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Seeking the Neural Representation of Statistical Properties in Print during Implicit Processing of Visual Words

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Title: Seeking the Neural Representation of Statistical Properties in Print during Implicit Processing of Visual Words
Language: English
Authors: Jianyi Liu (ORCID 0000-0002-9371-7232), Tengwen Fan, Yan Chen, Jingjing Zhao (ORCID 0000-0001-9000-6227)
Source: npj Science of Learning. 2023 8.
Availability: Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Semantics, Brain Hemisphere Functions, Language Processing, Reading Processes, Cognitive Mapping, Word Frequency, Phonology, Probability, Orthographic Symbols, Chinese, Visual Stimuli, Prediction, Statistics
DOI: 10.1038/s41539-023-00209-3
ISSN: 2056-7936
Abstract: Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1431632
Database: ERIC
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  – Url: https://dx.doi.org/doi:10.1038/s41539-023-00209-3
    Name: EDS - Springer Nature Journals (s7799221)
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  Data: <searchLink fieldCode="AR" term="%22Jianyi+Liu%22">Jianyi Liu</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-9371-7232">0000-0002-9371-7232</externalLink>)<br /><searchLink fieldCode="AR" term="%22Tengwen+Fan%22">Tengwen Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Yan+Chen%22">Yan Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Jingjing+Zhao%22">Jingjing Zhao</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-9000-6227">0000-0001-9000-6227</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22npj+Science+of+Learning%22"><i>npj Science of Learning</i></searchLink>. 2023 8.
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  Data: Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
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  Data: Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.
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        Value: 10.1038/s41539-023-00209-3
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      – Text: English
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