Neural and computational evidence for a predictive learning account of the testing effect

• 成果类型: Article
• 署名作者: Chen, Haopeng; Verbeke, Pieter; Mattioni, Stefania; Calderon, Cristian Buc; Verguts, Tom
• 署名单位: Ghent University; HOWEST University of Applied Sciences
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8929
• DOI: 10.1073/pnas.2506530122
• 发表日期: 2025-08-12
• 关键词: peoples hypercorrection; retrieval practice; high-confidence; insular cortex; reward; errors; hippocampus; retention; responses; neurons

摘要

Testing enhances memory more than studying. Although numerous studies have demonstrated the robustness of this classic effect, its neural and computational origin remains debated. Predictive learning is a potential mechanism behind this phenomenon: Because predictions and prediction errors (mismatch between predictions and feedback) are more likely to be generated in testing (relative to in studying), testing can benefit more from predictive learning. We shed light on the testing effect from a multilevel analysis perspective via a combination of cognitive neuroscience experiments (fMRI) and computational modeling. Behaviorally and computationally, only a model incorporating predictive learning can account for the full breadth of behavioral patterns and the robust testing effect. At the neural level, testing and prediction error both activate the canonical reward-related brain areas in the ventral striatum, insula, and midbrain. Crucially, back sorting analysis revealed that activation in the ventral striatum, insula, and midbrain can enhance declarative memory. These results provide strong and converging evidence for a predictive learning account of the testing effect.