# Neurocomputational modeling of rule abstraction and memorization during probabilistic stimulus-reward learning

## TL;DR

This study explores how people use rule abstraction or memorization to make choices based on rewards, using brain scans and eye-tracking.

## Contribution

The study introduces a novel cognitive model (CAL) that links behavioral and neural data to differentiate rule-based and memory-based learning strategies.

## Key findings

- Rule abstraction is associated with sudden insights and distinct brain activity patterns.
- Memorization involves gradual learning and different neural networks.
- The CAL model successfully captures both strategies using behavior and eye-tracking data.

## Abstract

Preferential choice among multi-attribute stimuli commonly involves one of the two learning strategies: rule abstraction and memorization. When stimulus features combine in a systematic, albeit complex way to predict rewarding or punishing outcomes (e.g., a combination of color and shape distinguishes edible from poisonous mushrooms), corresponding learning problems can be solved via rule abstraction. In other problems lacking this systematicity, stimuli have to be memorized individually. Here, we use fMRI, eye-tracking, and cognitive modeling to study how humans deploy these two learning strategies to select between two simultaneously presented objects. We observed differential learning trajectories and fixation patterns, indicating sudden rule discovery and incremental learning, respectively, captured by cognitive modeling. The derived process estimates allowed us to identify overlapping brain networks associated with cognitive control and value-based decision-making. Importantly, our multi-modal data and model-informed analyses link those processes to unique brain regions, revealing the neurocognitive mechanisms of rule abstraction and memorization.

•Value-based choice uses rule abstraction or memorization of stimuli•Rule learning entails sudden insight, while memorization shows gradual learning•The CAL model captures both via behavior and eye-tracking attention•FMRI links each strategy to distinct control, decision, and memory networks

Value-based choice uses rule abstraction or memorization of stimuli

Rule learning entails sudden insight, while memorization shows gradual learning

The CAL model captures both via behavior and eye-tracking attention

FMRI links each strategy to distinct control, decision, and memory networks

cognitive neuroscience and psychology