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

Reed, Lawrence Ian; Sayette, Michael A.; Cohn, Jeffrey F.

doi:10.1037/0021-843x.116.4.804

Cited by 71 publications

(55 citation statements)

References 42 publications

(54 reference statements)

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“…2) Automatic smile descriptors: Additionally, findings in [13] support that a reduced number of smiles can be observed in subjects with psychological disorders. However, this could not be confirmed for the number of smiles and laughter of depressed subjects in [27], but an increased amount of masking was observed. Further, [18] found less genuine smiles in PTSD patients.…”

Section: Research Goalscontrasting

confidence: 68%

“…These descriptors should be designed to support the diagnosis or treatment performed by a clinician; no descriptor is completely diagnostic by itself, but they show tendencies in people's behaviors. A promising result in this direction is the recent work of Cohn and colleagues who studied facial expressions and vocal patterns related to depression [27], [9].…”

Section: Introductionmentioning

confidence: 96%

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Automatic behavior descriptors for psychological disorder analysis

Scherer

Stratou

Mahmoud

et al. 2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

106

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We investigate the capabilities of automatic nonverbal behavior descriptors to identify indicators of psychological disorders such as depression, anxiety, and post-traumatic stress disorder. We seek to confirm and enrich present state of the art, predominantly based on qualitative manual annotations, with automatic quantitative behavior descriptors. In this paper, we propose four nonverbal behavior descriptors that can be automatically estimated from visual signals. We introduce a new dataset called the Distress Assessment Interview Corpus (DAIC) which includes 167 dyadic interactions between a confederate interviewer and a paid participant. Our evaluation on this dataset shows correlation of our automatic behavior descriptors with specific psychological disorders as well as a generic distress measure. Our analysis also includes a deeper study of selfadaptor and fidgeting behaviors based on detailed annotations of where these behaviors occur. I. INTRODUCTIONThe recent progress in facial feature tracking and articulated body tracking [2], [25], [34] has opened the door to new applications for automatic nonverbal behavior analysis. One promising direction for this technology is the medical domain where computer vision algorithms can assist clinicians and health care providers in their daily activities. For example, these new perceptual software can assist doctors during remote telemedicine sessions that lack the communication cues provided in face-to-face interactions. Automatic behavior descriptors can further add quantitative information to the interactions such as behavior dynamics and intensities. These quantitative data can improve both post-session and online analysis. Proper sensing of nonverbal cues can also provide support for an interactive virtual coach able to offer advice based on perceived indicators of distress or anxiety.A key challenge when building such nonverbal perception technology is to develop and validate robust descriptors of human behaviors that are correlated with psychological disorders such as depression, anxiety or post-traumatic stress disorder (PTSD). These descriptors should be designed to support the diagnosis or treatment performed by a clinician; no descriptor is completely diagnostic by itself, but they show tendencies in people's behaviors. A promising result in this direction is the recent work of Cohn and colleagues who studied facial expressions and vocal patterns related to depression [27], [9].

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Section: Research Goalscontrasting

confidence: 68%

Section: Introductionmentioning

confidence: 96%

Automatic behavior descriptors for psychological disorder analysis

Scherer

Stratou

Mahmoud

et al. 2013

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

106

View full text Add to dashboard Cite

show abstract

“…Individuals with depression or dysphoria, in comparison with nondepressed individuals, are less likely to smile in response to pictures or movies of smiling faces and affectively positive social imagery (Gehricke & Shapiro 2000;Sloan et al 2002). When they do smile, they are more likely to damp their facial expression (Reed et al 2007). …”

Section: Discussionmentioning

confidence: 99%

Effects of damping head movement and facial expression in dyadic conversation using real–time facial expression tracking and synthesized avatars

Boker

Cohn

Theobald

et al. 2009

Phil. Trans. R. Soc. B

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When people speak with one another, they tend to adapt their head movements and facial expressions in response to each others' head movements and facial expressions. We present an experiment in which confederates' head movements and facial expressions were motion tracked during videoconference conversations, an avatar face was reconstructed in real time, and naive participants spoke with the avatar face. No naive participant guessed that the computer generated face was not video. Confederates' facial expressions, vocal inflections and head movements were attenuated at 1 min intervals in a fully crossed experimental design. Attenuated head movements led to increased head nods and lateral head turns, and attenuated facial expressions led to increased head nodding in both naive participants and confederates. Together, these results are consistent with a hypothesis that the dynamics of head movements in dyadicconversation include a shared equilibrium. Although both conversational partners were blind to the manipulation, when apparent head movement of one conversant was attenuated, both partners responded by increasing the velocity of their head movements.

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“…In depression and BPD this particularly manifests in attenuation of positive expression. One hypothesis for this result in depression was explored in a study by Reed et al (2007). The authors examined whether the attenuated response to positive stimuli was related to how depressed people appraise emotional stimuli (based on research which suggests that appraisals of emotional stimuli may be different to non-depressed individuals).…”

Section: Discussionmentioning

confidence: 99%