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Emotion Processing in Child and Adolescent Psychopathology
Published in Cathy Laver-Bradbury, Margaret J.J. Thompson, Christopher Gale, Christine M. Hooper, Child and Adolescent Mental Health, 2021
There are many batteries assessing emotion recognition in children and adolescents, although these measures are not diagnostic, e.g. the Cambridge Mindreading Face-Voice Battery for children (Golan, Baron-Cohen and Hill, 2006), the Diagnostic Analysis of Nonverbal Accuracy (DANVA; Nowicki and Carton, 1993) and the Profile of Nonverbal Sensitivity (PONS, Rosenthal, 1979), using video-clips with faces, voices and body images. The Emotion Recognition Task in the CANTAB battery (Cambridge Cognition Ltd) has been used to assess emotion recognition in clinical populations.
The effects of epilepsy and its treatments on affect and emotion
Published in Howard J. Rosen, Robert W. Levenson, Neurocase, 2020
John D. Hixson, Heidi E. Kirsch
Taken collectively, this work supports the hypothesis that TLE with mesial temporal sclerosis creates functional disruption of cortical networks, particularly those involving emotion recognition and perception. However, the exact nature and extent of this disruption remains unclear. For example, Adolphs, Tranel, Damasio, and Damasio (1995) demonstrated that unilateral amygdala damage (in patients without epilepsy) was not sufficient to cause an emotion recognition deficit. This implies that the emotion processing problems seen in MTS are not caused solely by loss of function in the mesial temporal regions. A recent study by Fowler et al. (2006) examining individuals with TLE and asymmetric amygdala damage also supports this conclusion. In this study, the subjects were patients with TLE and any MRI evidence of amygdala asymmetry, as determined by ³20% difference in volume. This differed from the previously discussed studies that included only participants with clear MTS. Testing for emotion recognition in four domains (visual nonverbal, visual verbal, auditory nonverbal, and auditory verbal), Fowler and others found no consistent recognition deficits across the study group. A minority of individuals did display impairments for the recognition of negatively valenced items, but the authors could not identify any common characteristics in this subgroup. The authors found similar results for prosody perception.
The Menstrual Cycle
Published in Jane M. Ussher, Joan C. Chrisler, Janette Perz, Routledge International Handbook of Women’s Sexual and Reproductive Health, 2019
Joan C. Chrisler, Jenifer A. Gorman
Women tend to be better than men at recognizing the gender of faces and identifying the emotional expression in faces, particularly negative emotions (Schroeder, 2010). To determine whether the menstrual cycle is related to this skill, Derntl et al. (2008) asked women in either their follicular or luteal phases to identify the emotional expression in pictures of faces, including anger, disgust, fear, sadness, happiness, and neutral as their brains were scanned for amygdala activation. Behavioral results showed no differences across cycle phase in accuracy of emotion recognition. There was, however, a negative correlation between amygdala activation and progesterone levels for fearful, neutral, and sad faces. Thus, progesterone levels might mediate a fear-reducing effect, which could improve mood and help with social interactions.
Hybrid deep convolutional model-based emotion recognition using multiple physiological signals
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
As the future direction of this study, the easy-to-adapt emotion recognition service can be productized in several domains. There are many opportunities in digital advertising, such as understanding how consumers engage with their brand content and advertising. Companies or brands can also use this technology in market research to influence brand awareness and purchase intent. Healthcare is another industry that could benefit from emotion recognition technologies. Since some diseases cause sudden emotional changes, these services can facilitate the diagnosis processes of home-oriented patients. In the future, employing various types of pre-trained CNNs such as AlexNet, VGGNet, GoogLeNet, and ResNet will be investigated to solve this problem. In addition, the implementation of another hybrid model with double-GoogLeNet model is planned.
The complex audio visual emotion assessment task (CAVEAT): development of a shorter version for clinical use
Published in Disability and Rehabilitation, 2022
Skye McDonald, Emily Wilson, Travis Wearne, Lillian Darke, Anneli Cassel, Hannah Rosenberg
This study has several limitations. First and foremost, the sample size of studies 1 and 2 were small. While a combined sample of 50 people with TBI is large relative to many studies in this field, there is a need for a much larger sample of normal healthy adults in order to provide meaningful normative data. Access to representative normative data across age groups, genders, levels of education and, ideally, culture, is essential to enhance the clinical and diagnostic utility of assessment instruments. The utility of the CAVEAT short measures would be greatly enhanced by future work to add to the normative data base. Secondly, the small number of items in each short measure, combined with the wide range of emotions depicted has advantages and disadvantages. The advantage, on which we based this study, is the development of an emotion recognition test that samples a wider range of emotions than typically tested. The downside, however, is that there is considerable variability across items, rendering it unlikely that any specific dimension (such as valence) of emotion perception can be reliably assessed. Finally, while the aim of this study was to examine the psychometric properties of these short versions of the CAVEAT in people with TBI, we did not have specific neuropathological lesion data. Consequently, we were unable to assess the extent to which lesions in neural systems known to mediate emotion processes (such as the ventral frontal and temporal lobes) predicted poor performance on the CAVEAT short tests. Future research should address this interesting question.
Error, Reliability and Health-Related Digital Autonomy in AI Diagnoses of Social Media Analysis
Published in The American Journal of Bioethics, 2021
Importantly, such harms can occur even when benefits to the inferences made by emotion recognition technology, such as providing needed psychological help, happen. So, we have additional reasons both to resist this kind of phrenological impulse (Crawford 2021), which invites us to make assumptions about one’s internal states from mere external phenotypic traits and to fully consider the ethical challenges of emotion recognition technologies. We argue that the bioethical reasons are twofold. First, the furtive extraction of private mental states undermines a person’s epistemic status both as a competent knower and as a competent source of testimony to their own experience. Second, the aim of such technologies is always to extract more about a person than they decide to reveal themselves and thus, there is a clear sense in which, such extractive emotion-recognition technologies, diminish one’s status as a person by unveiling and by altering—even if unwantedly—parts of one’s inner and private self. This is particularly the case when these methods are deployed in settings like social media since they often need to be furtive to even function as intended (Zuboff 2019).