Use of 3D faces facilitates facial expression recognition in children

The inside out model of emotion recognition: how the shape of one's internal emotional landscape influences the recognition of others' emotions

Some people are exceptional at reading emotional expressions, while others struggle. Here we ask whether the way we experience emotion "on the inside" influences the way we expect emotions to be expressed in the "outside world" and subsequently our ability to read others' emotional expressions. Across multiple experiments, incorporating discovery and replication samples, we develop EmoMap (N = 20; N = 271) and ExpressionMap (N = 98; replication N = 193) to map adults' experiences of emotions and visual representations of others' emotions. Some individuals have modular maps, wherein emotional experiences and visual representations are consistent and distinct-anger looks and feels different from happiness, which looks and feels different from sadness. In contrast, others have experiences and representations that are variable and overlapping-anger, happiness, and sadness look and feel similar and are easily confused for one another. Here we illustrate an association between these maps: those with consistent and distinct experiences of emotion also have consistent and distinct visual representations of emotion. Finally (N = 193), we construct the Inside Out Model of Emotion Recognition, which explains 60.8% of the variance in emotion recognition and illuminates multiple pathways to emotion recognition difficulties. These findings have important implications for understanding the emotion recognition difficulties documented in numerous clinical populations.

Autistic adults exhibit highly precise representations of others' emotions but a reduced influence of emotion representations on emotion recognition accuracy

To date, studies have not yet established the mechanisms underpinning differences in autistic and non-autistic emotion recognition. The current study first investigated whether autistic and non-autistic adults differed in terms of the precision and/or differentiation of their visual emotion representations and their general matching abilities, and second, explored whether differences therein were related to challenges in accurately recognizing emotional expressions. To fulfil these aims, 45 autistic and 45 non-autistic individuals completed three tasks employing dynamic point light displays of emotional facial expressions. We identified that autistic individuals had more precise visual emotion representations than their non-autistic counterparts, however, this did not confer any benefit for their emotion recognition. Whilst for non-autistic people, non-verbal reasoning and the interaction between precision of emotion representations and matching ability predicted emotion recognition, no variables contributed to autistic emotion recognition. These findings raise the possibility that autistic individuals are less guided by their emotion representations, thus lending support to Bayesian accounts of autism.

Differences Between Autistic and Non-Autistic Adults in the Recognition of Anger from Facial Motion Remain after Controlling for Alexithymia

To date, studies have not established whether autistic and non-autistic individuals differ in emotion recognition from facial motion cues when matched in terms of alexithymia. Here, autistic and non-autistic adults (N = 60) matched on age, gender, non-verbal reasoning ability and alexithymia, completed an emotion recognition task, which employed dynamic point light displays of emotional facial expressions manipulated in terms of speed and spatial exaggeration. Autistic participants exhibited significantly lower accuracy for angry, but not happy or sad, facial motion with unmanipulated speed and spatial exaggeration. Autistic, and not alexithymic, traits were predictive of accuracy for angry facial motion with unmanipulated speed and spatial exaggeration. Alexithymic traits, in contrast, were predictive of the magnitude of both correct and incorrect emotion ratings.

Objectively Measuring Observer Attention in Severe Thyroid-Associated Orbitopathy: A 3D Study

OBJECTIVE

Measure the attentional distraction of facial deformity related to severe thyroid-associated orbitopathy using three-dimensional (3D) images and eye-tracking technology.

METHODS

Observers recruited at an academic tertiary referral center viewed 3D facial images of patients with severe thyroid-associated orbitopathy (TAO) and controls without TAO. An infrared eye-tracking monitor recorded their eye movements and fixations in real time. Multivariate Hotelling's analysis, followed by planned posthypothesis testing, was used to compare fixation durations for predefined regions of interest, including the eyes, nose, mouth, central triangle, and remaining face without the central triangle between severe TAO patients and controls.

RESULTS

One hundred sixteen observers (mean age 26.4 years, 51% female) successfully completed the eye-tracking experiment. The majority of their attention was directed toward the central triangle (eyes, nose, mouth). On multivariate analysis, there were significant differences in the distribution of attention between control and severe TAO faces (T²  = 49.37; F(5,922) = 9.8314, P < 0.0001). On planned posthypothesis testing, observers attended significantly more to the eyes (0.77 seconds, P < 0.0001, 95% confidence interval [CI], 0.51, 1.03 seconds) and less to the nose (-0.42 seconds, P < 0.0001, 95% CI, -0.23, -0.62 seconds) in severe TAO patients. There was no significant difference in time spent on the mouth, the total time spent on the central triangle, or time spent in the remaining face between the two groups.

CONCLUSION

Severe TAO distracted observer attention toward the eyes compared to control patients. These data lend insight into how TAO may alter observers' perceptions of these patients. Future studies should investigate how these changes in observer gaze patterns may reflect the social perception of TAO patients.

LEVEL OF EVIDENCE

NA Laryngoscope, 129:1250-1254, 2019.