Tao Xia

Chinese University of Hong Kong

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i-10 index

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Co-Authors

Xiaoqing Hu

University of Hong Kong

Ziqing Yao

University of Hong Kong

Danni Chen

Hangzhou Normal University

Jing Liu

Beijing Institute of Technology

Hailong Yang

Tsinghua University

Chaoshuai Lei

Aerospace Research Institute of Materials and Processing Technology

James W. Antony

California Polytechnic State University

Zijun Hu

Tianjin University

Junning Li

Beijing Forestry University

Ken A. Paller

Northwestern University

Cooperative Institutions

University of Hong Kong

Chinese University of Hong Kong

Beijing Normal University

Liaoning Normal University

Shenzhen University

Beihang University

Chinese Academy of Sciences

Aerospace Research Institute of Materials and Processing Technology

University of Chinese Academy of Sciences

Sun Yat-sen University

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The influence of spatial frequency content on facial expression processing: An ERP study using rapid serial visual presentation

Scientific Reports (2018)

Tian Jinhua Jian Wang Tao Xia Wenshuang Zhao Qianru Xu

Abstract Spatial frequency (SF) contents have been shown to play an important role in emotion perception. This study employed event-related potentials (ERPs) to explore the time course of neural dynamics involved in the processing of facial expression conveying specific SF information. Participants completed a dual-target rapid serial visual presentation (RSVP) task, in which SF-filtered happy, fearful, and neutral faces were presented. The face-sensitive N170 component distinguished emotional (happy and fearful) faces from neutral faces in a low spatial frequency (LSF) condition, while only happy faces were distinguished from neutral faces in a high spatial frequency (HSF) condition. The later P3 component differentiated between the three types of emotional faces in both LSF and HSF conditions. Furthermore, LSF information elicited larger P1 amplitudes than did HSF information, while HSF information elicited larger N170 and P3 amplitudes than did LSF information. Taken together, these results suggest that emotion perception is selectively tuned to distinctive SF contents at different temporal processing stages.

Spatial frequency

Expression (computer science)

Facial expression recognition

10.1038/s41598-018-20467-1

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Citations (29)

Targeted memory reactivation during sleep influences social bias as a function of slow‐oscillation phase and delta power

Psychophysiology (2022)

Tao Xia James W. Antony Ken A. Paller Xiaoqing Hu

Abstract To understand how memories are reactivated and consolidated during sleep, experimenters have employed the unobtrusive re‐presentation of memory cues from a variety of pre‐sleep learning tasks. Using this procedure, known as targeted memory reactivation (TMR), we previously found that reactivation of counter‐social‐bias training during post‐training sleep could selectively enhance training effects in reducing unintentional social biases. Here, we describe re‐analyses of electroencephalographic (EEG) data from this previous study to characterize neurophysiological correlates of TMR‐induced bias reduction. We found that TMR benefits in bias reduction were associated with (a) the timing of memory‐related cue presentation relative to the 0.1–1.5 Hz slow‐oscillation phase and (b) cue‐elicited EEG power within the 1–4 Hz delta range. Although cue delivery was at a fixed rate in this study and not contingent on the slow‐oscillation phase, cues were found to be clustered in slow‐oscillation upstates for those participants with stronger TMR benefits. Similarly, higher cue‐elicited delta power 250–1000 ms after cue onset was also linked with larger TMR benefits. These electrophysiological results substantiate the claim that memory reactivation altered social bias in the original study, while also informing neural explanations of these benefits. Future research should consider these sleep physiology parameters in relation to TMR applications and to memory reactivation in general.

Oscillation (cell signaling)

Sleep

10.1111/psyp.14224

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Citations (15)

Slow-wave sleep and REM sleep differentially contribute to memory representational transformation

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Jing Liu Danni Chen Tao Xia Shengzi Zeng Gui Xue

Abstract Memory transforms over time, gradually becoming less idiosyncratic and more gist-like. While sleep contributes to memory transformation, how different sleep stages and EEG activity influence memory transformation is far from clear. Applying representational similarity analysis to electroencephalogram (EEG) recordings, we examined memory representational transformation at both the idiosyncratic “item-level” and the generic “category-level”. Our findings revealed that after an overnight sleep, item-level neural representations for post-sleep remembered items were abolished. In contrast, category-level representations remained prominent, but they became distinctive from pre-sleep. Across participants, more rapid eye movement (REM) sleep relative to slow-wave sleep (SWS) was associated with reduced item-level neural representational strength, increased category-level representational strength, as well as the decreased item-level representational similarity between pre-sleep learning and post-sleep retrieval sessions. Moreover, the theta and beta EEG power during REM sleep, and delta power during SWS differentially supported these representational transformations. These findings suggest that post-learning REM sleep and SWS play differential roles in supporting overnight memory transformation.

Sleep

Similarity (geometry)

10.1101/2024.08.05.606592

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Citations (0)

Feature ranking of the EAPcompo model.

Yongtao Xu Yu Shui Jian‐Wei Zou Guixiang Hu A. B. D. Rahman Noorsaadah

Feature (linguistics)

10.1371/journal.pone.0144171.g001

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Citations (0)