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The environment provides multiple regularities that might be useful in guiding behavior if one was able to learn their structure. Understanding statistical learning across simultaneous regularities is important, but poorly understood. We investigate learning across two domains: visuomotor sequence learning through the serial reaction time (SRT) task, and incidental auditory category learning via the systematic multimodal association reaction time (SMART) task. Several commonalities raise the possibility that these two learning phenomena may draw on common cognitive resources and neural networks. In each, participants are unin- formed of the regularities that they come to use to guide actions, the outcomes of which may provide a form of internal feedback. We used dual-task conditions to compare learning of the regularities in isolation versus when they are simultaneously available to support behavior on a seemingly orthogonal visuomotor task. Learning occurred across the simultaneous regularities, without attenuation even when the informational value of a regularity was reduced by the presence of the additional, convergent regularity. Thus, the simultaneous regularities do not compete for associative strength, as in overshadowing effects. Moreover, the visuomotor sequence learning and incidental auditory category learning do not appear to compete for common cognitive resources; learning across the simultaneous regularities was comparable to learning each regularity in isolation.
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This content will become publicly available on February 24, 2026
Statistical learning subserves a higher purpose: Novelty detection in an information foraging system.
Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL ‘captures’ the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language— the domain in which SL theory has been most influential—we review evidence that the environment is far from fixed: it is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed.
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- Award ID(s):
- 2043903
- PAR ID:
- 10586649
- Publisher / Repository:
- American Psychological Association
- Date Published:
- Journal Name:
- Psychological Review
- ISSN:
- 0033-295X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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