Comparison with others influences encoding and recognition of their faces: Behavioural and ERP evidence

In daily life, faces are often memorized within contexts involving interpersonal interactions. However, little is known about whether interpersonal interaction-related contexts influence face memory. The present study aimed to understand this question by investigating how social comparison-related context affects face encoding and recognition. To address this issue, 40 participants were informed that they and another player each played a monetary game and were then presented with both of their outcomes (either monetary gain or loss). Subsequently, participants were shown the face of the player whom they were just paired with. After all the faces had been encoded, participants were asked to perform a sudden old/new recognition task involving these faces. The results showed that, during the encoding phase, another player's monetary gain, compared to loss, resulted in more negative responses in the N170 and early posterior negativity (EPN)/N250 to relevant players' faces when participants encountered monetary loss and a smaller late positive potential (LPP) response irrespective of self-related outcomes. In the subsequent recognition phase, preceding another player's monetary gain as compared to loss led to better recognition performance and stronger EPN/N250 and LPP responses to the faces of relevant players when participants had lost some amount of money. These findings suggest that the social comparison-related context, particularly self-disadvantageous outcomes in the context, influences the memory of comparators' faces.

Kinship identification using age transformation and Siamese network

Facial images are used for kinship verification. Traditional convolutional neural networks and transfer learning-based approaches are presently used for kinship identification. The transfer-learning approach is useful in many fields. However, it does not perform well in the identification of humans' kinship because transfer-learning models are trained on a different type of data that is significantly different as compared to human face image data, a technique which may be able for kinship identification by comparing images of parents and their children with transformed age instead of comparing their actual images is required. In this article, a technique for kinship identification using a Siamese neural network and age transformation algorithm is proposed. The results are satisfactory as an overall accuracy of 76.38% has been achieved. Further work can be carried out to improve the accuracy by improving the Life Span Age Transformation (LAT) algorithm for kinship identification using facial images.

Categorization training changes the visual representation of face identity

Previous research suggests that learning to categorize faces along a new dimension changes the perceptual representation of that dimension, but little is known about how the representation of specific face identities changes after such category learning. Here, we trained participants to categorize faces that varied along two morphing dimensions. One dimension was relevant to the categorization task and the other was irrelevant. We used reverse correlation to estimate the internal templates used to identify the two faces at the extremes of the relevant dimension, both before and after training, and at two different levels of the irrelevant dimension. Categorization training changed the internal templates used for face identification, even though identification and categorization tasks impose different demands on the observers. After categorization training, the internal templates became more invariant across changes in the irrelevant dimension. These results suggest that the representation of face identity can be modified by categorization experience.

Mapping face encoding using functional MRI in multiple sclerosis across disease phenotypes

Using fMRI during a face encoding (FE) task, we investigated the behavioral and fMRI correlates of FE in patients with relapse-onset multiple sclerosis (MS) at different stages of the disease and their relation with attentive-executive performance and structural MRI measures of disease-related damage. A fMRI FE task was administered to 75 MS patients (11 clinically isolated syndromes - CIS, 40 relapsing-remitting - RRMS - and 24 secondary progressive - SPMS) and 22 healthy controls (HC). fMRI activity during the face encoding condition was correlated with behavioral, clinical, neuropsychological and structural MRI variables. All study subjects activated brain regions belonging to face perception and encoding network, and deactivated areas of the default-mode network. Compared to HC, MS patients had the concomitant presence of areas of increased and decreased activations as well as increased and decreased deactivations. Compared to HC or RRMS, CIS patients experienced an increased recruitment of posterior-visual areas. Thalami, para-hippocampal gyri and right anterior cingulum were more activated in RRMS vs CIS or SPMS patients, while an increased recruitment of frontal areas was observed in SPMS vs RRMS. Areas of abnormal activations were significantly correlated with clinical, cognitive-behavioral and structural MRI measures. Abnormalities of FE network occur in MS and vary across disease clinical phenotypes. Early in the disease, an increased recruitment of areas typically devoted to face perception and encoding occurs. In SPMS patients, abnormal functional recruitment of frontal lobe areas might contribute to the severity of clinical manifestations.