Identifying differences in biased affective information processing in major depression

Gollan, Jackie K.; Pane, Heather T.; McCloskey, Michael S.; Coccaro, Emil F.

doi:10.1016/j.psychres.2007.06.011

Cited by 173 publications

(146 citation statements)

References 30 publications

(24 reference statements)

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“…We would expect to find a difference in intensity ratings for the neutral images between these two groups based on the literature that shows that depressed individuals are more likely to interpret neutral stimuli as negative and healthy individuals to interpret neutral stimuli as positive. Gollan, Pane, McCloskey, and Coccaro (2008) examined differences in affective information processing between healthy and currently depressed individuals using a facial recognition task and found that depression was related to interpreting neutral facial expressions as negative (i.e., sad) whereas healthy participants were more likely to interpret neutral facial expressions as positive (i.e., happy). Based on Gollan et al's (2008) findings one would expect these differences in the perception of neutral images to affect the emotional intensity ratings for these images.…”

Section: Discussionmentioning

confidence: 99%

“…Gollan, Pane, McCloskey, and Coccaro (2008) examined differences in affective information processing between healthy and currently depressed individuals using a facial recognition task and found that depression was related to interpreting neutral facial expressions as negative (i.e., sad) whereas healthy participants were more likely to interpret neutral facial expressions as positive (i.e., happy). Based on Gollan et al's (2008) findings one would expect these differences in the perception of neutral images to affect the emotional intensity ratings for these images. One could also expect these two groups to differ on intensity ratings for the negative images, based on cognitive biases observed in MDD (i.e., "distorted information processing or attentional allocation toward negative stimuli") (Murrough et al, 2011, p. 553).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Emotional memory in pregnant women at risk for postpartum depression

Williams

Becker

McKinnon

et al. 2015

Psychiatry Research

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Emotional memory in pregnant women at risk for postpartum depression

Williams

Becker

McKinnon

et al. 2015

Psychiatry Research

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“…While someone in love sees life through rose-tinted spectacles, the spectacles worn by depressed individuals are grey. This mood congruence effect explains why neutral faces are evaluated as negative, and negative faces as more negative by depressed compared to non-depressed individuals (Demenescu et al 2010;Gollan et al, 2008;Gur et al, 1992). Finally, people with depression generally achieve poorer performances when asked to evaluate emotional faces (George et al, 1998;Gur et al, 1992;Leppänen et al, 2004;Mendlewicz et al, 2005;Rubinow & Post, 1992;Suslow et al, 2001).…”

Section: Emotional Processing In Depressed Mothersmentioning

confidence: 99%

Postnatal Depression and Emotion: The Misfortune of Mother-Infant Interactions

Gil¹,

Droit‐Volet²,

Laval³

et al. 2012

Perinatal Depression

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“…This states that it is a universal and innate human ability to recognize the facial expressions corresponding to the six emotions called basic or primary (happiness, surprise, disgust, sadness, fear, and anger). The effectiveness of people with depression in decoding emotional expressions through photos was investigated with several methodologies, including the morphing task (Bediou et al, 2005;Joormann and Gotlib, 2006;Gilboa-Schechtman et al, 2008;LeMoult et al, 2009;Schaefer et al, 2010;Aldinger et al, 2013), the emotion recognition task (Kan et al, 2004;Leppänen et al, 2004;Gollan et al, 2008Gollan et al, , 2010Uekermann et al, 2008;Wright et al, 2009;Douglas and Porter, 2010;Milders et al, 2010;Naranjo et al, 2011;Punkanen et al, 2011;Péron et al, 2011;Watters and Williams, 2011;Schneider et al, 2012;Schlipf et al, 2013;Chen et al, 2014), the emotion attentional task (Gotlib et al, 2004;Joormann and Gotlib, 2007;Leyman et al, 2007;Kellough et al, 2008;Sanchez et al, 2013;Duque and Vázquez, 2014), the matching task (Milders et al, 2010;Liu et al, 2012;Chen et al, 2014), and the dot-probe detection task (Fritzsche et al, 2010).…”

mentioning

confidence: 99%

“…On this evidence, many studies prove that depressed patients show a labeling (recognition) bias toward negative emotions. More specifically, such investigations reported contradictory results, showing that MDDs can be either faster and/or more accurate (Mandal and Bhattacharya, 1985;Gilboa-Schechtman et al, 2002;Surguladze et al, 2004;Csukly et al, 2010;Milders et al, 2010;Liu et al, 2012) or slower and/or less accurate (Zuroff and Colussy, 1986;Cooley and Nowicki, 1989;Cerroni et al, 2007;Gollan et al, 2008;Csukly et al, 2009;Douglas and Porter, 2010;Anderson et al, 2011;Watters and Williams, 2011;Aldinger et al, 2013) than HC subjects in decoding fear, anger, and, in particular, sadness.…”

mentioning

confidence: 99%

How Major Depressive Disorder Affects the Ability to Decode Multimodal Dynamic Emotional Stimuli

et al. 2016

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Most studies investigating the processing of emotions in depressed patients reported impairments in the decoding of negative emotions. However, these studies adopted static stimuli (mostly stereotypical facial expressions corresponding to basic emotions) which do not reflect the way people experience emotions in everyday life. For this reason, this work proposes to investigate the decoding of emotional expressions in patients affected by recurrent major depressive disorder (RMDD) using dynamic audio/ video stimuli. RMDDs' performance is compared with the performance of patients with adjustment disorder with depressed mood (ADs) and healthy (HCs) subjects. The experiments involve 27 RMDDs (16 with acute depression -RMDD-A and 11 in a compensation phase -RMDD-C), 16 Ads, and 16 HCs. The ability to decode emotional expressions is assessed through an emotion recognition task based on short audio (without video), video (without audio), and audio/video clips. The results show that AD patients are significantly less accurate than HCs in decoding fear, anger, happiness, surprise, and sadness. RMDD-As with acute depression are significantly less accurate than HCs in decoding happiness, sadness, and surprise. Finally, no significant differences were found between HCs and RMDD-Cs in a compensation phase. The different communication channels and the types of emotion play a significant role in limiting the decoding accuracy.

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Identifying differences in biased affective information processing in major depression

Cited by 173 publications

References 30 publications

Emotional memory in pregnant women at risk for postpartum depression

Emotional memory in pregnant women at risk for postpartum depression

Postnatal Depression and Emotion: The Misfortune of Mother-Infant Interactions

How Major Depressive Disorder Affects the Ability to Decode Multimodal Dynamic Emotional Stimuli

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