Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities

Abstract

What determines individuals’ efficacy in detecting regularities in visual statistical learning? Our theoretical starting point assumes that the variance in performance of statistical learning (SL) can be split into the variance related to efficiency in encoding representations within a modality and the variance related to the relative computational efficiency of detecting the distributional properties of the encoded representations. Using a novel methodology, we dissociated encoding from higher-order learning factors, by independently manipulating exposure duration and transitional probabilities in a stream of visual shapes. Our results show that the encoding of shapes and the retrieving of their transitional probabilities are not independent and additive processes, but interact to jointly determine SL performance. The theoretical implications of these findings for a mechanistic explanation of SL are discussed.

Online neural monitoring of statistical learning

“…Binding of temporal or spatial contingencies may occur in both modality-specific brain areas (such as higher-level visual areas for visual stimuli, and higher-level auditory areas for auditory stimuli) as well as domain-general areas that are involved regardless of the stimulus modality (such as the medial temporal lobe system). A recent study provided support for this model, showing that these two factors can be dissociated and that they interact to jointly determine statistical learning performance (Bogaerts et al, 2016). This study also demonstrated that these two mechanisms are not independent or additive, but interact with one another.…”

“…This study also demonstrated that these two mechanisms are not independent or additive, but interact with one another. For example, sensitivity to the distributional properties of input can facilitate encoding of individual elements, and conversely, better encoding of elements can enhance the extraction of underlying statistics (Bogaerts et al, 2016). In the context of the present study, both of these mechanisms—encoding and binding—should presumably lead to changes at the perceptual level and influence the WLI in the structured condition.…”

Towards a theory of individual differences in statistical learning

“…Considering the computation of regularities, we note that participants first have to perceive and encode the individual elements of the stream (factor A). As individuals substantially differ in the resolution of their perceptual system, their differential ability to generate perceptual representations under specific exposure constraints, would inevitably contribute to the variance of performance in the subsequent test phase (see also [11,45]). While encoding the individual events, participants further have to discover the statistical regularities in the stream (factor B), which is, in our present context, the most central factor for SL research.…”

Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

“…For if two learning conditions result in similar score in the post-familiarization test-score, they are implicitly taken to be equal in terms of the complexity they impose on participants, with all resulting theoretical implication (e.g., Arciuli, von Koss Torkildsen, Stevens, & Simpson, 2014). In contrast, if they result in different test scores, the magnitude of the test-score difference is taken to represent the difference in complexity between condition possibly suggesting different mechanisms (e.g., Bogaerts, Siegelman, & Frost, 2016). Are these implicit assumptions necessarily true?…”