Emotional Expression Processing and Depressive Symptomatology: Eye-Tracking Reveals Differential Importance of Lower and Middle Facial Areas of Interest

Depressive symptoms and visual attention to others' eyes in healthy individuals

BACKGROUND

Eye contact is a fundamental part of social interaction. In clinical studies, it has been observed that patients suffering from depression make less eye contact during interviews than healthy individuals, which could be a factor contributing to their social functioning impairments. Similarly, results from mood induction studies with healthy persons indicate that attention to the eyes diminishes as a function of sad mood. The present screen-based eye-tracking study examined whether depressive symptoms in healthy individuals are associated with reduced visual attention to other persons' direct gaze during free viewing.

METHODS

Gaze behavior of 44 individuals with depressive symptoms and 49 individuals with no depressive symptoms was analyzed in a free viewing task. Grouping was based on the Beck Depression Inventory using the cut-off proposed by Hautzinger et al. (2006). Participants saw pairs of faces with direct gaze showing emotional or neutral expressions. One-half of the face pairs was shown without face masks, whereas the other half was presented with face masks. Participants' dwell times and first fixation durations were analyzed.

RESULTS

In case of unmasked facial expressions, participants with depressive symptoms looked shorter at the eyes compared to individuals without symptoms across all expression conditions. No group difference in first fixation duration on the eyes of masked and unmasked faces was observed. Individuals with depressive symptoms dwelled longer on the mouth region of unmasked faces. For masked faces, no significant group differences in dwell time on the eyes were found. Moreover, when specifically examining dwell time on the eyes of faces with an emotional expression there were also no significant differences between groups. Overall, participants gazed significantly longer at the eyes in masked compared to unmasked faces.

CONCLUSIONS

For faces without mask, our results suggest that depressiveness in healthy individuals goes along with less visual attention to other persons' eyes but not with less visual attention to others' faces. When factors come into play that generally amplify the attention directed to the eyes such as face masks or emotions then no relationship between depressiveness and visual attention to the eyes can be established.

A systematic review on automated clinical depression diagnosis

Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

Cross-cultural applicability of eye-tracking in assessing attention to emotional faces in preschool-aged children

Humans show an attention bias toward emotional versus neutral information, which is considered an adaptive pattern of information processing. Deviations from this pattern have been observed in children with socially withdrawn behaviors, with most research being conducted in controlled settings among children from urban areas. The goal of the current study was to examine the cross-cultural applicability of two eye-tracking-based measures in assessing attention biases and their relations to children's symptoms of socially withdrawn behaviors in two independent and diverse samples of preschool children. The cross-cultural comparison was conducted between the Navajo Birth Cohort study (NBCS), an indigenous cohort with relatively low socioeconomic status (SES), and the Illinois Kids Development study (IKIDS), a primarily Non-Hispanic White and high SES cohort. Children in both cohorts completed eye-tracking tasks with pictures of emotional faces, and mothers reported on children's symptoms of socially withdrawn behaviors. Results showed that general patterns of attention biases were mostly the same across samples, reflecting heightened attention toward emotional versus neutral faces. The differences across two samples mostly involved the magnitude of attention biases. NBCS children were slower to disengage from happy faces when these emotional faces were paired with neutral faces. Additionally, socially withdrawn children in the NBCS sample showed a pattern of attentional avoidance for emotional faces. The comparability of overall patterns of attention biases provides initial support for the cross-cultural applicability of the eye-tracking measures and demonstrates the robustness of these methods across clinical and community settings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Eyes Have It: Psychotherapy in the Era of Masks

Nonverbal communication is integral to the success of psychotherapy and facial expression is an important component of nonverbal communication. The SARS CoV-2 pandemic has caused alterations in how psychotherapy services are provided. In this paper, potential issues that may arise from conducting psychotherapy when both the patient and therapist are wearing masks are explored. These include higher likelihood of misidentifying facial expression, especially when expression is incongruent with body language, and when the lower face is more important for correct identification of emotion. These issues may be particularly problematic for patient populations for whom emotion recognition may be a problem at baseline, or for those more prone to biases in emotional recognition. Suggestions are made for therapists to consider when seeing patients in-person when masks are necessary.

Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.

Contribution of Eye-Tracking to Study Cognitive Impairments Among Clinical Populations

In the field of psychology, the merge of decision-theory and neuroscientific methods produces an array of scientifically recognized paradigms. For example, by exploring consumer’s eye-movement behavior, researchers aim to deepen the understanding of how patterns of retinal activation are being meaningfully transformed into visual experiences and connected with specific reactions (e.g., purchase). Notably, eye-movements provide knowledge of one’s homeostatic balance and gatekeep information that shape decisions. Hence, vision science investigates the quality of observed environments determined under various experimental conditions. Moreover, it answers questions on how human process visual stimuli and use gained information for a successful strategy to achieve certain goals. While capturing cognitive states with the support of the eye-trackers progresses at a relatively fast pace in decision-making research, measuring the visual performance of real-life tasks, which require complex cognitive skills, is tentatively translated into clinical experiments. Nevertheless, the potential of the human eye as a highly valuable source of biomarkers has been underlined. In this article, we aim to draw readers attention to decision-making experimental paradigms supported with eye-tracking technology among clinical populations. Such interdisciplinary approach may become an important component that will (i) help in objectively illustrating patient’s models of beliefs and values, (ii) support clinical interventions, and (iii) contribute to health services. It is possible that shortly, eye-movement data from decision-making experiments will grant the scientific community a greater understanding of mechanisms underlining mental states and consumption practices that medical professionals consider as obsessions, disorders or addiction.

How Our Gaze Reacts to Another Person's Tears? Experimental Insights Into Eye Tracking Technology

Crying is an ubiquitous human behavior through which an emotion is expressed on the face together with visible tears and constitutes a slippery riddle for researchers. To provide an answer to the question "How our gaze reacts to another person's tears?," we made use of eye tracking technology to study a series of visual stimuli. By presenting an illustrative example through an experimental setting specifically designed to study the "tearing effect," the present work aims to offer methodological insight on how to use eye-tracking technology to study non-verbal cues. A sample of 30 healthy young women with normal visual acuity performed a within-subjects task in which they evaluated images of real faces with and without tears while their eye movements were tracked. Tears were found to be a magnet for visual attention in the task of facial attribution, facilitating a greater perception of emotional intensity. Moreover, the inspection pattern changed qualitatively and quantitatively, with our participants becoming fully focused on the tears when they were visible. The mere presence of a single tear running down a cheek was associated with an increased emotional inference and greater perception of sincerity. Using normalized and validated tools (Reading the Eyes in the Mind Test and the SALAMANCA screening test for personality disorders), we measured the influence of certain characteristics of the participants on their performance of the experimental task. On the one hand, a higher level of cognitive empathy helped to classify tearful faces with higher emotional intensity and tearless faces with less emotional intensity. On the other hand, we observed that less sincerity was attributed to the tearful faces as the SALAMANCA test scores rose in clusters A (strange and extravagant) and B (immature and emotionally unstable) of our sample. The present findings highlight the advantages of using eye tracking technology to study non-verbal cues and draw attention to methodological issues that should be taken into account. Further exploration of the relationship between empathy and tear perception could be a fruitful avenue of future research using eye tracking.