supports memory functions. In particular, the idea that the construction and visualization of scenes involves the hippocampus seems at odds with the historic distinction between short-term (working) memory and long-term memory and the related idea that short-term memory is independent of the hippocampus (10–12). According to this perspective, hippocampal damage should not impair performance on spatial tasks, so long as testing puts no burden on long-term memory. In an attempt to clarify these issues, we gave tests of boundary extension, scene construction, and memory to patients with well-characterized lesions limited to the hippocampus or large lesions of the medial temporal lobe.