Evidence map›Paper›PMID 35545424›Full record

ArticleeNeuro

Functional Gradient of the Fusiform Cortex for Chinese Character Recognition.

Wanwan Guo, Shujie Geng, Miao Cao, Jianfeng Feng

Abstract read
In one paragraph

Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wanwan GuoInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
Shujie GengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
Miao CaoInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China mcao@fudan.edu.cn jianfeng64@gmail.com.
Jianfeng FengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China mcao@fudan.edu.cn jianfeng64@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Visual word recognition has been proposed to have a functional and spatial organization corresponding to hierarchical language-like word forms in the left fusiform gyrus (FG) during visual word recognition in alphabetic languages. However, it is still unclear whether the similar functional gradients of word-like representation exist during Chinese character recognition. In this study, we adopted univariate activation analysis and representational similarity analysis (RSA) methods to investigate the functional organization in the FG for Chinese character recognition using task fMRI data. Native Chinese readers were visually presented with four types of character-like stimuli (i.e., real characters, pseudo-characters, false characters, and stroke combinations). After analysis, we observed a posterior-to-anterior functional gradient in the left FG corresponding to the degree of likeness of stimuli to character. Additionally, distinct subregions of the left FG harbor different orthographic codes. The middle part of the left FG was involved in abstract orthographic processing, while the anterior part of the left FG was involved in lexical orthographic processing (i.e., mapping orthography onto phonology or semantics). Notably, for the right FG, we did not find similar coding pattern for selectivity to character likeness, indicating the asymmetry of the functional hierarchical organization in favor of the left hemisphere. In conclusion, our findings revealed that the left FG presents a posterior-to-anterior gradient functional processing for Chinese character recognition, which expands our understanding of the psychological, neural, and computational theories of word reading.

Indexed as

LanguageReadingChinaLinguisticsRecognition, PsychologyChinese character recognitionfunctional gradientfusiform cortexrepresentational similarity analysisunivariate activation analysis

Identifiers

PMID35545424
PMCPMC9172282

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.