The N100 Predicts Learning Performance of Visual Statistical Learning

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Publication statusPublished - Apr 2022

Conference

Title29th Annual Meeting of Cognitive Neuroscience Society (CNS 2022)
PlaceUnited States
CitySan Francisco
Period23 - 26 April 2022

Abstract

Extracting the statistical regularities in the environment over time and/or space is essential for many domains of cognition, including perception and language. Statistical learning (SL) is considered a domain-general learning principle, with previous behavioral studies demonstrating the association between SL and a variety of cognitive functions across modalities and species. However, the underlying neural process of acquiring statistical regularities under unsupervised learning has not been fully clarified. In the current study, we investigate the possible psychophysiological signatures sensitive to the extraction of temporal statistical regularities and the indication of hemispheric asymmetry in visual statistical learning. Young adults were asked to perform judgments of adjacent and nonadjacent dependencies items presented in visual half-field manipulation with event-related potentials (ERPs) recording after they were exposed to a continuous stream of nonsense shapes. The N100 (110 - 170 ms after onset time) at frontal-to-central regions elicited a larger negative activity for the final shape in both target and foil triplets when responded correctly. Also, there was a significant interaction between visual field (RVF-LVF), condition (target-foil) and response (correct-incorrect) on left electrodes (F3, FC3, C3). Our results reveal that the early time window linked to attention and sensory information processing is likely to reflect the learning performance of individuals, with a tendency of the left hemispheric effect. Visual statistical learning may impact the early processing of perceptual information. These findings provide evidence on the neurocognitive mechanisms associated with extracting patterns of regularity under visual statistical learning.

Research Area(s)

  • statistical learning, event-related potential, N100

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.

Citation Format(s)

The N100 Predicts Learning Performance of Visual Statistical Learning. / Mak, Hoi Yan; Lin, Qiduo; Li, Bing; Tzeng, Ovid J. L.; Huang, Hsu-Wen.

2022. Paper presented at 29th Annual Meeting of Cognitive Neuroscience Society (CNS 2022), San Francisco, California, United States.

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review