Visual perception of facial expressions of emotion

Martı́nez, Aleix M.

doi:10.1016/j.copsyc.2017.06.009

Cited by 35 publications

(40 citation statements)

References 23 publications

(39 reference statements)

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“…S1 A and Extended Methods, Databases ). Previous work has demonstrated that the pattern of AU activation in these facial expressions is consistent within an emotion category and differential between them ( 5 , 7 , 16 ). But are facial color patterns also consistent within each emotion and differential between emotions?…”

Section: Resultsmentioning

confidence: 92%

“…Emotions are the execution of a number of computations by the nervous system. For over two millennia, studies have evaluated the hypothesis that some of these computations yield visible facial muscle changes (called AUs) specific to each emotion ( 1 , 2 , 4 , 5 , 7 , 22 ).…”

Section: Discussionmentioning

confidence: 99%

“…It has been shown that different combinations of AUs yield facial expressions associated with distinct emotion categories ( 2 , 4 , 5 ), although cultural and individual differences exist ( 6 , 7 ) and context may influence their production and perception ( 8 , 9 ). These facial expressions are easily interpreted by our visual system, even in noisy environments ( 10 β 12 ).…”

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confidence: 99%

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Facial color is an efficient mechanism to visually transmit emotion

Benitez-Quiroz

Srinivasan

Martı́nez

2018

Proc. Natl. Acad. Sci. U.S.A.

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SignificanceEmotions correspond to the execution of a number of computations by the central nervous system. Previous research has studied the hypothesis that some of these computations yield visually identifiable facial muscle movements. Here, we study the supplemental hypothesis that some of these computations yield facial blood flow changes unique to the category and valence of each emotion. These blood flow changes are visible as specific facial color patterns to observers, who can then successfully decode the emotion. We present converging computational and behavioral evidence in favor of this hypothesis. Our studies demonstrate that people identify the correct emotion category and valence from these facial colors, even in the absence of any facial muscle movement.

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Section: Resultsmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Facial color is an efficient mechanism to visually transmit emotion

Benitez-Quiroz

Srinivasan

Martı́nez

2018

Proc. Natl. Acad. Sci. U.S.A.

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“…It is necessary to highlight, however, that emotion-decoding abilities, perspective taking and emotional empathy (personal distress and empathic concern) involve different cognitive processes and, furthermore, are sustained by different brain systems (Martinez, 2017;Nyhus & Barcel o, 2009;Sato et al, 2016;Stemme, Deco, & Busch, 2007). Therefore, each of them requires different paths and techniques for improvement.…”

Section: Discussionmentioning

confidence: 99%

Improving empathy with motivational strategies in batterer intervention programmes: Results of a randomized controlled trial

Romero‐Martínez

Lila

Gracia

et al. 2018

British J Clinic Psychol

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Objectives Empathy (i.e., the ability to decode emotions, as well as cognitive and emotional empathy) is involved in moral reasoning, prosocial behaviour, social and emotional adequacy, mood and behaviour regulation. Hence, alterations in these functions could reduce behaviour control and the adoption of specific types of violence such as intimate partner violence (IPV). Although interventions for IPV perpetrators focus on reducing IPV risk factors and increasing protective factors to prevent this kind of violence, the study of the effectiveness of these programmes in promoting changes in empathy (cognitive and emotional) has been neglected. Design Hence, the main aim of this study was to compare the effectiveness of two different modalities of IPV intervention programmes (Standard Batterer Intervention Programs [SBIP] vs. SBIP + Individualized Motivational Plan [IMP]) in promoting empathic improvements after both interventions. Method Participants were randomly assigned to receive SBIP (n = 40) or SBIP + IMP (n = 53). The effectiveness of the intervention in the total sample and the group effects were evaluated with general linear model repeated‐measures ANOVA. Results Results revealed that only the IPV perpetrators who received the SBIP + IMP were more accurate in decoding emotional facial signals and presented better cognitive empathy (perspective taking) after the intervention programme. Conclusions Our study reinforces the view that different modalities of IPV intervention might lead to different cognitive outcomes after the intervention. Thus, these results may help professionals to develop specific intervention programmes focused on improving cognitive abilities in order to reduce IPV recidivism. Practitioner points Interventions for batterers’ neglected empathic changes after these programmes. Not enough randomized controlled trials for these kinds of interventions. An improvement in the ability to decode emotions after the intervention programme. An improvement in cognitive empathy (perspective taking) after the intervention programme. Different modalities of IPV intervention might lead to different cognitive outcomes after the intervention.

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“…Humans generally learn from a single sample (Martinez, in press, 2002), not thousands as required by current deep learning algorithms.…”

Section: Deep Learningmentioning

confidence: 99%

Computational Models of Face Perception

Martı́nez

2017

Curr Dir Psychol Sci

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Faces are one of the most important means of communication in humans. For example, a short glance at a person’s face provides information on identity and emotional state. What are the computations the brain uses to solve these problems so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research studies the hypothesis that this algorithm is tasked to solve the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading image features that are invariant to facial expression, pose and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose and illumination. If one defines the physics equations that render an image under different identities, expressions, poses and illuminations, then gaining invariance to these factors is readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust and efficient as humans.

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Visual perception of facial expressions of emotion

Cited by 35 publications

References 23 publications

Facial color is an efficient mechanism to visually transmit emotion

Facial color is an efficient mechanism to visually transmit emotion

Improving empathy with motivational strategies in batterer intervention programmes: Results of a randomized controlled trial

Computational Models of Face Perception

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