Seeing and hearing in space and time: Effects of modality and presentation rate on implicit statistical learning

Conway, Christopher M.; Christiansen, Morten H.

doi:10.1080/09541440802097951

Cited by 85 publications

(110 citation statements)

References 54 publications

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“…[102]). Consider as an example the demonstration that rate of presentation has differential effects in auditory and visual statistical learning [103]. One possibility is that this differential effect arises because there are different processes underlying visual and audio statistical learning.…”

Section: Challenges For a Memory-based Perspectivementioning

confidence: 99%

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Thiessen¹

2017

Phil. Trans. R. Soc. B

104

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One contribution of 13 to a theme issue 'New frontiers for statistical learning in the cognitive sciences'. . The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting

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Section: Challenges For a Memory-based Perspectivementioning

confidence: 99%

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Thiessen¹

2017

Phil. Trans. R. Soc. B

104

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“…[64]), or, relatedly, unsupervised versus supervised learning settings (see [65] for a discussion of the role of feedback in perceptual category learning). An additional factor that was shown to affect SL performance is the rate of presentation-with opposite effects of both the inter stimulus interval and the actual stimulus duration on SL performance in the visual versus auditory modality ( [35,36]; but see [66]). Whether rate of presentation constitutes a separate facet, or simply affects peripheral aspects to SL such as the encoding of individual elements, with different constraints in different modalities (see [11]), deserves further investigation.…”

Section: (A) Modality Specificitymentioning

confidence: 99%

“…[6]), and that statistical contingencies are extracted across both time and space (e.g. [8], though with different biases; see [35]). Admittedly, to date there is little unequivocal evidence showing that these phenomena are governed by non-overlapping computations and necessarily result in different learning constraints.…”

Section: (A) Modality Specificitymentioning

confidence: 99%

Towards a theory of individual differences in statistical learning

Siegelman

Bogaerts

Christiansen

et al. 2017

Phil. Trans. R. Soc. B

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182

199

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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'.

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“…Consequently, insufficient evidence exists to attribute the lack of correlation to psychometric properties, task demands, or to potentially different processes underlying SL as a function of adjacency. Similarly, consider demonstrations that visual SL shows different learning constraints than audio SL: the optimal rate of presentation across these modalities is quite distinct, such that the visual modality requires a slower rate of presentation than the auditory modality for best learning [43,44]. This may be due to the fact that there are different processes underlying audio and visual SL.…”

mentioning

confidence: 99%

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

et al. 2016

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The ability to adapt to statistical structure (often referred to as "statistical learning") has been proposed to play a major role in the acquisition and use of natural languages. Several recent studies have explored the relationship between individual differences in statistical learning and language outcomes. These studies have produced mixed results, with some studies finding a significant relationship between statistical learning and language outcomes, and others finding weak or null results. Furthermore, the few studies that have used multiple measures of statistical learning have reported that they are not correlated (e.g., [1]). The current study assesses the reliability of various measures of auditory statistical segmentation, and their consistency over time. That is, do the generally low correlations observed between measures of statistical learning stem from task demands, the psychometric properties of the measures, or the fact that statistical learning may be a highly fragmented construct? Our results confirm previous reports that individual measures of statistical learning tend not to correlate with each other, and suggest that the somewhat weak reliability of the measures may be an important factor in the low correlations. Our data also suggest that aggregating performance across tasks may be an avenue for improving the reliability of the measures.

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Seeing and hearing in space and time: Effects of modality and presentation rate on implicit statistical learning

Cited by 85 publications

References 54 publications

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Towards a theory of individual differences in statistical learning

Individual Differences in Statistical Learning: Conceptual and Measurement Issues

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