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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

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

Haopeng Chen¹, Cristian Buc Calderon², Tom Verguts¹; ¹Universiteit Gent, ²Centro Nacional de Inteligencia Artificial

Presenter: Haopeng Chen

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 multi-level 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 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.

Topic Area: Memory, Spatial Cognition & Skill Learning

Extended Abstract: Full Text PDF