Measures of emotion: A review

By Iris B. Mauss, Michael D. Robinson. Cognition & Emotion, 2009

2,043 citations recorded by the source corpus.

A consensual, componential model of emotions conceptualises them as experiential, physiological, and behavioural responses to personally meaningful stimuli. The present review examines this model in terms of whether different types of emotion-evocative stimuli are associated with discrete and invariant patterns of responding in each response system, how such responses are structured, and if such responses converge across different response systems. Across response systems, the bulk of the available evidence favours the idea that measures of emotional responding reflect dimensions rather than discrete states. In addition, experiential, physiological, and behavioural response systems are associated with unique sources of variance, which in turn limits the magnitude of convergence across measures. Accordingly, the authors suggest that there is no “gold standard” measure of emotional responding. Rather, experiential, physiological, and behavioural measures are all relevant to understanding emotion and cannot be assumed to be interchangeable.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/49cae857e442076d74f4512c545ec1323ed9cb10

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Citations in the corpus

EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “Compared with power distribution analysis, connection analysis focuses on the measuring of the interactions on multiple brain regions, which are more likely to treat the cognitive processing as circuits than any brain region considered in isolation [13].”
Acute psychosocial stress: does the emotional stress response correspond with physiological responses? (2012, positive): “A substantial source of unique variance is related to the respective response systems and can reduce the degree of correspondence between them (Mauss and Robinson, 2009), while considerable unexplained variance still remains (Oldehinkel et al.”
The Role of Emotion Regulation in Autism Spectrum Disorder RH: Emotion Regulation in ASD (2013, neutral): “Thus, emotions emanate from an individual’s evaluation of a stimulus.(13,14) The initial strength (i.”
The Definition, Assessment, and Mitigation of State Boredom Within Educational Settings: A Comprehensive Review (2012, background): “A major challenge with self-report measures is that individuals, in many situations, cannot perceive their own emotions (Mauss and Robinson 2009).”
The psychology of emotion regulation: An integrative review (2009, background): “However, the available evidence does not support the existence of discrete emotional states (Mauss & Robinson, in press ; Russell, 2003 ).”
What’s in a Word? Language Constructs Emotion Perception (2013, background): “…catego ries of emotions are not evidenced as consistent and specific pat terns in facial muscle movements (see Barrett, 2006a; Cacioppo et al., 2000; Mauss & Robinson, 2009; Russell et al., 2003), vocal acoustics (Russell et al., 2003), peripheral nervous system activity (Barrett, 2006a; Cacioppo…”
Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “Mauss and Robinson [23] proposed that emotional state is likely to involve circuits rather than any brain region considered in isolation.”
Real-Time EEG-Based Human Emotion Recognition and Visualization (2010, background): “The taxonomy can be seen from two perspectives, dimensional and discrete one [12].”
The brain basis of emotion: A meta-analytic review (2012, positive): “…natural kinds (e.g., Bachorowski & Owren 1995; Barrett 2006b; Barrett et al. 2007a; Barrett et al. 2011; Cacioppo et al. 2000; Ortony & Turner 1990; Mauss & Robinson 2009; Russell et al. 2003), while at the same time being broadly consistent with a psychological construction approach to emotion…”
Dealing with feeling: a meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. (2012, background): “Mauss and Robinson (2009) reviewed measures of emotion, dividing them into experiential (typically, self-reported emotional experience), physiological (e.g., heart rate, EEG), and behavioral (typically, observer ratings of emotional expression) measures.”
Feature Extraction and Selection for Emotion Recognition from EEG (2014, background): “On the other hand, recent studies reach consensus that frontal alpha asymmetry primarily reflects approach versus withdrawal motivation rather than the level of valence [62].”
Real-Time Measurement of Tourists’ Objective and Subjective Emotions in Time and Space (2018, background): “Instead, such data allow for the inference of arousal levels as a component of emotional experience (Mauss and Robinson 2009; Wilhelm and Grossman 2010).”
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals (2018, background): “nition, such as self-report, startle, behavioral responses, and physiological measures [3], [4].”