MEG reveals spatiotemporal dynamics of structural and semantic traits processing in Mandarin words
Poster No:
803
Submission Type:
Abstract Submission
Authors:
Yanlin Fu1,2,3, Yangjiayi Mu1,2, Zhao Xu1,2, Qingyang Liu1,2, Qinhui Yao1,2, Ziyou Wang1,2, Xinwei Liu1,2, Yuwei Jiang1,2
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China, 3Department of Nuclear Science and Technology, Fudan University, Shanghai, China
First Author:
Yanlin Fu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education|Department of Nuclear Science and Technology, Fudan University
Shanghai, China|Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education|Department of Nuclear Science and Technology, Fudan University
Shanghai, China|Shanghai, China|Shanghai, China
Co-Author(s):
Yangjiayi Mu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Zhao Xu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Qingyang Liu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Qinhui Yao
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Ziyou Wang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Xinwei Liu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Yuwei Jiang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Introduction:
There is extensive research on how humans process structured pure-tone sequences, but the impact of semantic features within such sequences is not fully investigated. This study examined the behavioral contrast of human responses to semantic and structural deviations during sequential learning, and the MEG revealed such dynamics in right hemisphere.
Methods:
We recruited 27 participants in community nearby Fudan University, and 21 of them was included in the study (6 Female, mean age [SD] = 21.14[1.91]). In this experiment, we used three-syllable Chinese reduplication words (ABB, AAB) and they were semantically categorized as nouns, adjectives, or verbs. In the main task session (Figure1a), in each block participants will have an encoding phase with 4 words showing the same sequence pattern. After encoding phase, the matching phase has four kinds of trials: trials with consistent patterns (same structure and semantics), trials with structural changes, trials with semantic changes, and trials with both changes. Participants were asked to identify the block pattern during the encoding phase and report if there was a deviation after 800ms of the stimulus offset. They were requested to respond with their first thought regarding any deviations (Figure1a). The study was performed on MEGIN Truix Neo MEG system and the data was processed with MaxFilter to remove environmental noise. And then the data was converted into a BIDS format (Niso, 2018) and further processed using the FLUX pipeline (Ferrante, 2022) with MNE-Python 1.6.0 (Gramfort, 2013). Sensor data was filtered between 1 Hz and 45 Hz; trials with incorrect responses or excessive noise were excluded from analysis.
Results:
A 2-way ANOVA was conducted to assess the effects of deviant type (structural change versus semantic change) and encoding pattern (ABB and AAB) on reaction time (RT), revealing a significant main effect of deviant type (p=0.005, uncorrected) rather than encoding pattern (p= 0.333). Subsequent Wilcoxon rank tests indicated that structural deviants elicited faster responses compared to semantic deviants (p<0.05, FDR corrected, Figure1b) . After that, the decoding-based contrast analysis (Bonetti, 2024) was applied to explore the activation patterns (sensor arrays) associated with deviant responses. The activation pattern was determined by a threshold, which was applied to identify the sensors that exhibiting specific responses to deviations, and was calculated based on timepoints where the classifier's decoding scores significantly exceeded chance level (p<0.05, cluster-based permutation corrected, Figure1c). Initially, we examined the common patterns between semantic and structural decoding, finding that structural deviations elicited longer responses (p<0.05, cluster-based permutation corrected) in both ABB and AAB conditions (Figure2a, b). Since the common patterns in the ABB condition showed no significant difference for semantic deviant trials compared to regular trials, we conducted a contrast analysis on the unique pattern of semantic deviant decoding. This analysis revealed that semantic deviants also elicited responses, albeit shorter than those for structural deviants (Figure 2c). The prolonged retention of deviant information may underlie their behavioral correlations.
Conclusions:
Our MEG findings reveal that the right hemisphere (in major) exhibit a specific response to deviation information, and the temporal dynamics of structural and semantic deviations are different. This temporal distinction corresponds to the rapid behavioral responses observed for structural deviations. These results provide insights into the distinct neural mechanisms underlying the processing of structural and semantic features in sequential learning.
Language:
Language Comprehension and Semantics 1
Speech Perception 2
Keywords:
Cognition
Hemispheric Specialization
Language
MEG
1|2Indicates the priority used for review
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2. Ferrante, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047.
3. Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in neuroscience, 7, 267.
4. Niso, G. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific data, 5, 180110.