What drives individual differences in statistical learning? The role of perceptual fluency and familiarity

Abstract

Humans have the ability to learn surprisingly complicated statistical information in avariety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here we present experimental work suggesting that at least some individual differences arise from variation in perceptual fluency — the ability to rapidly or efficiently code and remember the stimuli that statistical learning occurs over – and that perceptual fluency is driven at least in part by stimulus familiarity: performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, we find that test-retest correlations of performance in a statistical learning task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no statistical learning component at all. Our results suggest that a key component of statistical learning performance may be related to stimulus-specific perceptual processing and familiarity.

Investigating the Cognitive Affective Engagement Model of Learning From Multiple Texts: A Structural Equation Modeling Approach

“…A variety of individual difference factors have been investigated to determine the extent to which different students may be stymied or engaged by the complexity that learning from multiple texts presents. Individual difference factors are a somewhat reliable or stable set of learner characteristics that may be directly assessed, independent of a specific learning task; expected to vary across learners; associated with and distinguishable from one another; and meaningfully related to learners’ processing and performance across a variety of tasks (Friedman & Miyake, 2017; Perfors & Kidd, 2018; Tavor et al, 2016). Individual difference factors examined have included prior knowledge, epistemic beliefs, and argumentative reasoning skills, with these found to result in differences in task performance, both independently and in interaction (Bråten & Strømsø, 2006a; Burin, Barreyro, Saux, & Irrazábal, 2015; Ferguson & Bråten, 2013; Mason, Ariasi, & Boldrin, 2011).…”