An Exploration of Individual Differences in Statistical Learning

2014 ◽  
Author(s):  
Lucy C. Erickson ◽  
Michael Kaschak ◽  
Erik D. Thiessen ◽  
Cassie Berry
Keyword(s):  
Individual Differences ◽  
Statistical Learning

scholarly journals Information-theoretic Modeling of Perceived Musical Complexity

2019 ◽  
Vol 37 (2) ◽  
pp. 165-178
Author(s):  
Sarah A. Sauvé ◽  
Marcus T. Pearce
Keyword(s):  
Individual Differences ◽  
Computational Model ◽  
Information Content ◽  
Statistical Learning ◽  
Probabilistic Prediction ◽  
Information Theoretic ◽  
Musical Background ◽  
Musical Excerpts ◽  
Definition Of ◽  
Perceived Complexity

What makes a piece of music appear complex to a listener? This research extends previous work by Eerola (2016), examining information content generated by a computational model of auditory expectation (IDyOM) based on statistical learning and probabilistic prediction as an empirical definition of perceived musical complexity. We systematically manipulated the melody, rhythm, and harmony of short polyphonic musical excerpts using the model to ensure that these manipulations systematically varied information content in the intended direction. Complexity ratings collected from 28 participants were found to positively correlate most strongly with melodic and harmonic information content, which corresponded to descriptive musical features such as the proportion of out-of-key notes and tonal ambiguity. When individual differences were considered, these explained more variance than the manipulated predictors. Musical background was not a significant predictor of complexity ratings. The results support information content, as implemented by IDyOM, as an information-theoretic measure of complexity as well as extending IDyOM's range of applications to perceived complexity.

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

2018 ◽  
Author(s):  
Amy Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Learning Skills ◽  
Domain Specific ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in a variety 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. We show that 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 different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency which contains no statistical learning component at all. Our results suggest that a key component of SL performance may be unrelated to either domain-specific statistical learning skills or modality-specific perceptual processing.

scholarly journals Match me if you can: Evidence for a domain-general visual comparison ability

2022 ◽  
Author(s):  
Bethany Growns ◽  
James D. Dunn ◽  
Erwin J. A. T. Mattijssen ◽  
Adele Quigley-McBride ◽  
Alice Towler
Keyword(s):  
Individual Differences ◽  
Visual Search ◽  
Statistical Learning ◽  
Real World ◽  
Discrimination Task ◽  
Psychological Processes ◽  
Substantial Portion ◽  
Visual Comparison ◽  
Perceptual Ability ◽  
Forensic Scientists

AbstractVisual comparison—comparing visual stimuli (e.g., fingerprints) side by side and determining whether they originate from the same or different source (i.e., “match”)—is a complex discrimination task involving many cognitive and perceptual processes. Despite the real-world consequences of this task, which is often conducted by forensic scientists, little is understood about the psychological processes underpinning this ability. There are substantial individual differences in visual comparison accuracy amongst both professionals and novices. The source of this variation is unknown, but may reflect a domain-general and naturally varying perceptual ability. Here, we investigate this by comparing individual differences (N = 248 across two studies) in four visual comparison domains: faces, fingerprints, firearms, and artificial prints. Accuracy on all comparison tasks was significantly correlated and accounted for a substantial portion of variance (e.g., 42% in Exp. 1) in performance across all tasks. Importantly, this relationship cannot be attributed to participants’ intrinsic motivation or skill in other visual-perceptual tasks (visual search and visual statistical learning). This paper provides novel evidence of a reliable, domain-general visual comparison ability.

scholarly journals The multi-component nature of statistical learning

2017 ◽  
Vol 372 (1711) ◽  
pp. 20160058 ◽  
Author(s):  
Joanne Arciuli
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Language Processing ◽  
Autism Spectrum ◽  
Future Studies ◽  
Multiple Components ◽  
Age Related ◽  
Infancy And Childhood ◽  
Central Argument ◽  
Spectrum Disorders

The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

scholarly journals Towards a theory of individual differences in statistical learning

2017 ◽  
Vol 372 (1711) ◽  
pp. 20160059 ◽  
Author(s):  
Noam Siegelman ◽  
Louisa Bogaerts ◽  
Morten H. Christiansen ◽  
Ram Frost
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Experimental Approach ◽  
Methodological Approach ◽  
Individual Performance ◽  
Forced Choice ◽  
Choice Test ◽  
Cognitive Sciences ◽  
Choice Tests ◽  
Good Proxy

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 informational-specificity, 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’.

scholarly journals Facial Stereotyping Drives Judgments of Perceptually Ambiguous Social Groups

2021 ◽  
pp. 194855062110622
Author(s):  
Maryam Bin Meshar ◽  
Ryan M. Stolier ◽  
Jonathan B. Freeman
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Social Groups ◽  
Causal Role ◽  
Facial Features ◽  
Facial Appearance ◽  
Social Categories ◽  
Social Encounters

When seeing a face, people form judgments of perceptually ambiguous social categories (PASCs), for example, gun-owners, gay people, or alcoholics. Previous research has assumed that PASC judgments arise from the statistical learning of facial features in social encounters. We propose, instead, that perceivers associate facial features with traits (e.g., extroverted) and then infer PASC membership via learned stereotype associations with those traits. Across three studies, we show that when any PASC is more stereotypically associated with a trait (e.g., alcoholics = extroverted), perceivers are more likely to infer PASC membership from faces conveying that trait (Study 1). Furthermore, we demonstrate that individual differences in trait–PASC stereotypes predict face-based judgments of PASC membership (Study 2) and have a causal role in these judgments (Study 3). Together, our findings imply that people can form any number of PASC judgments from facial appearance alone by drawing on their learned social–conceptual associations.

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