Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions

Abstract

Our understanding of the world is shaped by inferences about underlying structure. For example, at the gym, you might notice that the same people tend to arrive around the same time and infer that they are friends that work out together. Consistent with this idea, after participants are presented with a temporal sequence of objects that follows an underlying community structure, they are biased to infer that objects from the same community share the same properties. Here, we used fMRI to measure neural representations of objects after temporal community structure learning and examine how these representations support inference about object relationships. We found that community structure learning affected inferred object similarity: When asked to spatially group items based on their experience, participants tended to group together objects from the same community. Neural representations in perirhinal cortex predicted individual differences in object grouping, suggesting that high-level object representations are affected by temporal community learning. Furthermore, participants were biased to infer that objects from the same community would share the same properties. Using computational modeling of temporal learning and inference decisions, we found that inductive reasoning is influenced by both detailed knowledge of temporal statistics and abstract knowledge of the temporal communities. The fidelity of temporal community representations in hippocampus and precuneus predicted the degree to which temporal community membership biased reasoning decisions. Our results suggest that temporal knowledge is represented at multiple levels of abstraction, and that perirhinal cortex, hippocampus, and precuneus may support inference based on this knowledge.

Dynamic emergence of relational structure network in human brains

“…Reasoning the hidden relational structure from outside events is a crucial ability we human beings possess to help predict the future and make inferences (Balaguer et al, 2016; Garvert et al, 2021; Pudhiyidath et al, 2021). Here we demonstrate the behaviorally-related neural representations of two key aspects of relational knowledge – lower-order transition probability and higher-order community structure, which occur around 840 msec after image onset, before making a response.…”

“…This neural signature occurs around 840 msec after image onset, before subject make responses, and could well explain the subsequent behavioral performance (i.e., RTs). Previous fMRI studies have revealed multiple brain regions, such as the hippocampus, inferior prefrontal gyrus (IFG), insula, anterior temporal lobe (ATL) in representing the network-based temporal relationship (Pudhiyidath et al, 2021; Schapiro et al, 2013; Schapiro et al, 2016). Thus, the observed neural signature might arise from collective contributions from many brain regions.…”

“…What is the computational mechanism underlying the formation of a higher-order community structure? Computational models of associative learning and reinforcement learning have been previously used to explain transition probability learning (Ciranka et al, 2022; Maheu et al, 2019), and the successor representation (SR) model could account for the emergence of community structure (Pudhiyidath et al, 2021; Stachenfeld et al, 2017). Here we built three computational models to seek the computational nature of the structural network.…”

“…Events in the world are not isolated but are always interconnected according to some hidden structure, which we human beings are constantly pursuing (Pudhiyidath et al, 2021; Schapiro, Rogers, Cordova, Turk-Browne, & Botvinick, 2013). In fact, humans are gifted with tremendous abilities to discover the latent structure behind sequences of events, which is essential for rule inference, future prediction, and decision making (Balaguer, Spiers, Hassabis, & Summerfield, 2016; Garvert, Dolan, & Behrens, 2017; Garvert, Saanum, Schulz, Schuck, & Doeller, 2021; Pudhiyidath et al, 2021; Pudhiyidath, Roomea, Coughlina, Nguyena, & Preston, 2019). For example, after watching several scenes in a movie containing various character combinations, we could quickly form a structural network in mind that signifies connections among characters, such as families, lovers, friends, enemies, colleagues, etc., which are further attached to make a panoramic relational network.…”

“…Remarkably, humans could not only track transition probabilities, but also infer new abstract (higher-order) statistical structures from event sequences (Garvert et al, 2021; Mark et al, 2020). For example, humans tend to group events into clusters or hierarchical trees, which bias their subsequent reasonings (Balaguer et al, 2016; Pudhiyidath et al, 2021).…”