J Exp Psychol Learn Mem Cogn. 2026 Mar 19. doi: 10.1037/xlm0001589. Online ahead of print.
ABSTRACT
Statistical learning is the process of extracting regularities from the environment. Although sequential cross-modal regularities are ubiquitous (e.g., the sight of a falling ball followed by the sound of impact), previous research has failed to demonstrate that such learning occurs between sensory modalities. This has led to the view that sequential statistical learning is modality specific. The present study investigates factors that may determine statistical learning of cross-modal sequences. In the first experiment, participants viewed a stream of meaningless visual fractals and synthetic auditory stimuli. The sequence of stimuli could be grouped into either unimodal or cross-modal pairs based on their transitional probabilities. Using implicit and explicit measures of learning, we found that participants learned only the unimodal pairs. In the second experiment, pairs were presented in separate unimodal and cross-modal blocks. The cross-modal blocks alternated between visual and auditory modalities, allowing participants to anticipate the upcoming modality. This manipulation enabled significant implicit statistical learning for the cross-modal pairs. This suggests that the predictability of modality transitions facilitates appropriate deployment of attention across sensory modalities, which is crucial for learning cross-modal sequential contingencies. In the third experiment, we used audiovisual stimuli with semantic content. Here, participants were able to implicitly learn and explicitly recognize statistical regularities between cross-modal pairs even when the upcoming modality was unpredictable. Together, these findings challenge the view that sequential statistical learning is strictly modality specific, showing that it occurs when sensory-level limitations are bypassed by attentional cues or when learning engages higher level semantic representations shared across modalities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
PMID:41854691 | DOI:10.1037/xlm0001589
