scholarly journals Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

2021 ◽  
Author(s):  
Leona Polyanskaya
Keyword(s):  
Statistical Learning ◽  
Associative Memory ◽  
Sensory Input ◽  
Visual Modality ◽  
Conscious Awareness ◽  
Cognitive Mechanisms ◽  
Behavioral Experiments

AbstractTwo classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.

scholarly journals Sensory Processing of Art as a Unique Window into Cognitive Mechanisms: Evidence from Behavioral Experiments and fMRI Studies

2013 ◽  
Vol 86 ◽  
pp. 10-17 ◽  
Author(s):  
Ernst Pöppel ◽  
Mihai Avram ◽  
Yan Bao ◽  
Verena Graupmann ◽  
Evgeny Gutyrchik ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Cognitive Mechanisms ◽  
Behavioral Experiments

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 Statistical learning in songbirds: from self-tutoring to song culture

2017 ◽  
Vol 372 (1711) ◽  
pp. 20160053 ◽  
Author(s):  
Olga Fehér ◽  
Iva Ljubičić ◽  
Kenta Suzuki ◽  
Kazuo Okanoya ◽  
Ofer Tchernichovski
Keyword(s):  
Statistical Learning ◽  
Sensory Input ◽  
Group Level ◽  
Acoustic Features ◽  
Vocal Production ◽  
Cognitive Sciences ◽  
Vocal Behaviour ◽  
Gradual Process ◽  
Phonological Categories ◽  
Micro Level

At the onset of vocal development, both songbirds and humans produce variable vocal babbling with broadly distributed acoustic features. Over development, these vocalizations differentiate into the well-defined, categorical signals that characterize adult vocal behaviour. A broadly distributed signal is ideal for vocal exploration, that is, for matching vocal production to the statistics of the sensory input. The developmental transition to categorical signals is a gradual process during which the vocal output becomes differentiated and stable. But does it require categorical input? We trained juvenile zebra finches with playbacks of their own developing song, produced just a few moments earlier, updated continuously over development. Although the vocalizations of these self-tutored (ST) birds were initially broadly distributed, birds quickly developed categorical signals, as fast as birds that were trained with a categorical, adult song template. By contrast, siblings of those birds that received no training (isolates) developed phonological categories much more slowly and never reached the same level of category differentiation as their ST brothers. Therefore, instead of simply mirroring the statistical properties of their sensory input, songbirds actively transform it into distinct categories. We suggest that the early self-generation of phonological categories facilitates the establishment of vocal culture by making the song easier to transmit at the micro level, while promoting stability of shared vocabulary at the group level over generations. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

scholarly journals The Necessity of the Medial Temporal Lobe for Statistical Learning

2014 ◽  
Vol 26 (8) ◽  
pp. 1736-1747 ◽  
Author(s):  
Anna C. Schapiro ◽  
Emma Gregory ◽  
Barbara Landau ◽  
Michael McCloskey ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Statistical Learning ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Sensory Input ◽  
Matched Control ◽  
Test Phase ◽  
Control Groups ◽  
Subsequent Test ◽  
Continuous Sequence

The sensory input that we experience is highly patterned, and we are experts at detecting these regularities. Although the extraction of such regularities, or statistical learning (SL), is typically viewed as a cortical process, recent studies have implicated the medial temporal lobe (MTL), including the hippocampus. These studies have employed fMRI, leaving open the possibility that the MTL is involved but not necessary for SL. Here, we examined this issue in a case study of LSJ, a patient with complete bilateral hippocampal loss and broader MTL damage. In Experiments 1 and 2, LSJ and matched control participants were passively exposed to a continuous sequence of shapes, syllables, scenes, or tones containing temporal regularities in the co-occurrence of items. In a subsequent test phase, the control groups exhibited reliable SL in all conditions, successfully discriminating regularities from recombinations of the same items into novel foil sequences. LSJ, however, exhibited no SL, failing to discriminate regularities from foils. Experiment 3 ruled out more general explanations for this failure, such as inattention during exposure or difficulty following test instructions, by showing that LSJ could discriminate which individual items had been exposed. These findings provide converging support for the importance of the MTL in extracting temporal regularities.

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

2020 ◽  
Author(s):  
Andrew Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Processing ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Stimulus Familiarity ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in avariety 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 – and that perceptual fluency is driven at least in part by stimulus familiarity: 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 stimuli of different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no statistical learning component at all. Our results suggest that a key component of statistical learning performance may be related to stimulus-specific perceptual processing and familiarity.

scholarly journals Open and empathic personalities see two things at the same time: the relationship of big-five personality traits and cognitive empathy with mixed percepts during binocular rivalry

2021 ◽  
Author(s):  
Mika Koivisto ◽  
Maija Virkkala ◽  
Mika Puustinen ◽  
Jetta Aarnio
Keyword(s):  
Big Five ◽  
Binocular Rivalry ◽  
Sensory Input ◽  
Preliminary Evidence ◽  
Cognitive Empathy ◽  
Big Five Personality ◽  
Cognitive Mechanisms ◽  
The One ◽  
Relationship Of ◽  
The Relationship

AbstractDoes our personality predict what we see? This question was studied in 100 university students with binocular rivalry paradigm by presenting incompatible images to each eye, allowing multiple interpretations of the same sensory input. During continuous binocular presentation, dominance of perception starts to fluctuate between the images. When neither of the images is fully suppressed, the two images combine into mixed percepts. We focused on the link between mixed percepts, big-five traits, and empathy. The results revealed that openness and agreeableness correlated with the occurrence of mixed percepts after the first dominant perception. However, these correlations of openness and agreeableness were mediated by cognitive empathy. In addition, openness had a direct association with reporting the initial percept in the onset of stimulation as a mixed percept, suggesting a mechanism that is separate from the one mediated by cognitive empathy. Overall, the results provide preliminary evidence suggesting that personality predicts what we see. Such individual differences in perceptual interpretations may be linked to both higher level cognitive mechanisms as well as lower level visual mechanisms.

scholarly journals Cognitive mechanisms underpinning statistical learning of grammatical regularities in child and adult learners: Evidence for a multicomponential view

2021 ◽  
Author(s):  
Jelena Mirković ◽  
Marissa Yee ◽  
Maddison Kennedy ◽  
Marianna E. Hayiou-Thomas
Keyword(s):  
Adult Learners ◽  
Statistical Learning ◽  
Word Learning ◽  
Explicit Knowledge ◽  
Meta Analysis ◽  
Cognitive Mechanisms ◽  
Lexical Knowledge ◽  
Group Type ◽  
Grammar Learning

Statistical learning plays a key role in language acquisition and development, from word segmentation to grammar learning. In a recent review and meta-analysis, Frost et al. (2019) identified key contributions of the statistical learning literature over the last 20 years, as well as a number of limitations. Here we address three of those limitations across three experiments. First, we address the issue of unrealistic learning environments in previous statistical learning research by training participants on an artificial language comprising multiple regularities (phonological, distributional, semantic), unlike the majority of previous statistical learning studies. Second, to examine learning at several levels of linguistic structure, we use a word learning paradigm at training, which allowed us to assess both word and grammar learning, including generalization of the trained regularities to previously unseen items. Third, to address the issue of underspecification of cognitive mechanisms underpinning statistical learning, we examine the emergence and role of explicit knowledge in generalization performance in both child and adult learners. Additionally, we examine the role of off-line memory consolidation processes. Across three experiments and multiple tasks, we found that both children and adults showed good levels of word learning, but variable levels of generalization of the trained grammatical regularities. Generalization success depended on the age group, type of training, and type of regularity assessed. Across all three experiments, explicit knowledge of the regularities contributed to the performance in some generalization tasks, but it was not key for successful generalization. Off-line consolidation processes consistently influenced long-term maintenance of the newly acquired lexical knowledge, but evidence of their role in grammar learning was mixed. We argue that our findings shed light on the cognitive mechanisms underpinning statistical learning, and provide evidence in support of multicomponential views of statistical learning.

scholarly journals Unlocking adults’ implicit statistical learning by cognitive depletion

2022 ◽  
Vol 119 (2) ◽  
pp. e2026011119
Author(s):  
Eleonore H. M. Smalle ◽  
Tatsuya Daikoku ◽  
Arnaud Szmalec ◽  
Wouter Duyck ◽  
Riikka Möttönen
Keyword(s):  
Language Learning ◽  
Statistical Learning ◽  
Memory Task ◽  
Linguistic Knowledge ◽  
Choice Test ◽  
Control Mechanisms ◽  
Cognitive Mechanisms ◽  
Learning Mechanisms ◽  
Neural Entrainment ◽  
Cognitive Depletion

Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.

The bottleneck may be the solution, not the problem

2016 ◽  
Vol 39 ◽  
Author(s):  
Arnon Lotem ◽  
Oren Kolodny ◽  
Joseph Y. Halpern ◽  
Luca Onnis ◽  
Shimon Edelman
Keyword(s):  
Working Memory ◽  
Language Acquisition ◽  
Recent Theory ◽  
Cognitive Mechanisms ◽  
Selection Pressures ◽  
Biological Trait ◽  
Memory Bottleneck

AbstractAs a highly consequential biological trait, a memory “bottleneck” cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.

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