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

By Michael L. Mack, Bradley C. Love, Alison R. Preston. 2017

166 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Ali Ayub
2. Alan R. Wagner
3. Dagmar Zeithamova
4. M. V. van Kesteren

In Cognitive Neuroscience

1. Ali Ayub
2. Alan R. Wagner
3. Dagmar Zeithamova
4. Michael L. Mack
5. M. V. van Kesteren

In Neurology

1. Ali Ayub
2. Alan R. Wagner
3. Dagmar Zeithamova

Citations in the corpus

• EEC: Learning to Encode and Regenerate Images for Continual Learning (2021, methodology): “For efficient memory management, we use the notion of memory integration, from hippocampal and neocortical concept learning (Mack et al., 2018), to combine similar episodes into centroids and covariance matrices eliminating the need to store real data.”
• Storing Encoded Episodes as Concepts for Continual Learning (2020, methodology): “To cope with this issue, we propose a process inspired by memory integration in the hippocampus and the neocortex (Mack et al., 2018; Renoult et al., 2015; Moscovitch et al., 2016).”
• Cognitively-Inspired Model for Incremental Learning Using a Few Examples (2020, methodology): “In the hippocampal concept learning model [29, 40, 33], after the feature extraction step, the hippocampus calculates a term called the memory-based prediction error.”
• Brain Mechanisms of Concept Learning (2019, background): “Work on the medial temporal lobes has focused on how functions ascribed to the hippocampus, such as pattern completion, pattern separation, and memory integration, can be applied to concept learning and categorization (Mack et al., 2018).”
• Brain-Inspired Model for Incremental Learning Using a Few Examples (2020, methodology): “Inspired by the EpCon model for concept learning in hippocampus [28], Agg-Var clustering begins by creating one centroid from the first image in the training set of class y.”
• Centroid Based Concept Learning for RGB-D Indoor Scene Classification (2019, background): “CBCL is cognitively inspired from concept learning models of the hippocampus and the neo-cortex [14, 16, 18].”