Negatively Biased Facial Affect Discernment and Socially Inhibited Behavior in Middle Childhood

Negatively biased facial affect discernment may prompt socially inhibited behavior. Characterizing normative patterns of facial affect discernment across emotions and expression intensity during middle childhood will help to identify subtle, yet meaningful, deviations that may emerge for individuals and potentially negatively impact their social behavior. Facial affect discernment for happy, sad, and angry expressions across low, medium, and high intensities and parent-reported socially inhibited behavior were measured in this study in a sample of 7-10 year-old children (N = 80; 53% female). Discernment accuracy improved with increased expression intensity for all emotions. Specifically, we found a quartic effect for the association between intensity and accuracy for anger and negative quadratics effects with decelerating positive rates of changes for associations between intensity and accuracy for happiness and intensity and accuracy for sadness. Additionally, discernment accuracy for happiness was generally better than for sadness and anger; discernment accuracy for anger was generally better than for sadness. However, at low intensity, discernment accuracy for sadness was comparable to accuracy for happiness but better than for anger. Neither misidentification of neutral and low intensity faces as negative nor discernment accuracy of happiness at low intensity was significantly associated with socially inhibited behaviors. Although accurate discernment of anger and sadness at low intensity was not significantly related to socially inhibited behavior, better discernment accuracy of anger and sadness at medium intensity was significantly related to more socially inhibited behavior. Overall, these results enhance understanding of normative facial affect discernment and its relation to maladaptive social behaviors in middle childhood, a developmental stage at which intervention efforts may prove effective at heading off detrimental outcomes associated with socially inhibited behavior such as loneliness, low self-esteem, peer victimization, social anxiety, and depression that increase in late childhood and adolescence

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall

Development of a Methodology for Predicting Consumer Acceptance and Preference Toward Beverages

Consumer behavior toward food/beverages is influenced by multisensory attribute perceptions as well as emotional experiences. Traditional methods of sensory testing lack the ability to capture emotional responses and as a result, measuring food/beverage-evoked emotions remains a research challenge. There were three objectives of this dissertation study. Firstly, this study aimed to develop prediction models of acceptance of and preference for basic taste solutions using sensory attribute intensities and emotional responses. Secondly, this study aimed to extend the findings of the first objective to develop prediction models of commercially-available vegetable juice products in terms of (a) acceptance and preference under blind-tasting conditions and (b) purchase behavior under informed-tasting conditions. Lastly, this study aimed to determine the influence of individual personality traits on the prediction models of acceptance and preference for basic taste solutions. Combination of explicit measures (self-reported emotions) and implicit measures (facial expressions and autonomic nervous system responses) were used to measure beverage-evoked emotions. Findings from this study suggest that combination of explicit and implicit emotional measures along with sensory attribute intensities can better predict acceptance of and preference toward basic taste solutions or vegetable juice products as compared to individual variables. In addition, combination of sensory attribute intensities and emotional responses along with non-sensory factors provided optimal prediction model of purchase behavior. Finally, individual differences such as personality traits, specifically those associated with extraversion and neuroticism, have potential to influence the prediction models developed to predict consumer behavior. In conclusion, this dissertation study recommends the combined use of explicit and implicit emotional measures, in addition to sensory and/or non-sensory cues, to predict consumer behavior in terms of acceptance, preference, and purchase-related decisions. In addition, it is important to consider individual differences such as personality traits of participants when developing prediction models of consumer behavior using sensory intensities and emotional responses. This dissertation study provides valuable and practical information for better understanding of consumer behavior to sensory scientists, applied-emotion researchers, and food manufacturers