The Peak–End Rule and Retrospective Emotional Valence in Digital Learning Tasks: Evidence from a Word-Learning App

The peak–end rule proposes that retrospective evaluations depend on the emotional peak and the end of an experience rather than on its duration. Two short, controlled vocabulary-learning experiments tested whether optimizing these moments improves retrospective emotional valence. Study 1 (N = 32) manipulated task length (4 vs. 8 words). Retrospective emotional valence did not differ significantly between groups (p = 0.459, d = 0.27), a result consistent with duration neglect under this short task–episode manipulation but not a strong test of pure temporal duration neglect. Retrospective emotional valence correlated more strongly with the peak–end mean than with the mean of reconstructed page-level ratings (r = 0.761 vs. r = 0.314; Steiger’s Z = 3.03, p = 0.002). Study 2 (N = 56) used a 2 × 2 design to optimize the candidate peak-related completion page and the structurally defined end check-in page through color and anthropomorphic graphics. Both peak (ηp2 = 0.18) and end (ηp2 = 0.22) optimization enhanced retrospective emotional valence, with a significant non-additive interaction (ηp2 = 0.09): the effect of optimizing one node was reduced when the other node had already been optimized. For learning accuracy, the main effect of peak optimization was significant (F(1, 52) = 4.44, p = 0.040), but only the combined peak-and-end optimization significantly outperformed the control condition (p = 0.041, d = 1.11); neither single-optimization condition significantly differed from the control condition after correction. The findings provide preliminary evidence for a peak–end-consistent evaluation pattern in brief, controlled vocabulary-learning tasks, identify a non-additive interaction in peak–end optimization, and offer guidance for designing key interactive moments within similarly short, task-based learning episodes.