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This paper introduces COntextual Retrieval and Encoding (CORE), a computational model integrating established cognitive principles into a hierarchical episodic memory system where prediction errors are encoded and dynamically managed. Fitted to a large-scale dataset of human choices across 137 experiments, CORE surpasses black-box models in predictive accuracy and reproduces a significant majority of observed behavioral effects. The model's latent dynamics exhibit Bayesian learning and event segmentation, suggesting a parsimonious cognitive architecture underlies diverse human behaviors.
A unified cognitive architecture, built from just a few core principles, predicts human choices across thousands of experiments and reproduces 75% of known behavioral effects.
Psychology has proposed many principles of cognition. Each, however, was developed in isolation, leaving their combined explanatory power unknown. We assembled a small set of established principles into one model, which we call COntextual Retrieval and Encoding (CORE). CORE consists of a hierarchy of episodic memories into which prediction errors are written and whose encoding, retrieval, and forgetting depend on context. Fitted jointly to 7,036,442 choices from 137 experiments, the model predicts human choices better than black-box models and reproduces 75% of reported behavioral effects when simulated. Moreover, its latent variables show classic signatures of Bayesian learning and event segmentation. Taken together, these findings suggest that a small cognitive core accounts for much of human behavior. They also open a path toward making psychology more than the sum of its parts, by building computational models on the entire experimental record.