The COVID-19 pandemic is far more than a health crisis: it has unpredictably changed our whole way of life. As suggested by the analysis of economic data on sales, this dramatic scenario has also heavily impacted individuals’ spending levels. To better understand these changes, the present study focused on consumer behavior and its psychological antecedents. Previous studies found that crises differently affect people’s willingness to buy necessities products (i.e., utilitarian shopping) and non-necessities products (i.e., hedonic shopping). Therefore, in examining whether changes in spending levels were associated with changes in consumer behavior, we adopted a fine-grained approach disentangling between necessities and non-necessities. We administered an online survey to 3833 participants (age range 18–64) during the first peak period of the contagion in Italy. Consumer behavior toward necessities was predicted by anxiety and COVID-related fear, whereas consumer behavior toward non-necessities was predicted by depression. Furthermore, consumer behavior toward necessities and non-necessities was predicted by personality traits, perceived economic stability, and self-justifications for purchasing. The present study extended our understanding of consumer behavior changes during the COVID-19 pandemic. Results could be helpful to develop marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings.

Objectives: Previous research has shown that individual differences in Theory of Mind (ToM) are crucial for people's social relationships. However, very few studies have investigated this issue in ageing. The present study was designed to fill this gap and examine the associations between ToM and social relationships in elderly adults. In doing so, this study considered people's relationships with their relatives and friends, and examined the possible moderating role of social motivation. Method: The study involved 53 healthy older adults (age: M = 67.91; SD = 6.93; range: 60--85 years). All participants were tested collectively during a 2-hr session and completed a demographic questionnaire as well as a battery of tests assessing verbal ability (vocabulary and word fluency), ToM and social relationships. They also answered a social motivation question. Results: Results showed that individual differences in older people's ToM were overall significantly associated with those in relationships with friends, but not relatives. In addition, the Hayes moderating procedure showed that individual differences in ToM were related to those in friendships only for those people who had a high or medium level of social motivation. Conclusion: These findings underline the importance of motivation in guiding the use of ToM in everyday social interactions.

Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.