Building concepts one episode at a time: The hippocampus and concept formation

Abstract

Concepts organize our experiences and allow for meaningful inferences in novel situations. Acquiring new concepts requires extracting regularities across multiple learning experiences, a process formalized in mathematical models of learning. These models posit a computational framework that has increasingly aligned with the expanding repertoire of functions associated with the hippocampus. Here, we propose the Episodes-to-Concepts (EpCon) theoretical model of hippocampal function in concept learning and review evidence for the hippocampal computations that support concept formation including memory integration, attentional biasing, and memory-based prediction error. We focus on recent studies that have directly assessed the hippocampal role in concept learning with an innovative approach that combines computational modeling and sophisticated neuroimaging measures. Collectively, this work suggests that the hippocampus does much more than encode individual episodes; rather, it adaptively transforms initially-encoded episodic memories into organized conceptual knowledge that drives novel behavior.

Keywords: Attention; Computational modeling; Concept learning; Episodic memory; Hippocampus; Prediction error.

Copyright © 2017 Elsevier B.V. All rights reserved.

Figures

Figure 1

The Episodes-to-Concepts (EpCon) theoretical model of concept formation in the hippocampus. Initially, each new learning experience consisting of stimulus features (e.g., dotted outline, red fill, and vertical center) and concept label (e.g., B) is encoded as a distinct memory (dotted blue lines represent hippocampal encoding). After encoding these initial experiences, memory integration processes soon dominate learning: Pattern completion processes retrieve related memories (solid blue lines depict hippocampal retrieval) that are used to predict a concept label. Feedback then leads to integration across experiences (e.g., red items with dotted outlines are associated with concept B) and/or distinct representation of the current experience through pattern separation. Concept formation continues as learning progresses, with more complex integrated representations that span experiences retrieved through pattern completion and encoded through memory integration. This adaptive process culminates in conceptual coding in which the learned integrated representations capture the structure of the concept. Brain illustration by Margaret Schlichting.

Figure 2

Associative inference paradigm and RSA results from Schlichting et al. [32]. Participants learned direct associations (AB and BC) before being tested on an indirect inference (AC). Participants were cued with a C object and selected the indirectly associated A object (circled object). RSA measures showed evidence of integrated representations (i.e., increased similarity between A and C objects post- versus pre-learning) in left anterior hippocampus. Figure adapted from [32].

Figure 3

SUSTAIN-based measures of concept formation during a rule-plus-exception category learning task [12] and corresponding statistical maps of the hippocampus. A) Recognition strength varies across learning trials and is greater for exception (red) versus rule-following (green) items. Trial-by-trial activation in bilateral hippocampus (red regions) correlated with recognition strength. B) Error correction correlated with activation in bilateral hippocampus (yellow regions) during learning trial feedback. Figure adapted from [12].

Figure 4

Mack et al. [14] learning problem schematics, model predictions, and corresponding neural results. A) Participants learned to classify the same set of multidimensional objects (beetles with different legs, antennae, and mandibles) according to two different learning problems. B) SUSTAIN-based predictions of the similarity between object representations in the two problems. Lighter cells correspond to higher similarity. C) Neural representations in left anterior hippocampus corresponded with the conceptual reorganization between learning problems as predicted by SUSTAIN. Figure adapted from [14].