Females Are More Sensitive to Opponent’s Emotional Feedback: Evidence From Event-Related Potentials

Chen, Xuhai; Yuan, Hongping; Zheng, Tingting; Chang, Ying-Chao; Luo, Yangmei

doi:10.3389/fnhum.2018.00275

Cited by 27 publications

(11 citation statements)

References 71 publications

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“…Gender difference has been widely reported in psychiatric and neurological diseases (Piccinelli and Wilkinson, 2000; Baron-Cohen et al, 2005; Shulman, 2007; Eranti et al, 2013; Lai et al, 2015), cognitive functions (Ren et al, 2009; Ohla and Lundstr, 2013; Yin et al, 2017; Chen et al, 2018) and behaviors (Christov-Moore et al, 2014), while its neurobiological mechanism is unclear yet (Giudice, 2009). As neural function has its structural basis, studying brain neuroanatomy may provide us new insights and understandings of gender difference.…”

Section: Introductionmentioning

confidence: 99%

Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Luo

Hou

Wang

et al. 2019

Front. Hum. Neurosci.

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Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.

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Section: Introductionmentioning

confidence: 99%

Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Luo

Hou

Wang

et al. 2019

Front. Hum. Neurosci.

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“…They showed that congruence between emotional and monetary outcomes lowered the reaction times during decision making 24 . Moreover, another study found that healthy women tend to take more risks after concordant feedback (monetary losses combined with angry faces) 39 .…”

Section: Discussionmentioning

confidence: 99%

Prefrontal activation in suicide attempters during decision making with emotional feedback

et al. 2020

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Emotional feedback, such as faces showing emotions, can influence decision making. Decision making and emotional face processing, mainly mediated by the prefrontal and cingulate cortices, are impaired in suicide attempters. Here, we used functional MRI (fMRI) to study prefrontal activation in suicide attempters during a modified version of the Iowa Gambling Task (IGT) that included emotional face feedback. We randomly distributed the 116 euthymic women (n = 45 suicide attempters, n = 41 affective controls with history of depression without suicide attempt, and n = 30 healthy controls) included in the study in three emotional IGT groups: concordant (safe and risky choices followed by happy and angry faces, respectively), discordant (safe and risky choices followed by angry and happy faces, respectively), and neutral condition (safe and risky choices followed by neutral faces). Considering the two IGT phases (ambiguous and risky), we then analyzed five regions of interest during the risky vs. safe choices: orbitofrontal (OFC), anterior cingulate (ACC), ventrolateral (VLPFC), medial (MPFC) and dorsal prefrontal (DPFC) cortices. We found: (1) impaired decision making and increased DPFC and OFC activation in suicide attempters vs. controls in the discordant condition during the risky phase; (2) reduced VLPFC activation in suicide attempters in the concordant condition during the ambiguous phase; and (3) decreased OFC, ACC and DPFC activation in both control groups in the concordant condition during the ambiguous phase. Suicide attempters showed prefrontal alterations during reward-learning decision making with emotional feedback. Suicide attempters may guide their decisions to avoid social negative feedback despite the expected outcome.

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“…However, targeting the identification of a single emotion that could be related to a specific situation and considering gender-related particularities could result in a more accurate system. This last assertion is based on the idea that women more accurately recognize nonverbal communication or emotional prosody [ 4 ] and are more sensitive to emotional expressions in interpersonal interactions [ 5 ]. These concepts are not considered in any current emotion recognition system using physiological signals presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Fear Recognition for Women Using a Reduced Set of Physiological Signals

Miranda

Canabal

Gutiérrez-Martín

et al. 2021

Sensors

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Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.

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Females Are More Sensitive to Opponent’s Emotional Feedback: Evidence From Event-Related Potentials

Cited by 27 publications

References 71 publications

Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Prefrontal activation in suicide attempters during decision making with emotional feedback

Fear Recognition for Women Using a Reduced Set of Physiological Signals

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