Modeling physiological responses induced by an emotion recognition task using latent class mixed models

Autor: Riccardo Maria Martoni, Clelia Di Serio, Federica Cugnata, Manuela Ferrario, Chiara Brombin
Přispěvatelé: Cugnata, F., Martoni, R. M., Ferrario, M., Di Serio, C., Brombin, C.
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Genetics and Molecular Biology (all)
Male
Psychometrics
Physiology
Emotions
lcsh:Medicine
Social Sciences
Anxiety
Biochemistry
Facial recognition system
0302 clinical medicine
Cognition
Learning and Memory
Biochemistry
Genetics and Molecular Biology (all)
Agricultural and Biological Sciences (all)
Heart Rate
Medicine and Health Sciences
Heart rate variability
Psychology
lcsh:Science
Class (computer programming)
Multidisciplinary
Depression
05 social sciences
Middle Aged
Facial Expression
Social Skill
Female
Anatomy
Facial Recognition
Cognitive psychology
Human
Research Article
Nervous System Physiology
Adult
Adolescent
Cardiology
Face Recognition
Models
Biological
050105 experimental psychology
Social Skills
03 medical and health sciences
Young Adult
Memory
Mental Health and Psychiatry
Humans
0501 psychology and cognitive sciences
Aged
Emotion
Facial expression
Psychological Tests
Rasch model
Models
Statistical
Mood Disorders
lcsh:R
Univariate
Cognitive Psychology
Biology and Life Sciences
Psychological Test
Face
Cognitive Science
lcsh:Q
Perception
Head
030217 neurology & neurosurgery
Neuroscience
Zdroj: PLoS ONE
PLoS ONE, Vol 13, Iss 11, p e0207123 (2018)
ISSN: 1932-6203
Popis: Correctly recognizing emotions is an essential skill to manage interpersonal relationships in everyday life. Facial expression represents the most powerful mean to convey important information on emotional and cognitive states during interactions with others. In this paper, we analyze physiological responses triggered by an emotion recognition test, which requires the processing of facial cues. In particular, we evaluate the modulation of several Heart Rate Variability indices, collected during the Reading the Mind in the Eyes Test, accounting for test difficulty (derived from a Rasch analysis), test performances, demographic and psychological characteristics of the participants. The main idea is that emotion recognition is associated with the Autonomic Nervous System and, as a consequence, with the Heart Rate Variability. The principal goal of our study was to explore the complexity of the collected measures and their possible interactions by applying a class of flexible models, i.e., the latent class mixed models. Actually, this modelling strategy allows for the identification of clusters of subjects characterized by similar longitudinal trajectories. Both univariate and multivariate latent class mixed models were used. In fact, while the interpretation of the Heart Rate Variability indices is very difficult when considered individually, a joint evaluation provides a better description of the Autonomic Nervous System state.
Databáze: OpenAIRE
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