Tag Archives: Statistical Learning In Psychology

On how children learn with progressive environment changes aimed at improving their learning statistically

Linda B. Smith, Swapnaa Jayaraman, Elizabeth Clerkin, Chen Yu, The Developing Infant Creates a Curriculum for Statistical Learning, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 325-336, DOI: 10.1016/j.tics.2018.02.004.

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning.

On how the human cognition detects regularities in noisy sensory data (“Statistical learning” in psychology terms)

Annabelle Goujon, André Didierjean, Simon Thorpe, Investigating implicit statistical learning mechanisms through contextual cueing, Trends in Cognitive Sciences, Volume 19, Issue 9, September 2015, Pages 524-533, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.07.009.

Since its inception, the contextual cueing (CC) paradigm has generated considerable interest in various fields of cognitive sciences because it constitutes an elegant approach to understanding how statistical learning (SL) mechanisms can detect contextual regularities during a visual search. In this article we review and discuss five aspects of CC: (i) the implicit nature of learning, (ii) the mechanisms involved in CC, (iii) the mediating factors affecting CC, (iv) the generalization of CC phenomena, and (v) the dissociation between implicit and explicit CC phenomena. The findings suggest that implicit SL is an inherent component of ongoing processing which operates through clustering, associative, and reinforcement processes at various levels of sensory-motor processing, and might result from simple spike-timing-dependent plasticity.