Impact of depression on response to comedy: A dynamic facial coding analysis

Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Journal of Abnormal Psychology (Impact Factor: 4.86). 11/2007; 116(4):804-9. DOI: 10.1037/0021-843X.116.4.804
Source: PubMed


Individuals suffering from depression show diminished facial responses to positive stimuli. Recent cognitive research suggests that depressed individuals may appraise emotional stimuli differently than do nondepressed persons. Prior studies do not indicate whether depressed individuals respond differently when they encounter positive stimuli that are difficult to avoid. The authors investigated dynamic responses of individuals varying in both history of major depressive disorder (MDD) and current depressive symptomatology (N = 116) to robust positive stimuli. The Facial Action Coding System (Ekman & Friesen, 1978) was used to measure affect-related responses to a comedy clip. Participants reporting current depressive symptomatology were more likely to evince affect-related shifts in expression following the clip than were those without current symptomatology. This effect of current symptomatology emerged even when the contrast focused only on individuals with a history of MDD. Specifically, persons with current depressive symptomatology were more likely than those without current symptomatology to control their initial smiles with negative affect-related expressions. These findings suggest that integration of emotion science and social cognition may yield important advances for understanding depression.

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    • "Recording sessions with patients to analyse the behavioural changes were commonly used in research described by (Reed et al. 2007, Girard et al. 2014, Hurley and Frank 2011). The gathered material proved to be very useful in diagnosis process and mental state evaluation of the patient. "
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    ABSTRACT: Many affective disorders can influence the capacity of emotion expression and understanding. For instance, in the case of depression, the frequency and intensity of smiling is diminishing when the disease proceeds. Analysing these changes by a psychologist could bring information about the effectiveness of the prescribed medical treatment. Therefore, this work describes a system which automatically analyses video recordings from sessions with patients and determines the length and intensity of smile. The proposed system exploits a standard database to train the classifier, which is later used to distinguish between the smiling and neutral facial expression depicted in the video frames. The obtained information is presented graphically, indicating the class membership and temporal smile intensity. The performed experiments revealed that the classification accuracy exceeds 85%. Moreover, the case studies show, that the variations in smile magnitude enable better insight into the smile analysis.
    Computational Vision and Medical Image Processing V, Edited by Joao Tavares & Natal Jorge, 01/2016: pages 347-353; Taylor & Francis Group., ISBN: 978-1-138-02926-2
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    • "Studies in clinical populations have shown differences in the expression of DS in response to positive stimuli, compared to healthy controls (HC). For example, individuals suffering from depression have been found to produce fewer DS when exposed to positive stimuli (Ekman et al., 2005) and to show smiles that were followed by negative affect-related expressions in response to amusing film clips (Reed et al., 2007). Participants with schizophrenia display fewer DS when induced to feel positive emotions through remembering biographic emotional situations (Kohler et al., 2008). "
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    ABSTRACT: A large body of research has associated Eating Disorders with difficulties in socio-emotional functioning and it has been argued that they may serve to maintain the illness. This study aimed to explore facial expressions of positive emotions in individuals with Anorexia Nervosa (AN) and Bulimia Nervosa (BN) compared to healthy controls (HC), through an examination of the Duchenne smile (DS), which has been associated with feelings of enjoyment, amusement and happiness (Ekman et al., 1990). Sixty participants (AN=20; BN=20; HC=20) were videotaped while watching a humorous film clip. The duration and intensity of DS were subsequently analyzed using the facial action coding system (FACS) (Ekman and Friesen, 2003). Participants with AN displayed DS for shorter durations than BN and HC participants, and their DS had lower intensity. In the clinical groups, lower duration and intensity of DS were associated with lower BMI, and use of psychotropic medication. The study is the first to explore DS in people with eating disorders, providing further evidence of difficulties in the socio-emotional domain in people with AN. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Psychiatry Research 08/2015; 230(1). DOI:10.1016/j.psychres.2015.08.019 · 2.47 Impact Factor
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    • "Much research using FACS has focused on the occurrence and AU composition of different expressions (Ekman and Rosenberg, 2005). For example, smiles that recruit the orbicularis oculi muscle (i.e., AU 6) are more likely to occur during pleasant circumstances (Ekman et al., 1990, Frank et al., 1993) and smiles that recruit the buccinator muscle (i.e., AU 14) are more likely to occur during active depression (Reed et al., 2007, Girard et al., 2013). A promising subset of research has begun to focus on what can be learned about and from the intensity of expressions. "
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    ABSTRACT: Both the occurrence and intensity of facial expressions are critical to what the face reveals. While much progress has been made towards the automatic detection of facial expression occurrence, controversy exists about how to estimate expression intensity. The most straight-forward approach is to train multiclass or regression models using intensity ground truth. However, collecting intensity ground truth is even more time consuming and expensive than collecting binary ground truth. As a shortcut, some researchers have proposed using the decision values of binary-trained maximum margin classifiers as a proxy for expression intensity. We provide empirical evidence that this heuristic is flawed in practice as well as in theory. Unfortunately, there are no shortcuts when it comes to estimating smile intensity: researchers must take the time to collect and train on intensity ground truth. However, if they do so, high reliability with expert human coders can be achieved. Intensity-trained multiclass and regression models outperformed binary-trained classifier decision values on smile intensity estimation across multiple databases and methods for feature extraction and dimensionality reduction. Multiclass models even outperformed binary-trained classifiers on smile occurrence detection.
    Pattern Recognition Letters 10/2014; DOI:10.1016/j.patrec.2014.10.004 · 1.55 Impact Factor
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