Neural and computational evidence for a predictive learning account of the testing effect
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
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 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.
Significance Statement
An exam is not a neutral measurement of memory. The testing effect entails that a test (e.g., an exam), is more effective than study for learning and memory. The same effect can be harnessed also before events of significance take place, rendering it an important aspect of an active learning strategy. Nevertheless, its origin remains unknown. We propose a novel predictive learning account, which posits that testing (vs studying) facilitates predictions about study material and promotes learning from prediction errors. Computationally, the testing effect was explained through a predictive-learning-based neural network. Neurally, testing and prediction error activate common neural areas, which in turn enhance declarative memory. This account may extend beyond testing to support active learning.
