Beyond error-driven adaptation: proactive validation as a goal-directed mechanism of language processing and learning

Recent advances in psycholinguistics increasingly frame language processing as a predictive process: listeners and readers continuously anticipate upcoming linguistic input. Deviations from those expectations—prediction errors—are assumed to stimulate both moment-by-moment processing and long-term learning. While highly influential, this view implicitly assumes that the adaptation is driven by discrepancies. Such an approach overlooks a crucial aspect of rational human behavior: Rational agents generally act to avoid failure, not to repeatedly learn from it. In the current perspective paper, I review the evidence for prediction error minimization as a proactive, preemptive (rather than reactive repair) mechanism of language processing. Rather than reacting after a mismatch, language users will accumulate evidence by maintaining and validating less probable parses to reduce a risk of failure. By proposing proactive validation as a proactive, goal-directed mechanism, the paper seeks to complement rational models of predictive processing by shifting the temporal and mechanistic focus from post-hoc long-term statistical adjustment to anticipatory optimization. This framework provides a unified explanation for cross-linguistic and developmental variability in processing difficulty, such as reduced processing cost for non-canonical structures in morphologically rich languages and the gradual shift from reactive to proactive strategies in language learners. On a broader scale, anticipatory validation can explain why the comprehension system tolerates ambiguity and maintains suboptimal parses—not to correct errors retrospectively, but to pre-empt them prospectively.