Profiles on the Orientation Discrimination Processing of Human Faces

The Relationship between Face Processing, Cognitive and Affective Empathy

This study aims to examine the relationship between affective and cognitive empathy scores and perceptual face recognition skills. A total of 18 young adults participated in the study. Cognitive and Affective Empathy Test (TECA), The eyes Test and an experimental task were carried out. The experimental task has two blocks, a presentation, and a recognition phase, under the Karolinska battery of images expressing different emotions. Cognitive empathy sub-factors were found to be related to the hit rate on the recognition of surprise faces as well as the discarding of faces of disgust. In relation to the hit rate on discarding faces of disgust, this was related to perspective taking. Reaction time and Cognitive empathy subfactors were found to be positively correlated to the recognition of disgust, surprise, and sadness. Lastly, Perspective taking was also related to the discarding of disgust reaction time in a direct way. The relationships between affective empathy and other measures for emotional face recognition were not statistically significant. Knowledge of individual differences in cognitive and affective empathy, as well as of their relationship with behavioral responses such as the recognition or dismissal of emotional faces are of interest for social interaction and in psychotherapy.

Word and Face Recognition Processing Based on Response Times and Ex-Gaussian Components

The face is a fundamental feature of our identity. In humans, the existence of specialized processing modules for faces is now widely accepted. However, identifying the processes involved for proper names is more problematic. The aim of the present study is to examine which of the two treatments is produced earlier and whether the social abilities are influent. We selected 100 university students divided into two groups: Spanish and USA students. They had to recognize famous faces or names by using a masked priming task. An analysis of variance about the reaction times (RT) was used to determine whether significant differences could be observed in word or face recognition and between the Spanish or USA group. Additionally, and to examine the role of outliers, the Gaussian distribution has been modified exponentially. Famous faces were recognized faster than names, and differences were observed between Spanish and North American participants, but not for unknown distracting faces. The current results suggest that response times to face processing might be faster than name recognition, which supports the idea of differences in processing nature.

Personality and Attitudes Confronting Death Awareness During the COVID-19 Outbreak in Italy and Spain

Italy and Spain are two representative examples on strict lockdown last March 2020, also suffering a high rate of mortality in Europe. The aim of this study is to examine their attitudes confronting death awareness during the Covid-19 outbreak. Moreover, Personality was also considered. Different sociodemographic, in situ questions related to attitudes and the brief Big Five of Personality were employed in a cross-sectional design. The main results suggested that Personality traits were stable across countries. A relationship was found between Fear to contagious diseases and Neuroticism and other attitudes during the Covid-19 outbreak, and two different clusters were identified with regards to attitudes, however these did not differ on Personality. Finally, a Cluster group, Neuroticism, Age and Sense of belonging to the Country did predict Fear to contagious diseases. Of note, no differences were found across countries during grief.

To Google or Not: Differences on How Online Searches Predict Names and Faces

Word and face recognition are processes of interest for a large number of fields, including both clinical psychology and computer calculations. The research examined here aims to evaluate the role of an online frequency’s ability to predict both face and word recognition by examining the stability of these processes in a given amount of time. The study will further examine the differences between traditional theories and current contextual frequency approaches. Reaction times were recorded through both a logarithmic transformation and through a Bayesian approach. The Bayes factor notation was employed as an additional test to support the evidence provided by the data. Although differences between face and name recognition were found, the results suggest that latencies for both face and name recognition are stable for a period of six months and online news frequencies better predict reaction time for both classical frequentist analyses. These findings support the use of the contextual diversity approach.

Visual Scene-Aware Hybrid and Multi-Modal Feature Aggregation for Facial Expression Recognition

Facial expression recognition (FER) technology has made considerable progress with the rapid development of deep learning. However, conventional FER techniques are mainly designed and trained for videos that are artificially acquired in a limited environment, so they may not operate robustly on videos acquired in a wild environment suffering from varying illuminations and head poses. In order to solve this problem and improve the ultimate performance of FER, this paper proposes a new architecture that extends a state-of-the-art FER scheme and a multi-modal neural network that can effectively fuse image and landmark information. To this end, we propose three methods. To maximize the performance of the recurrent neural network (RNN) in the previous scheme, we first propose a frame substitution module that replaces the latent features of less important frames with those of important frames based on inter-frame correlation. Second, we propose a method for extracting facial landmark features based on the correlation between frames. Third, we propose a new multi-modal fusion method that effectively fuses video and facial landmark information at the feature level. By applying attention based on the characteristics of each modality to the features of the modality, novel fusion is achieved. Experimental results show that the proposed method provides remarkable performance, with 51.4% accuracy for the wild AFEW dataset, 98.5% accuracy for the CK+ dataset and 81.9% accuracy for the MMI dataset, outperforming the state-of-the-art networks.