Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development / Tiffany M. SHADER in Development and Psychopathology, 30-1 (February 2018)  

Public ISBD [article]  inDevelopment and Psychopathology > 30-1 (February 2018) . - p.351-366 Titre : Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development Type de document : Texte imprimé et/ou numérique Auteurs : Tiffany M. SHADER, Auteur ; Lisa M. GATZKE-KOPP, Auteur ; Sheila E. CROWELL, Auteur ; M. JAMILA REID, Auteur ; Julian F. THAYER, Auteur ; Michael W. VASEY, Auteur ; Carolyn WEBSTER-STRATTON, Auteur ; Ziv BELL, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.351-366 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Low resting respiratory sinus arrhythmia (RSA), and to a lesser extent excessive RSA reactivity to emotion evocation, are observed in many psychiatric disorders characterized by emotion dysregulation, including syndromes spanning the internalizing and externalizing spectra, and other conditions such as nonsuicidal self-injury. Nevertheless, some inconsistencies exist. For example, null outcomes in studies of RSA–emotion dysregulation relations are sometimes observed among younger participants. Such findings may derive from use of age inappropriate frequency bands in calculating RSA. We combine data from five published samples (N = 559) spanning ages 4 to 17 years, and reanalyze RSA data using age-appropriate respiratory frequencies. Misspecifying respiratory frequencies results in overestimates of resting RSA and underestimates of RSA reactivity, particularly among young children. Underestimates of developmental shifts in RSA and RSA reactivity from preschool to adolescence were also observed. Although correlational analyses revealed weak negative associations between resting RSA and aggression, those with clinical levels of externalizing exhibited lower resting RSA than their peers. No associations between RSA reactivity and externalizing were observed. Results confirm that age-corrected frequency bands should be used when estimating RSA, and that literature-wide overestimates of resting RSA, underestimates of RSA reactivity, and underestimates of developmental shifts in RSA and RSA reactivity may exist. En ligne : https://doi.org/10.1017/S0954579417000669 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=336 [article] Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development [Texte imprimé et/ou numérique] / Tiffany M. SHADER, Auteur ; Lisa M. GATZKE-KOPP, Auteur ; Sheila E. CROWELL, Auteur ; M. JAMILA REID, Auteur ; Julian F. THAYER, Auteur ; Michael W. VASEY, Auteur ; Carolyn WEBSTER-STRATTON, Auteur ; Ziv BELL, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.351-366. Langues : Anglais (eng) in Development and Psychopathology > 30-1 (February 2018) . - p.351-366 Index. décimale : PER Périodiques Résumé : Low resting respiratory sinus arrhythmia (RSA), and to a lesser extent excessive RSA reactivity to emotion evocation, are observed in many psychiatric disorders characterized by emotion dysregulation, including syndromes spanning the internalizing and externalizing spectra, and other conditions such as nonsuicidal self-injury. Nevertheless, some inconsistencies exist. For example, null outcomes in studies of RSA–emotion dysregulation relations are sometimes observed among younger participants. Such findings may derive from use of age inappropriate frequency bands in calculating RSA. We combine data from five published samples (N = 559) spanning ages 4 to 17 years, and reanalyze RSA data using age-appropriate respiratory frequencies. Misspecifying respiratory frequencies results in overestimates of resting RSA and underestimates of RSA reactivity, particularly among young children. Underestimates of developmental shifts in RSA and RSA reactivity from preschool to adolescence were also observed. Although correlational analyses revealed weak negative associations between resting RSA and aggression, those with clinical levels of externalizing exhibited lower resting RSA than their peers. No associations between RSA reactivity and externalizing were observed. Results confirm that age-corrected frequency bands should be used when estimating RSA, and that literature-wide overestimates of resting RSA, underestimates of RSA reactivity, and underestimates of developmental shifts in RSA and RSA reactivity may exist. En ligne : https://doi.org/10.1017/S0954579417000669 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=336

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research / Nathaniel HAINES in Development and Psychopathology, 31-3 (August 2019)  

Public ISBD [article]  inDevelopment and Psychopathology > 31-3 (August 2019) . - p.871-886 Titre : Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research Type de document : Texte imprimé et/ou numérique Auteurs : Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.871-886 Langues : Anglais (eng) Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403 [article] Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research [Texte imprimé et/ou numérique] / Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.871-886. Langues : Anglais (eng) in Development and Psychopathology > 31-3 (August 2019) . - p.871-886 Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403

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