Focusing on what matters: Modulation of the human hippocampus by relational attention.

Hippocampal episodic memory is fundamentally relational, comprising links between events and the spatiotemporal contexts in which they occurred. Such relations are also important over shorter timescales, during online perception. For example, how do we assess the relative spatial positions of objects, their temporal order, or the relationship between their features? Here, we investigate the role of the hippocampus in online relational processing by manipulating attention to different kinds of relations. While undergoing fMRI, participants viewed two images in rapid succession on each trial and performed one of three relational tasks, judging the images' relative: spatial positions, temporal onsets, or sizes. Additionally, they sometimes judged whether one image was tilted, irrespective of the other. This served as a baseline item task with no demands on relational processing. The hippocampus showed reliable deactivation when participants attended to relational vs. item information. Attention to temporal relations was associated with the most robust deactivation. One interpretation of such deactivation is that it reflects hippocampal disengagement. If true, there should be reduced information content and noisier activity patterns for the temporal vs. other tasks. Instead, multivariate pattern analysis revealed more stable hippocampal representations in the temporal task. This increased pattern similarity was not simply a reflection of lower univariate activity. Thus, the hippocampus differentiates between relational and item processing even during online perception, and its representations of temporal relations are particularly robust. These findings suggest that the relational computations of the hippocampus extend beyond long-term memory, enabling rapid extraction of relational information in perception.

Attentional modulation of background connectivity between ventral visual cortex and the medial temporal lobe.

Attention prioritizes information that is most relevant to current behavioral goals. This prioritization can be accomplished by amplifying neural responses to goal-relevant information and by strengthening coupling between regions involved in processing this information. Such modulation occurs within and between areas of visual cortex, and relates to behavioral effects of attention on perception. However, attention also has powerful effects on learning and memory behavior, suggesting that similar modulation may occur for memory systems. We used fMRI to investigate this possibility, examining how visual information is prioritized for processing in the medial temporal lobe (MTL). We hypothesized that the way in which ventral visual cortex couples with MTL input structures will depend on the kind of information being attended. Indeed, visual cortex was more coupled with parahippocampal cortex when scenes were attended and more coupled with perirhinal cortex when faces were attended. This switching of MTL connectivity was more pronounced for visual voxels with weak selectivity, suggesting that connectivity might help disambiguate sensory signals. These findings provide an initial window into an attentional mechanism that could have consequences for learning and memory.

Evidence of hippocampal learning in human infants.

The hippocampus is essential for human memory.1 The protracted maturation of memory capacities from infancy through early childhood2-4 is thus often attributed to hippocampal immaturity.5-7 The hippocampus of human infants has been characterized in terms of anatomy,8,9 but its function has never been tested directly because of technical challenges.10,11 Here, we use recently developed methods for task-based fMRI in awake human infants12 to test the hypothesis that the infant hippocampus supports statistical learning.13-15 Hippocampal activity increased with exposure to visual sequences of objects when the temporal order contained regularities to be learned, compared to when the order was random. Despite the hippocampus doubling in anatomical volume across infancy, learning-related functional activity bore no relationship to age. This suggests that the hippocampus is recruited for statistical learning at the youngest ages in our sample, around 3 months. Within the hippocampus, statistical learning was clearer in anterior than posterior divisions. This is consistent with the theory that statistical learning occurs in the monosynaptic pathway,16 which is more strongly represented in the anterior hippocampus.17,18 The monosynaptic pathway develops earlier than the trisynaptic pathway, which is linked to episodic memory,19,20 raising the possibility that the infant hippocampus participates in statistical learning before it forms durable memories. Beyond the hippocampus, the medial prefrontal cortex showed statistical learning, consistent with its role in adult memory integration21 and generalization.22 These results suggest that the hippocampus supports the vital ability of infants to extract the structure of their environment through experience.

Neural representations of events arise from temporal community structure.

Our experience of the world seems to divide naturally into discrete, temporally extended events, yet the mechanisms underlying the learning and identification of events are poorly understood. Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. We present human behavioral and functional magnetic resonance imaging (fMRI) evidence in favor of a different account, in which event representations coalesce around clusters or 'communities' of mutually predicting stimuli. Through parsing behavior, fMRI adaptation and multivoxel pattern analysis, we demonstrate the emergence of event representations in a domain containing such community structure, but in which transition probabilities (the basis of uncertainty and surprise) are uniform. We present a computational account of how the relevant representations might arise, proposing a direct connection between event learning and the learning of semantic categories.