Initial and Sustained Attentional Bias Toward Emotional Faces in Patients with Major Depressive Disorder.

Hanliang Wei, Tak Kwan Lam, Weijian Liu, Waxun Su, Zheng Wang, Qiandong Wang, Xiao Lin, Peng Li

Abstract read

In one paragraph

Article in Journal of eye movement research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Hanliang WeiThe First Affiliated Hospital of Xinxiang Medical University, Xinxiang 453199, China.ORCID 0009-0005-8910-5646

Tak Kwan LamDepartment of Psychology, The Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0003-3576-0811

Weijian LiuNational Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.

Waxun SuBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing 100875, China.ORCID 0000-0002-0781-0247

Zheng WangBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing 100875, China.

Qiandong WangBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing 100875, China.

Xiao LinNational Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.

Peng LiNational Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China.

Funding

Peng Li; Xiao Lin 82271543, 8230229; 82101566

6 · The paper itself

Abstract

Major depressive disorder (MDD) represents a prevalent mental health condition characterized by prominent attentional biases, particularly toward negative stimuli. While extensive research has established the significance of negative attentional bias in depression, critical gaps remain in understanding the temporal dynamics and valence-specificity of these biases. This study employed eye-tracking technology to systematically examine the attentional processing of emotional faces (happy, fearful, sad) in MDD patients (

Indexed as

attentional biasemotional processingeye-trackingmajor depressive disordertemporal dynamic

Identifiers

PMID41440772

DOI10.3390/jemr18060072

PMCPMC12734090

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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.