Small cognitive core model predicts human choices across many experiments
A small cognitive core explains human behavior at scale
Machine Learning
Summary
People’s thinking and decisions are complex, but many ideas about how we think have been studied separately. The authors combined a few well-known ideas into one simple model called CORE that uses memory and context to predict choices. They tested this model with over 7 million decisions from many experiments and found it predicted behavior well and matched many known psychological patterns. This suggests a small set of basic processes might explain a large part of how people behave.
What this means in practice
- •For user experience designers: Improve interface designs by anticipating user choices based on a unified model of memory and context effects.
- •For adaptive learning developers: Create personalized learning systems that use cognitive core predictions to tailor content timing and retrieval cues for better retention.
Authors
Marcel Binz, Eric Schulz
Abstract
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.