Modality and Stimulus Effects on Distributional Statistical Learning: Sound vs. Sight, Time vs. Space

Haoyu Zhou, Sabine van der Ham, Bart De Boer, Louisa Bogaerts, Limor Raviv

Research output: Contribution to journalArticlepeer-review


Statistical learning (SL) is postulated to play an important role in the process of language acquisition as well as in other cognitive functions. It was found to enable learning of various types of statistical patterns across different sensory modalities. However, few studies have distinguished distributional SL (DSL) from sequential and spatial SL, or examined DSL across modalities using comparable tasks. Considering the relevance of such findings to the nature of SL, the current study investigated the modality- and stimulus-specificity of DSL. Using a within-subject design we compared DSL performance in auditory and visual modalities. For each sensory modality, two stimulus types were used: linguistic versus non-linguistic auditory stimuli and temporal versus spatial visual stimuli. In each condition, participants were exposed to stimuli that varied in their length as they were drawn from two categories (short versus long). DSL was assessed using a categorization task and a production task. Results showed that learners’ performance was only correlated for tasks in the same sensory modality. Moreover, participants were better at categorizing the temporal signals in the auditory conditions than in the visual condition, where in turn an advantage of the spatial condition was observed. In the production task participants exaggerated signal length more for linguistic signals than non-linguistic signals. Together, these findings suggest that DSL is modality- and stimulus-sensitive.

Original languageEnglish
Article number104531
Journaljournal of memory and language
Publication statusPublished - Oct 2024

Bibliographical note

Funding Information:
Haoyu Zhou and Louisa Bogaerts received funding within the framework of the Odysseus programme from the Research Foundation Flanders, Belgium (FWO; project number: G0F3121N). Sabine van der Ham and Bart de Boer were funded by the ERC project ABACUS (grant number 283435). Furthermore, Bart de Boer received funding from the Flemish Government under the \u201COnderzoeksprogramma Artifici\u00EBle Intelligentie (AI) Vlaanderen\u201D programme.

Publisher Copyright:
© 2024


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