The integration of visual context information in facial emotion recognition in 5- to 15-year-olds

Theurel, Anne; Witt, Arnaud; Malsert, Jennifer; Lejeune, Fleur; Fiorentini, Chiara; Barisnikov, Koviljka; Gentaz, Édouard

doi:10.1016/j.jecp.2016.06.004

Cited by 41 publications

(40 citation statements)

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“…Behaviorally, facial emotion identification emerges early in infancy with continued improvements through childhood and adolescence (Batty and Taylor, 2006;Thomas et al, 2007;Rodger et al, 2015;Leitzke and Pollak, 2016;Theurel et al, 2016). We found that, although emotion identification accuracy was high for all five emotion categories, even in the youngest individuals (age 8), identification of fear and anger emotion categories improved significantly with age, consistent with previous reports (Gee et al, 2013;Kujawa et al, 2016;Wu et al, 2016).…”

Section: Maturation Of Emotion Identification and Specificity With Resupporting

confidence: 90%

“…Identification of distinct facial expressions, which is fundamental to recognizing the emotional state of others, begins in infancy and continues to develop throughout childhood and adolescence (Batty and Taylor, 2006;Thomas et al, 2007;Rodger et al, 2015;Leitzke and Pollak, 2016;Theurel et al, 2016). How emotions are identified and categorized in the developing brain remains a fundamental open question.…”

Section: Introductionmentioning

confidence: 99%

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Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

et al. 2019

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Emotion perception is fundamental to affective and cognitive development and is thought to involve distributed brain circuits. Efforts to chart neurodevelopmental changes in emotion have been severely hampered by narrowly focused approaches centered on activation of individual brain regions and small sample sizes. Here we investigate the maturation of human functional brain circuits associated with identification of fearful, angry, sad, happy, and neutral faces using a large sample of 759 children, adolescents, and adults (ages 8-23; female/male ϭ 419/340). Network analysis of emotion-related brain circuits revealed three functional modules, encompassing lateral frontoparietal, medial prefrontal-posterior cingulate, and subcortical-posterior insular cortices, with hubs in medial prefrontal, but not posterior cingulate, cortex. This overall network architecture was stable by age 8, and it anchored maturation of circuits important for salience detection and cognitive control, as well as dissociable circuit patterns across distinct emotion categories. Our findings point to similarities and differences in functional circuits associated with identification of fearful, angry, sad, happy, and neutral faces, and reveal aspects of brain circuit organization underlying emotion perception that are stable over development as well as features that change with age. Reliability analyses demonstrated the robustness of our findings and highlighted the importance of large samples for probing functional brain circuit development. Our study emphasizes a need to focus beyond amygdala circuits and provides a robust neurodevelopmental template for investigating emotion perception and identification in psychopathology.

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Section: Maturation Of Emotion Identification and Specificity With Resupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

et al. 2019

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“…In sighted children, facial expressions, gestures and vocalisations are perceived in an integrated package that allows them to make connections between different facets of emotions and how emotions are related to behaviors (Minter, Hobson, & Pring, 1991 ). The role of context in the perception of emotion was highlighted in a recent study about facial emotion recognition by sighted children (Theurel et al 2016 ). Thus, blind children who have limited input in this regard may have more difficulty in differentiating emotional tones of voice when these are presented in an isolated manner.…”

Section: General Discussion and Perspectivesmentioning

confidence: 99%

The role of visual experience in the production of emotional facial expressions by blind people: a review

2017

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Facial expressions of emotion are nonverbal behaviors that allow us to interact efficiently in social life and respond to events affecting our welfare. This article reviews 21 studies, published between 1932 and 2015, examining the production of facial expressions of emotion by blind people. It particularly discusses the impact of visual experience on the development of this behavior from birth to adulthood. After a discussion of three methodological considerations, the review of studies reveals that blind subjects demonstrate differing capacities for producing spontaneous expressions and voluntarily posed expressions. Seventeen studies provided evidence that blind and sighted spontaneously produce the same pattern of facial expressions, even if some variations can be found, reflecting facial and body movements specific to blindness or differences in intensity and control of emotions in some specific contexts. This suggests that lack of visual experience seems to not have a major impact when this behavior is generated spontaneously in real emotional contexts. In contrast, eight studies examining voluntary expressions indicate that blind individuals have difficulty posing emotional expressions. The opportunity for prior visual observation seems to affect performance in this case. Finally, we discuss three new directions for research to provide additional and strong evidence for the debate regarding the innate or the culture-constant learning character of the production of emotional facial expressions by blind individuals: the link between perception and production of facial expressions, the impact of display rules in the absence of vision, and the role of other channels in expression of emotions in the context of blindness.

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“…The CINET is inspired by the context effect, which is the influence of the surrounding environment on the perception of stimuli [1][2][3]. Numerous studies related to the context effect have shown that the integration of contextual information improves the interpretation of spoken words [4,5], written letters and words [6][7][8], physical objects [9][10][11], sounds [12,13], smells [14], tastes [15], threats [16], colors [17], and facial emotions [18][19][20]. The context effect has also been widely studied to show how contextual information is used to uniquely resolve the interpretation of ambiguous stimuli [7,8,[21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

CINET: A Brain-Inspired Deep Learning Context-Integrating Neural Network Model for Resolving Ambiguous Stimuli

Amerineni

Gupta

2020

Brain Sciences

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The brain uses contextual information to uniquely resolve the interpretation of ambiguous stimuli. This paper introduces a deep learning neural network classification model that emulates this ability by integrating weighted bidirectional context into the classification process. The model, referred to as the CINET, is implemented using a convolution neural network (CNN), which is shown to be ideal for combining target and context stimuli and for extracting coupled target-context features. The CINET parameters can be manipulated to simulate congruent and incongruent context environments and to manipulate target-context stimuli relationships. The formulation of the CINET is quite general; consequently, it is not restricted to stimuli in any particular sensory modality nor to the dimensionality of the stimuli. A broad range of experiments is designed to demonstrate the effectiveness of the CINET in resolving ambiguous visual stimuli and in improving the classification of non-ambiguous visual stimuli in various contextual environments. The fact that the performance improves through the inclusion of context can be exploited to design robust brain-inspired machine learning algorithms. It is interesting to note that the CINET is a classification model that is inspired by a combination of brain’s ability to integrate contextual information and the CNN, which is inspired by the hierarchical processing of information in the visual cortex.

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The integration of visual context information in facial emotion recognition in 5- to 15-year-olds

Cited by 41 publications

References 68 publications

Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness

The role of visual experience in the production of emotional facial expressions by blind people: a review

CINET: A Brain-Inspired Deep Learning Context-Integrating Neural Network Model for Resolving Ambiguous Stimuli

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