The long road of statistical learning research: past, present and future

Armstrong, Blair C.; Frost, Ram; Christiansen, Morten H.

doi:10.1098/rstb.2016.0047

Cited by 66 publications

(49 citation statements)

References 27 publications

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“…Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment enabling predictive processing during perception and acquisition of perceptual, motor, cognitive, and social skills (Armstrong, Frost & Christiansen, 2017;Aslin, 2017;Cleeremans & McClelland, 1991;Reber, 1967). Learning statistical probabilities of environmental stimuli induces structural and functional plasticity in the nervous system (Fiser et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of EEG functional connectivity during statistical learning

Tóth

Janacsek

Takács

et al. 2017

Neurobiology of Learning and Memory

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Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning.

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Section: Introductionmentioning

confidence: 99%

Dynamics of EEG functional connectivity during statistical learning

Tóth

Janacsek

Takács

et al. 2017

Neurobiology of Learning and Memory

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“…One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks (see Armstrong, Frost, & Christiansen, 2017, for discussion). Typically, the to-be-learned regularities (i.e., co-occurrence of elements, their transitional probabilities, etc.)…”

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

et al. 2017

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From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.

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“…Statistical learning has been proposed as a mechanism for learning complex behavioral sequences, including speech and language acquisition [11][12][13][14] . In particular, young infants demonstrate a remarkable ability to distinguish auditory sequences based on the transition probabilities between various acoustic elements, and this learning is thought to scaffold subsequent word and sentence learning 15,16 .…”

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Statistical learning for vocal sequence acquisition in a songbird

James

Sun

Wada

et al. 2020

Sci Rep

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Birdsong is a learned communicative behavior that consists of discrete acoustic elements ("syllables") that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. Statistical learning could contribute to the acquisition of vocal sequences, and we investigated the nature and extent of sequence learning at various levels of song organization in the Bengalese finch, Lonchura striata var. domestica. We found that, under semi-natural conditions, pupils (sons) significantly reproduced the sequence statistics of their tutor's (father's) songs at multiple levels of organization (e.g., syllable repertoire, prevalence, and transitions). For example, the probability of syllable transitions at "branch points" (relatively complex sequences that are followed by multiple types of transitions) were significantly correlated between the songs of tutors and pupils. We confirmed the contribution of learning to sequence similarities between fathers and sons by experimentally tutoring juvenile Bengalese finches with the songs of unrelated tutors. We also discovered that the extent and fidelity of sequence similarities between tutors and pupils were significantly predicted by the prevalence of sequences in the tutor's song and that distinct types of sequence modifications (e.g., syllable additions or deletions) followed distinct patterns. Taken together, these data provide compelling support for the role of statistical learning in vocal production learning and identify factors that could modulate the extent of vocal sequence learning.www.nature.com/scientificreports www.nature.com/scientificreports/ syllables. Some studies using experimental tutoring have confirmed the developmental acquisition of vocal sequencing 27-31 , but most of such studies have been conducted on species that produce stereotyped, relatively simple sequences of syllables (e.g., white-crowned sparrows and zebra finches) 26 . Understanding sequence learning in songbird species that produce more complex and variable sequencing is important not only for comparative reasons but also because such species produce syntactic structures that more closely resemble the complexity of syntax in human language 14 .The Bengalese finch (Lonchura striata var. domestica) offers an excellent opportunity to study the acquisition of relatively complex vocal sequences 32 . The songs of individual Bengalese finches consist of a discrete, learned repertoire of syllables that are arranged both in highly stereotyped sequences ("motifs" or "chunks") and in sequences that vary from rendition to rendition ("branch points"; Fig. 1) 26,[33][34][35][36][37][38][39][40] . Previous studies have provided some insight into vocal sequence learning in Bengalese finches by comparing songs between tutors and pupils 41-43 ; for example, Yamashita et al. (2008) analyzed the distribution of three-syllable sequences across two points in an individual's song development and...

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The long road of statistical learning research: past, present and future

Abstract: One contribution of 13 to a theme issue 'New frontiers for statistical learning in the cognitive sciences'.

Cited by 66 publications

References 27 publications

Dynamics of EEG functional connectivity during statistical learning

Dynamics of EEG functional connectivity during statistical learning

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

Statistical learning for vocal sequence acquisition in a songbird

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