Examining the Role of Negative Attentional Bias in Patients with Adolescent Depressive Disorders: An Emotion-Attention Blink Task Study.

doi:10.21203/rs.3.rs-3865806/v1

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Examining the Role of Negative Attentional Bias in Patients with Adolescent Depressive Disorders: An Emotion-Attention Blink Task Study.

https://doi.org/10.21203/rs.3.rs-3865806/v1

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Background

Negative cognitive bias significantly influences the onset, progression, and persistence of depression. However, the detailed aspects of attentional bias within this negative cognitive framework remain insufficiently delineated. This study aimed to (1) employ the attentional blink disengagement effect for assessing cognitive bias in facial emotion recognition among individuals with depressive disorders and (2) explore the specific elements of negative bias in these patients.

Methods

Thirty-three adolescents diagnosed with depressive disorders and 33 control subjects were selected based on the Depressive Disorder Severity Questionnaire (DDSQ) and psychiatric interviews. A mixed-design emotion-attention blink task (4 SOAs: 0 ms/235 ms/706 ms/1176 ms × 2 tasks: dual task/single task × 2 subgroups: control group/depressive disorder group) was utilized to measure the accuracy of emotion recognition. Differences in negative attentional bias between the two groups were analyzed using repeated-measures ANOVA.

Results

Adolescents with depressive disorders exhibited a negative attentional bias, primarily characterized by difficulties in attentional disengagement. The disparity in this negative attentional bias was most pronounced in scenarios involving exclusively negative emotional faces. Clinical trial registration number ChiCTR2300067671.

Conclusion

Negative attentional bias is a hallmark of processing styles in patients with depressive disorders. In adolescent patients, this bias in emotion recognition tasks predominantly manifests as difficulties in attentional disengagement.

depressive disorders

attentional blinks

negative bias

emotion recognition

attentional dissociation difficulties

Depression is a relatively prevalent mental health condition. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [1], depression is classified as a disorder marked by a blend of emotional, cognitive, and neurotrophic shifts. Its primary symptoms include feelings of sadness, emptiness, or irritability, accompanied by bodily and cognitive changes that significantly impact an individual's neurological function. This condition extends beyond the patient's personal experiences, influencing their interactions with family and friends. In severe instances, it can even disrupt the patient’s social capabilities. Annually, more than 700,000 individuals with depression succumb to suicide. According to projections, by 2030, depression will surpass cardiovascular disease as the leading cause of disability[2]. A study on disease burden published in The Lancet highlighted that individuals aged 15 to 24 are most susceptible to depression[3].

Individuals with depressive disorders often experience a diminished quality of life, compromised cognitive functioning, and social and interpersonal challenges, which significantly contribute to the progression of their condition[4]. Research on social cognition and depression has focused on facial emotion recognition, which is inversely related to the severity of depressive psychopathology[5]. Studies have revealed that people with depression tend to perceive neutral or ambiguous faces as sad[6–9] and are more adept at identifying sadness than nondepressed individuals[10]. A comprehensive meta-analysis indicated that depressed individuals also struggle with recognizing a broader spectrum of facial emotions[11].

Much of the existing research is cross-sectional, providing limited insights into the causal relationship between emotion recognition and depression. Cognitive neuropsychological models suggest that alterations in automatic emotion processing may precede and predict the onset of depressive symptoms [12–14]. A study by Tranter et al. [15] revealed that early in antidepressant treatment, before symptom improvement, the ability to recognize happy faces predicted a reduction in depressive symptoms in six weeks. Conversely, the recognition of sad emotions correlated with the persistence of depression after six months[6, 16]. Milders et al.[10] observed that despite a decrease in depressive symptom severity in six months, patients’ responses to sad facial expressions remained consistent. Over an extended period (2.5 years), a negative bias in facial expression recognition persisted in patients with major depressive disorder, irrespective of their relapse status [17].

Beck’s theory of cognitive impairment emphasizes that depression is accompanied by a negative attentional bias toward information processing and that this bias hypothesis has strong explanatory power in the onset and maintenance of depression, which is manifested primarily in reduced attention given to positive information and a preference for negative information[18]. Attentional bias can manifest as a sharper response to a stimulus, prolonged exposure to a stimulus from which the individual is unable to extricate himself or herself, or a combination of both of the above, but either of the above can impair the individual’s perception and processing of other stimuli, resulting in a narrowing of attention, interfering with goal-directed behaviors, and even seriously impairing the individual’s ability to make judgments and decisions[19]. However, many studies have shown that the attention span of individuals is limited. Although a number of studies have found a negative attentional bias in some of the assignments used to detect cognitive responses in depressed individuals, the occurrence of this attentional bias has been found to be a significant factor in the development of a negative attentional bias. However, the mechanism by which this attentional bias occurs is complex, and no obvious conclusions have been reached yet. Regarding how attention is affected by emotion, some psychologists have suggested that this effect is due to competition between multiple “biases” [20]. According to the components of attention theory, the three components of attention are attentional orienting, attentional disengagement, and attentional shifting [21]. The majority of scholars have focused on attention orienting and attention disengagement [22]. Previous studies have shown that, compared with nondepressed individuals, depressed individuals lack a “protective bias”, which is characterized by a tendency to avoid negative information; this lack of protective emotion makes it more difficult for depressed individuals to deregulate their attention from negative emotional information[23]. The lack of this protective emotion makes it more difficult for depressed individuals to disengage from negative emotional information[23]. According to the protective bias hypothesis, depressed patients do not develop an attentional bias during the attentional phase of negative information but rather have difficulties in attentional disengagement after the information has become the focus of attention, and such disengagement difficulties are associated with ineffective use of emotion management and persistent negative affection.

Adolescents’ self-awareness is rapidly developing, and self-awareness refers to the sum of an individual’s ideas about his or her own physical and mental state and his or her position in society [24]. Self-centered thinking is highlighted by the construction of an “imaginary audience” and “personal myth”; according to Erikson’s theory of mental development, adolescents experience a crisis of self-identity during this period[25]. Negative self-consciousness interferes with cognitive functioning and social interaction. Maintaining a negative sense of self for a long period of time can lead to disorganization of adolescents’ self-reference system and the creation of negative cognitive biases. Such cognitive biases at the developmental stage may lead to depressive episodes in adolescents[26].

To verify the above questions, the present study was designed to detect cognitive differences between depressed and normal adolescents using the attentional blink effect of facial emotion recognition in a “two-target paradigm (T1-T2 paradigm)” experimental design [27]. The two-target paradigm requires adolescents to make judgments about two emotional faces at very brief intervals. Adolescents’ recognition of the emotion of the first face interferes with their recognition of the emotion of the second face, an effect known as “attentional blink (AB)”.

Broadbent discovered the phenomenon in 1987, and Raymond et al. first named this phenomenon in 1992. The term “attentional blink” refers to the phenomenon in which participants are presented with two target stimuli in rapid succession, separated by a short period of time, and their recognition of the second target stimulus decreases. Typically, this results in a decrease in recognition or detection performance for the second target after the observer has recognized the first target. Several studies have suggested that AB is caused by limitations in ability inherent in the two simple behaviors of perceiving and reporting visual stimuli (e.g., consolidation of relevant information in working memory) [28]. Greater attentional blinks are induced when emotional stimuli act as T1, i.e., emotional attentional blinks[29]. When the emotional stimulus is the second stimulus in the target stimulus (T2), it reduces the attentional blink, i.e., the emotional antagonistic attentional blink[30]. The inclusion of emotional stimuli in the attentional blink task provides a window of observation and a research tool for us to better understand and study the preferential conscious processing of emotional stimuli in humans.

Most of those previous studies used the rapid serial visual presentation (RSVP) technique. The RSVP method, which involves the presentation of a rapid sequence of stimuli, has been pivotal in understanding the AB effect, where the detection of a second target is hindered when it appears shortly after the first. Indeed, the process involves not only discriminating targets but also filtering out irrelevant stimuli. The uncertainty about the timing of the first target (T1) onset adds another layer of complexity to this task. The models proposing limitations in perceiving and reporting visual stimuli as the cause of AB align with the idea that AB might stem from the constraints of working memory and how information is consolidated[31]. If this holds true, it challenges the necessity of presenting irrelevant objects in the RSVP or introducing temporal uncertainty at T1 onset. This line of thinking suggests that the core issue lies within how information is processed and consolidated rather than in the complex setup of RSVP, which involves multiple distractors and temporal uncertainties. By recording and calculating the nature and extent of the AB effect in adolescents with depression under different emotional stimulus conditions, we can understand the characteristics of adolescents’ prioritized conscious processing of emotional stimuli in an emotion recognition task and thus explore the possible factors influencing the onset of depression in adolescents. According to the general conclusion of the AB effect, when T1 and T2 are emotional materials of the same nature, interference from T1 to T2 occurs but not when the conscious resources required for T1 processing are not enough to consume all the cognitive resources of the individual. If depressed adolescents have a deficit in “protective tendency” in the face of negative stimuli, they will invest more in processing the negative stimuli, have difficulties in attentional disengagement, and produce a priming effect on T2 recognition.

Therefore, when T1 was a neutral emotional face and T2 was a negative emotional face, depressed adolescents’ accuracy on the T2 task was significantly greater than that of the controls, and an emotionally antagonistic AB effect occurred; when both T1 and T2 were negative stimuli, no emotional AB effect occurred in the depressed group relative to the controls.

2.1 Participants

Power analysis was conducted using G*Power [32] with effect sizes set to a moderate ρ = 0.25, an α of 0.05, and a statistical test power of 0.80, determining the need for 24 subjects. Forty-four hospitalized patients with depression were recruited from Lishui Second People’s Hospital and Wenzhou Kangning Hospital. All patients underwent a structured clinical interview based on the DSM-IV, administered by a trained clinician. The inclusion criteria involved assessments using the Beck Depression Self-Rating Scale and the Flow Call Depression Self-Rating Scale. The exclusion criteria included the following: (a) history of psychotic disorders; (b) comorbid tic disorders or syndromes; (c) family history of psychosis; (d) organic brain disease; and (d) history of electroshock therapy. Seven participants were excluded, including two withdrawals, resulting in 33 patients (9 males, 24 females; age: M = 16.909, SD = 2.789; Beck: M = 33.210, SD = 1.540; Ces-d: M = 42.790, SD = 1.201) eligible for the study.

Thirty-three healthy controls matched for age, sex, and education were recruited via clinical services, the Internet, and local schools (15 males, 18 females; age: M = 17.030, SD = 2.680; Beck: M = 10.550, SD = 1.123; Ces-d: M = 3.610, SD = 0.592). No significant differences were found between groups in age (t_{(1, 64)} =-0.177, p = 0.860) or sex (t_{(1, 64)} = 1.540, p = 0.129), but the depressive symptoms of the patients were significantly greater (t_{(1, 64)} = 11.893, p < 0.001; t_{(1, 64)} = 29.261, p < 0.001). Detailed information is provided in Table 1.

All participants provided informed consent and were briefed on the purpose of the experiment, the potential risks, their right to withdraw at any time without consequences, and whether withdrawal would affect their medical treatment. The study adhered to the ethical standards set by the Ethics Committee of Wenzhou Medical University, ethics document approval number 2022-072. Clinical trial registration number ChiCTR2300067671, the registration date is 2023-01-17, and the registration center is Beijing Anding Hospital affiliated with Capital Medical University.

Table 1. Age of parcipants and scale scores

2.2 Experimental Design and Procedure

The experiment utilized Eprime 2.0 software for programming, with face (firgure1) material from the Chineseized Face Emotion Picture System[33] and masking stimuli created in Adobe Photoshop CC 2018. Detailed information on the facial dimensions is shown in Table 2. The experiment was conducted on a laptop with a 1024×768 resolution and a 60 Hz refresh rate.

The task involved a 4 (SOA: 0 ms/235 ms/706 ms/1176 ms) × 2 (task: dual/single) × 2 (subgroups: control/depression) mixed design. The SOA and task are intrasubject variables, and the grouping is an intersubject variable. The task variable had dual-task (emotional valence judgments for T1 and T2) and single-task (judgments for T2 only) categories. The SOA intervals between T1 and T2 were 0 ms, 235 ms, 706 ms, and 1,176 ms. T1 and T2 emotional faces were equally divided into negative and neutral faces.

Table 2 Emotional Face Dimension Test

The experiment comprised 320 trials in 4 modules, each repeated 10 times under different conditions. The modules alternated between dual-task and single-task conditions, with task order balanced using the ABBA method. The SOA and T1 and T2 variables were randomized.

At the onset of the formal experiment, instructions were displayed on the screen, followed by the presentation of emotional faces T1 at either the top or bottom of the screen and T2 on either the left or right side. The sequence of each trial was structured as follows (refer to Fig. 1 for illustration): (1) the central gaze point, along with two additional gaze points positioned vertically 4 degrees above and below the central point, was displayed for 1529 ms; (2) the images of the T1 and T2 faces, as well as the masking stimuli, were of identical dimensions (4 degrees wide and 5.5 degrees high); and (3) T1 and T2 were selected randomly from a pool of 20 emotional faces and displayed randomly at the top and bottom locations of the screen for a duration of 71 ms each. Subsequently, a masking stimulus appeared randomly at the same location for 59 ms. (4) The stimulus onset asynchrony (SOA) intervals between the presentations of T1 and T2 varied and were set at 0 ms, 235 ms, 706 ms, and 1176 ms. Participants were required to respond after the presentation of both T1 and T2.

A one-minute break was allocated between each module to ensure participant comfort and readiness for the subsequent trials.

Figue1 Timeline of the stimulus presentation and response window

2.3 Outcome Measures

The assessment of participants’ depressive symptoms was conducted using the Beck Depression Inventory-II, Chinese Version (BDI-II-C), and the Center for Epidemiologic Studies Depression Scale (CES-D). The BDI-II-C, utilized to evaluate depression levels over the past week, was adapted from a previously translated version tailored for adolescent studies. The CES-D is a globally recognized tool for screening depressive symptoms and is employed extensively in both Chinese and international contexts. This questionnaire was designed to avoid infringing on participants’ privacy, thereby enhancing response rates. In our sample, the Cronbach’s alpha coefficients were determined to be 0.943 for the BDI-II-C and 0.981 for the CES-D, indicating high reliability for these scales.

In notable studies, the scope of dependent variables is often narrower than that of independent variables, with response time being the most preferred and widely used metric [34]. However, in our experiment involving participants with depression whose action slowness could lead to significant disparities in response time between groups but was unrelated to the objective of the experiment, we opted for another common variable: correctness. Drawing from De Martino et al. [35], extended reaction times could be influenced by a priori probabilities, casting doubt on participants' confidence in stimulus judgment. Consequently, responses exceeding 4000 ms were excluded from our analysis. Therefore, only the T2 data were analyzed when T1 was correctly identified.

One-sample T tests were conducted on the experimental data to compare group characteristics, and repeated-measures ANOVA was employed to evaluate continuous variables for between-group differences. A significance threshold of 0.5 was set for all analyses, and P values were subjected to Bonferroni correction using IBM SPSS Statistics (version 23).

3.1 Between-subject Differences in Face Emotion Recognition Ability

The data from the single-task condition were subjected to a 2 (T2 emotion: negative/neutral) × 2 (subgroup: depressed/control) repeated-measures ANOVA. The results revealed no significant interaction between T2 emotion and subgroup or any significant main effects for T2 emotion or subgroup.

3.2 Neutral Faces Affect Recognition of Negative Emotional Faces

Data from the control group participants, where T1 was neutral and T2 was a negative emotional face, were analyzed using a 2 (task: dual-task/single-task) × 4 (SOA: 0 ms/235 ms/706 ms/1176 ms) repeated-measures ANOVA. The results indicated a significant interaction effect between task type and SOA (F_{(3, 32)} = 2.760, p < 0.05, η_p² =0.079). Post hoc pairwise comparisons revealed significant differences in response correctness between the two tasks when the SOA was 0 and 506 ms. Similar analyses of the depressed group data showed a significant main effect only for the SOA variable (F_{(3, 32)} = 3.748, p < 0.05, η_p² =0.108). Refer to Fig. 2 for further details.

Figue2 Percentage of correct identification of participants in the control and depressed group

3.3 Effect of emotion in T1 faces on T2 negative face recognition

To compare the effects of different T1-face emotions on T2-weighted negative emotion face recognition, the experimental data were subjected to 2 (T1emotion: neutral/negative) × 2 (Subgroup: depressed group/control group) × 4 (SOA: 0/235/706/1176 ms) repeated-measures ANOVA. ANOVA revealed a significant main effect of T1 emotion (F_{(1, 63)} = 6.400, p = 0.014, η_p² =.092) and a significant main effect of subgroup (F_{(1, 63)} = 4.798, p = 0.032, η_p² =0.071). Simple effects analyses of the main effect of T1 emotion revealed that this differential effect was reflected in the control group; when T1 was a negative emotional face, the control and depressed participants differed significantly in their recognition of negative emotional faces at SOAs of 0 and 235 ms, with depressed participants having significantly more correct T2 recognition than controls in this situation. Simple effects analyses of group main effects revealed a significant difference in recognition correctness between the single-task and dual-task conditions in the control group at an SOA of 1176 ms.

3.4 The case where T1 and T2 are both neutral faces

Participants’ experimental data in the experimental conditions with neutral faces at T1 and T2 were subjected to a 2 (task: dual-task/single-task) × 4 (SOA: 0/235/706/1176 ms) × 2 (subgroups: depressed/control) repeated-measures analysis of variance (ANOVA), and the ANOVA results showed a significant main effect of task (F_{(1, 63)} = 6.611, p = 0.014, η_p² =0.095); additionally, a simple effects analysis was performed to show significant differences between dual-task and single-task responses in the control group at SOAs of 0 and 235 ms. See Fig. 3.

Figue3 Percentage of correct identification of participants in the control and depressed group

3.5 Investigating the Influence of Negative Emotional Faces at T1 on T2 Recognition

To explore the impact of initial negative emotional faces on the recognition of subsequent negative emotional faces at T2, we established an experimental condition where both T1 and T2 were negative emotional faces. Participant data were analyzed using a 2 (subgroups: depressed/control group) × 2 (task: dual-task/single-task) × 4 (SOA: 0/235/706/1176 ms) repeated-measures ANOVA. The results indicated a significant interaction between task and group (F_{(1, 63)} = 5.693, p = 0.020, η_p² =0.083). Post hoc pairwise comparisons revealed notable differences between the depressed and control groups in dual-task performance at SOAs of 0 and 235 ms (p = 0.026; p = 0.029), with the depressed group exhibiting significantly greater rates of correct identification. For further details, refer to Fig. 4.

Figue4 Percentage of correct identification of participants in the control and depressed group

Exploring the attentional blink effects in facial emotion recognition among patients with depressive disorders has enhanced our understanding of the cognitive mechanisms through which emotions receive preferential conscious processing. Our primary objective was to evaluate the presence and intensity of negative cognitive biases in these patients. We discovered that, compared to controls, patients with depressive disorders demonstrated a more significant negative cognitive bias in facial emotion recognition. Our secondary aim was to investigate the underlying mechanisms and individual variations in emotionally antagonistic attentional blinks, striving to identify an appropriate behavioral indicator that could aid in the diagnosis of depressive disorders.

No significant differences in facial emotion recognition were found between the depressed and control groups when they were recognizing either neutral or negative emotion faces. This finding suggested that the depressed group was as competent as the control group at identifying emotions in rapidly presented faces. When assessing the impact of neutral faces on the recognition of negative emotional faces, we found a significant interaction effect between task performance and SOA in the control group, indicating a notable attentional blink effect. In contrast, the depressed group did not exhibit significant interactions or main effects, suggesting an absence of attentional blinks in this scenario, reflecting a pronounced emotionally antagonistic attentional blink effect.

Analysis of conditions where both T1 and T2 were neutral faces revealed a significant difference in task performance between the control group and the OSA group at 0 and 235 ms, demonstrating the occurrence of an attentional blink, thus validating our dual-target paradigm. When both target stimuli were negative emotional faces, there was a significant interaction between task and group, with the control group exhibiting an attentional blink effect and the depressed group showing differing cognitive characteristics.

In nonemotional attentional blink tasks, the control group displayed a clear attentional blink effect at an SOA of 235 ms, while the attentional blink window of the depressed group was notably delayed. This delay aligns with previous studies reporting extended attentional blinks in depressed patients[36] and is consistent with major depressive disorder symptoms such as motor retardation and indecision[1]. This finding suggested that such motor slowness may manifest at a nonconscious level, as evidenced by the shifted attentional blink window.

After introducing emotional stimuli into T2 of the attentional blink task, the control group continued to experience an attentional blink effect, while the depressed group exhibited an emotionally antagonistic attentional blink effect. This was attributed to the increased correct recognition rates at T2 in depressed patients and their cognitive bias toward negative emotional stimuli. Beck’s cognitive theory of depression underscores this negative information processing bias, providing a robust explanation for the onset and maintenance of depression, characterized by a predilection for negative emotional information.

Harmer et al. [12, 37] reported that medication could alter negative biases, with these changes preceding symptom improvement. Facial emotion recognition, which is critical for interpersonal and cognitive functioning development [38], was used as our assessment method. The differences in attentional blink effects observed between depressed individuals and healthy controls suggest that emotionally antagonistic attentional blinks could serve as a behavioral indicator of individual differences in emotion processing, laying the groundwork for future research and aiding in screening for clinical depressive disorders.

Considering these limitations, the emotional material used was static and context free, potentially affecting the study’s external validity. The cross-sectional design limits our ability to explore the causal relationship between negative attentional bias and depressive disorders. Future studies might adjust the SOA levels and duration of the experiment, focusing on adolescents' responses to objective stimuli at shorter time intervals. This approach bypasses subjective reporting and delves into subconscious cognitive biases, aligning with fast-paced clinical requirements.

Larger sample studies are needed to establish norms for applying these findings in screening and treating depressive disorders. Nonpharmacological treatments targeting attentional release difficulties could be novel approaches. In conclusion, we suggest that negative bias characterizes the processing style of individuals with depressive disorders, particularly attentional disengagement difficulties in emotion recognition. This behavioral indicator could be invaluable in clinical screenings for depressive disorders.

Financial support

The authors did not receive support from any organization for the submitted work.

Acknowledgments

We would like to thank the researchers and study participants for their contributions.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Competing Interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

We declare all participants provided informed consent and were briefed on the purpose of the experiment, the potential risks, their right to withdraw at any time without consequences, and whether withdrawal would affect their medical treatment. The study adhered to the ethical standards set by the Ethics Committee of Wenzhou Medical University, ethics document approval number 2022-072.

Consent for publication

Agree to publish. All of the material is owned by the authors and/or no permissions are required.

Availability of data and materials

All study data is uploaded with attachments.

Conflict of interest statement

All of the material is owned by the authors and/or no permissions are required. No conflict of interest.

Funding

No application.

Author Contributions Statement

S.S.: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Writing - Review & Editing; Y.Q.: Investigation, Resources, Writing - Original Draft, Writing - Review & Editing; H.H.: Methodology, Software, Writing - Review & Editing; F.: Data Curation, Resources; C.T.: Chunting Lin: Data Curation, Resources; H.L.: Writing - Review & Editing, Supervision; K.: Writing - Review & Editing, Supervision, Project administration.

All authors reviewed the manuscript.

Publication of consent statements

All images of faces in this article have been published with permission.

Credit authorship contribution statement

Shasha Zhu: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Writing - Review & Editing; Yuqing Zhao: Investigation, Resources, Writing - Original Draft, Writing - Review & Editing; Hehe Lu: Methodology, Software, Writing - Review & Editing; Feng Guo: Data Curation, Resources; Chunting Lin: Data Curation, Resources; Huilin Qiu: Writing - Review & Editing, Supervision; Ke Jiang: Writing - Review & Editing, Supervision, Project Administration

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Table 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

Table1Ageofparticipantsandscalescores.png
Table2EmotionalFaceDimensionTest..png
fig.5EmotionalFaceMaterial.png
Appendice1 Emotional Face Material
datas.zip
procedure.zip

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Examining the Role of Negative Attentional Bias in Patients with Adolescent Depressive Disorders: An Emotion-Attention Blink Task Study.

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Abstract

Background

Methods

Results

Conclusion

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1. Introduction

2. Methods

2.1 Participants

2.2 Experimental Design and Procedure

2.3 Outcome Measures

3. Results

3.1 Between-subject Differences in Face Emotion Recognition Ability

3.2 Neutral Faces Affect Recognition of Negative Emotional Faces

3.3 Effect of emotion in T1 faces on T2 negative face recognition

3.4 The case where T1 and T2 are both neutral faces

3.5 Investigating the Influence of Negative Emotional Faces at T1 on T2 Recognition

4. Discussions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1