Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Abstract

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.category learning | attention | computational modeling | hippocampus | fMRI C oncepts are organizing principles that define how items or events are similar to one another. Goals are critical to shaping concepts, by emphasizing some shared features over others. When goals change, previously experienced events may be organized in new ways, resulting in an updated concept that highlights the features most critical to the new goal. For instance, consider purchasing a home. One must learn which features make for the most desirable home. A young couple seeking a cosmopolitan lifestyle may organize potential houses based on trendy features like exposed brick walls, a wet bar, and room for vintage record collections. However, with the news of a baby on the way, the couple's goals are likely to shift. After pouring through parenting books and web forums to learn what makes for a child-friendly home, they may look at those previously seen potential homes in a different light. Instead, family-oriented features such as whether or not a home has a bathtub, is within walking distance to a park, and is in a well-respected school district may matter more resulting in a reorganization of which homes are a good buy. At the core of this example are the fundamental challenges we face in flexible goaldirected learning. When learning new concepts (e.g., child-friendly instead of a trendy house), attention changes focus to different information and items that were conceptually dissimilar (e.g., two houses with and without a wet bar) may become more similar (e.g., they both are close to a park) and vice versa (1). Understanding how conceptual knowledge is created and updated during learning is a cen...

Model-based fMRI Reveals Dissimilarity Processes Underlying Base Rate Neglect

“…In line with recent evidence, we identify representational changes indicative of a selective attention mechanism that tracks the predictive value of different stimulus dimensions during learning (Mack et al, 2016;Leong et al, 2017). The data suggest that attention to these goal-relevant cues enhances their representation in objectselective cortex quickly and persistently in a simple rule-based category learning task (Figure 3).…”

“…For example, MVPA has been used to test mechanistic theories of how attention modulates activation of predictive information being held in working memory (LewisPeacock and Postle, 2008;Lewis-Peacock et al, 2012) and how bottom-up attentional orienting to visual stimulus features (Leong et al, 2017) manifests in enhanced neural activation of these features in ventral temporal cortex. Applications of these attentional findings to category learning have shown that, as a result of learning, stimulus dimensions that are predictive of category membership become more prominent in activation patterns in ventral temporal cortex (Mack et al, 2013) and stored stimulus representations in the hippocampus come to reflect these attention-based changes in neural stimulus representations (Mack et al, 2016).…”

Memory integration constructs maps of space, time, and concepts

“…Moreover, hippocampal responses scale with the conceptual novelty of experiences [70, 71], and such signals may be critical to concept formation and updating [72, 73]. Further evidence for the role of the hippocampus and mPFC in formation of conceptual maps comes from a set of recent studies that use representational approaches to neuroimaging data analysis [20, 74, 75]. These studies have shown that the similarity of hippocampal [20, 75] and mPFC [74] activation patterns between individual concept exemplars scales with their distance in a learned conceptual space; exemplars of the same concept evoke more similar activation patterns in hippocampus and mPFC and are distinct from exemplars of different concepts (Fig.…”

“…Finally, new content is integrated with existing mental models via hippocampus—mPFC interactions [17–19]. During integration, mPFC biases hippocampal encoding processes to emphasize representation of features common to multiple events [20, 21], resulting in cognitive maps that use overlapping neural codes to represent the relationships among discrete learning episodes [22, 23]. Here, we review recent evidence that hippocampus—mPFC memory integration mechanisms support the formation of cognitive maps across different domains of experience, including space, time, and concepts.…”

Medial prefrontal cortex compresses concept representations through learning

“…http://dx.doi.org/10.1101/178145 doi: bioRxiv preprint first posted online Aug. 23, 2017; counterbalanced across participants. This problem order was used for purposes 372 described in a prior analysis of this data (Mack et al, 2016 The classification problems consisted of learning trials (Figure 1a …”