Bidirectional influences of information sampling and concept learning

A Distinct Neural Code Supports Prospection of Future Probabilities During Instrumental Information-Seeking

To make adaptive decisions, we must actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain estimates expected information gains (EIG) when comparing the current decision uncertainty with the uncertainty that is expected after gathering information. We examined this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently by prior decision uncertainty, information diagnosticity, and the penalty for an erroneous choice. Consistent with value of information theory, bids were sensitive to EIG and its components of prior certainty and expected posterior certainty. Expected posterior certainty was decoded above chance from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation was independent of instrumental rewards and overlapped with distinct representations of EIG and prior certainty. Thus, posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to estimate EIG during active information gathering.

Inefficient prioritization of task-relevant attributes during instrumental information demand

In natural settings, people evaluate complex multi-attribute situations and decide which attribute to request information about. Little is known about how people make this selection and specifically, how they identify individual observations that best predict the value of a multi-attribute situation. Here show that, in a simple task of information demand, participants inefficiently query attributes that have high individual value but are relatively uninformative about a total payoff. This inefficiency is robust in two instrumental conditions in which gathering less informative observations leads to significantly lower rewards. Across individuals, variations in the sensitivity to informativeness is associated with personality metrics, showing negative associations with extraversion and thrill seeking and positive associations with stress tolerance and need for cognition. Thus, people select informative queries using sub-optimal strategies that are associated with personality traits and influence consequential choices.

Category structure and region-specific selective attention

A fundamental component of human categorization involves learning to attend selectively to relevant dimensions and ignore irrelevant ones. Past research has shown that humans can learn flexible strategies in which the attended dimensions vary depending on the region of feature space in which classification takes place. However, region-specific selective attention (RSA) is often challenging to learn. Here, we test the hypothesis that RSA is facilitated when individual categories are embedded within single regions of stimulus space rather than dispersed across multiple regions. We conduct an experiment that varies across conditions whether categories are embedded within regions, but in which the same RSA strategy would benefit performance across the conditions. To evaluate the hypothesis, we use measures of overall performance accuracy as well as comparisons among formal computational models that do and do not make allowance for RSA. We find strong support for the hypothesis among the upper-median-performing participants in the tested groups. However, even in the condition that promotes the learning of RSA, performance is considerably worse than in comparison conditions in which a single set of dimensions can be attended throughout the entire stimulus space.

Learning and Representation of Hierarchical Concepts in Hippocampus and Prefrontal Cortex

A key aspect of conceptual knowledge is that it can be flexibly applied at different levels of abstraction, implying a hierarchical organization. It is yet unclear how this hierarchical structure is acquired and represented in the brain. Here we investigate the computations underlying the acquisition and representation of the hierarchical structure of conceptual knowledge in the hippocampal-prefrontal system of 32 human participants (22 females). We assessed the hierarchical nature of learning during a novel tree-like categorization task via computational model comparisons. The winning model allowed to extract and quantify estimates for accumulation and updating of hierarchical compared with single-feature-based concepts from behavior. We find that mPFC tracks accumulation of hierarchical conceptual knowledge over time, and mPFC and hippocampus both support trial-to-trial updating. As a function of those learning parameters, mPFC and hippocampus further show connectivity changes to rostro-lateral PFC, which ultimately represented the hierarchical structure of the concept in the final stages of learning. Our results suggest that mPFC and hippocampus support the integration of accumulated evidence and instantaneous updates into hierarchical concept representations in rostro-lateral PFC.SIGNIFICANCE STATEMENT A hallmark of human cognition is the flexible use of conceptual knowledge at different levels of abstraction, ranging from a coarse category level to a fine-grained subcategory level. While previous work probed the representational geometry of long-term category knowledge, it is unclear how this hierarchical structure inherent to conceptual knowledge is acquired and represented. By combining a novel hierarchical concept learning task with computational modeling of categorization behavior and concurrent fMRI, we differentiate the roles of key concept learning regions in hippocampus and PFC in learning computations and the representation of a hierarchical category structure.