RT Journal Article
SR Electronic
T1 On the automaticity of visual statistical learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.07.04.498716
DO 10.1101/2022.07.04.498716
A1 Kevin D. Himberger
A1 Amy S. Finn
A1 Christopher J. Honey
YR 2022
UL http://biorxiv.org/content/early/2022/07/06/2022.07.04.498716.abstract
AB Humans can extract regularities from their environment, enabling them to recognize and predict sequences of events. The process of regularity extraction is called ‘statistical learning’ and is generally thought to occur rapidly and automatically; that is, regularities are extracted from repeated stimulus presentations, without intent or awareness, as long as the stimuli are attended. We hypothesized that visual statistical learning is not entirely automatic, even when stimuli are attended, and that the learning depends on the extent to which viewers process the relationships between stimuli. To test this, we measured statistical learning performance across seven conditions in which participants (N=774) viewed image sequences. As task instructions across conditions increasingly required participants to attend to relationships between stimuli, their learning performance increased from chance to robust levels. We conclude that the learning observed in visual statistical learning paradigms is, for the most part, not automatic and requires more than passively attending to stimuli.Competing Interest StatementThe authors have declared no competing interest.