Anna C. Schapiro

Shaping of Object Representations in the Human Medial Temporal Lobe Based on Temporal Regularities

Regularities are gradually represented in cortex after extensive experience, and yet they can influence behavior after minimal exposure. What kind of representations support such rapid statistical learning? The medial temporal lobe (MTL) can represent information from even a single experience, making it a good candidate system for assisting in initial learning about regularities. We combined anatomical segmentation of the MTL, high-resolution fMRI, and multivariate pattern analysis to identify representations of objects in cortical and hippocampal areas of human MTL, assessing how these representations were shaped by exposure to regularities. Subjects viewed a continuous visual stream containing hidden temporal relationships--pairs of objects that reliably appeared nearby in time. We compared the pattern of blood oxygen level-dependent activity evoked by each object before and after this exposure, and found that perirhinal cortex, parahippocampal cortex, subiculum, CA1, and CA2/CA3/dentate gyrus (CA2/3/DG) encoded regularities by increasing the representational similarity of their constituent objects. Most regions exhibited bidirectional associative shaping, whereas CA2/3/DG represented regularities in a forward-looking predictive manner. These findings suggest that object representations in MTL come to mirror the temporal structure of the environment, supporting rapid and incidental statistical learning.

The necessity of the medial temporal lobe for statistical learning

The sensory input that we experience is highly patterned, and we are experts at detecting these regularities. Although the extraction of such regularities, or statistical learning (SL), is typically viewed as a cortical process, recent studies have implicated the medial temporal lobe (MTL), including the hippocampus. These studies have employed fMRI, leaving open the possibility that the MTL is involved but not necessary for SL. Here, we examined this issue in a case study of LSJ, a patient with complete bilateral hippocampal loss and broader MTL damage. In Experiments 1 and 2, LSJ and matched control participants were passively exposed to a continuous sequence of shapes, syllables, scenes, or tones containing temporal regularities in the co-occurrence of items. In a subsequent test phase, the control groups exhibited reliable SL in all conditions, successfully discriminating regularities from recombinations of the same items into novel foil sequences. LSJ, however, exhibited no SL, failing to discriminate regularities from foils. Experiment 3 ruled out more general explanations for this failure, such as inattention during exposure or difficulty following test instructions, by showing that LSJ could discriminate which individual items had been exposed. These findings provide converging support for the importance of the MTL in extracting temporal regularities.

Neural Context Reinstatement Predicts Memory Misattribution

What causes new information to be mistakenly attributed to an old experience? Some theories predict that reinstating the context of a prior experience allows new information to be bound to that context, leading to source memory confusion. To examine this prediction, we had human participants study two lists of items (visual objects) on separate days while undergoing functional magnetic resonance imaging. List 1 items were accompanied by a stream of scene images during the intertrial interval, but list 2 items were not. As in prior work by Hupbach et al. (2009), we observed an asymmetric pattern of misattributions on a subsequent source memory test: participants showed a strong tendency to misattribute list 2 items to list 1 but not vice versa. We hypothesized that these memory errors were due to participants reinstating the list 1 context during list 2. To test this hypothesis, we used a pattern classifier to measure scene-related neural activity during list 2 study. Because scenes were visually present during list 1 but not list 2, scene-related activity during list 2 study can be used as a time-varying neural indicator of how much participants were reinstating the list 1 context during list 2 study. In keeping with our hypothesis, we found that prestimulus scene activation during the study of list 2 items was significantly higher for items subsequently misattributed to list 1 than for items subsequently correctly attributed to list 2. We conclude by discussing how these findings relate to theories of memory reconsolidation.

Statistical learning of temporal community structure in the hippocampus

The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher‐order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher‐order structure that requires sensitivity to overlapping associations.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

Hippocampal structure predicts statistical learning and associative inference abilities during development

Despite the importance of learning and remembering across the lifespan, little is known about how the episodic memory system develops to support the extraction of associative structure from the environment. Here, we relate individual differences in volumes along the hippocampal long axis to performance on statistical learning and associative inference tasks—both of which require encoding associations that span multiple episodes—in a developmental sample ranging from ages 6 to 30 years. Relating age to volume, we found dissociable patterns across the hippocampal long axis, with opposite nonlinear volume changes in the head and body. These structural differences were paralleled by performance gains across the age range on both tasks, suggesting improvements in the cross-episode binding ability from childhood to adulthood. Controlling for age, we also found that smaller hippocampal heads were associated with superior behavioral performance on both tasks, consistent with this regions hypothesized role in forming generalized codes spanning events. Collectively, these results highlight the importance of examining hippocampal development as a function of position along the hippocampal axis and suggest that the hippocampal head is particularly important in encoding associative structure across development.

Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans

The field of computational reinforcement learning (RL) has proved extremely useful in research on human and animal behavior and brain function. However, the simple forms of RL considered in most empirical research do not scale well, making their relevance to complex, real-world behavior unclear. In computational RL, one strategy for addressing the scaling problem is to introduce hierarchical structure, an approach that has intriguing parallels with human behavior. We have begun to investigate the potential relevance of hierarchical RL (HRL) to human and animal behavior and brain function. In the present chapter, we first review two results that show the existence of neural correlates to key predictions from HRL. Then, we focus on one aspect of this work, which deals with the question of how action hierarchies are initially established. Work in HRL suggests that hierarchy learning is accomplished by identifying useful subgoal states, and that this might in turn be accomplished through a structural analysis of the given task domain. We review results from a set of behavioral and neuroimaging experiments, in which we have investigated the relevance of these ideas to human learning and decision making.

The role of sleep in consolidating semantic knowledge

• Ongoing and future directions: Running a nap version of paradigm in collaboration with Sara Mednick to directly assess contributions of sleep stages with PSG, and running fMRI version to test model’s predictions about representational changes over different kinds of offline learning periods. average plus phase coactivity weight = learning rate average minus phase coactivity gavan volar motar nivex sorex

Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

Sleep selectively stabilizes contextual aspects of negative memories

Sleep and emotion are both powerful modulators of the long-term stability of episodic memories, but precisely how these factors interact remains unresolved. We assessed changes in item recognition, contextual memory, and affective tone for negative and neutral memories across a 12 h interval containing sleep or wakefulness in 71 human volunteers. Our data indicate a sleep-dependent stabilization of negative contextual memories, in a way not seen for neutral memories, item recognition, or across wakefulness. Furthermore, retention of contextual memories was positively associated with time spent in non-rapid eye movement sleep. Finally, our results offer partial support for the hypothesis that sleep attenuates emotional responses to previously memorized material.