Learning from Mistakes: Objective and Subjective Error Magnitudes Predict the Testing Effect

The testing effect—where retrieval practice enhances memory more than restudy—remains mechanistically debated. We advance an error-driven learning (EDL) account, proposing that testing enhances memory by generating error signals that drive learning. Across two experiments using a color wheel paradigm, which quantified error on a continuous scale, we demonstrated that larger objective errors (OEs) during practice predict greater memory improvement. Experiment 2 further revealed that subjective error signals—predicted (PredEs) and perceived errors (PercEs)—independently shape memory outcomes, even after accounting for OEs. To formalize these dynamics, we developed a computational model (WRAP-E) that simulates distinct memory-strengthening roles for OEs, PredEs, and PercEs across different stages of retrieval. Our findings highlight errors as not merely byproducts of failed recall, but as potent drivers of learning—both through their magnitude and metacognitive interpretation. This framework offers a unified account of the testing effect and carries implications for theories of memory, metacognition, and educational practice.