Statistical learning is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. Recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal, however, modality and stimulus specificity. An important question is, therefore, how and why a hypothesized domain-general learning mechanism systematically produces such effects. We offer a theoretical framework according to which statistical learning is not a unitary mechanism, but a set of domain-general computational principles, that operate in different modalities and therefore are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

Most research in Statistical Learning (SL) has focused on mean success rate of participants in detecting statistical contingencies at a group level. In recent years, however, researchers show increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most, if not all of this research enterprise employs SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak and sometimes even flawed. In particular, existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetitions of the same targets throughout the test), (2) a large proportion of the sample performs at chance level so that most of the data points reflect noise, and (3) test items following familiarization are all of the same type and identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task, and discuss the importance of the implementation of such tasks in future research.

Although the power of statistical learning (SL) in explaining a wide range of linguistic functions is gaining increasing support, relatively little research has focused on this theoretical construct from the perspective of individual differences. However, to be able to reliably link individual differences in a given ability such as language learning to individual differences in SL, three critical theoretical questions should be posed: Is SL a componential or unified ability? Is it nested within other general cognitive abilities? Is it a stable capacity of an individual? Following an initial mapping sentence outlining the possible dimensions of SL, we employed a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies. SL tasks were administered along with general cognitive tasks in a within-subject design at two time points to explore our theoretical questions. We found that SL, as measured by some tasks, is a stable and reliable capacity of an individual. Moreover, we found SL to be independent of general cognitive abilities such as intelligence or working memory. However, SL is not a unified capacity, so that individual sensitivity to conditional probabilities is not uniform across modalities and stimuli.

One contribution of 13 to a theme issue 'New frontiers for statistical learning in the cognitive sciences'. In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality-and informationalspecificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

We examined whether success (or failure) in assimilating the structure of a second language could be predicted by general statistical learning abilities that are non-linguistic in nature. We employed a visual statistical learning (VSL) task, monitoring our participants’ implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task is not correlated with abilities related to a general G factor or working memory. We found that native speakers of English who picked up the implicit statistical structure embedded in the continuous stream of shapes, on average, better assimilated the Semitic structure of Hebrew words. Our findings thus suggest that languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and these are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment.