Updating the Chieti Affective Action Videos database with older adults

Age-related differences on temporal source memory by using dynamic stimuli: the effects of POV and emotional valence

Previous studies have highlighted that temporal source memory can be influenced by factors such as the individual's age and the emotional valence of the event to be remembered. In this study, we investigated how the different points of view (POVs) from which an event is presented could interact with the relationship between age-related differences and emotional valence on temporal source memory. One hundred and forty-one younger adults (aged 18-30) and 90 older adults (aged 65-74) were presented with a series of emotional videos shot from different POVs (first vs. third-person) in three sessions. In the fourth session, participants were asked to indicate in which session (1, 2, or 3) they viewed each video. The results indicated that the first-person POV amplified the effects of the emotional valence on temporal source memory. Only in this experimental condition, older adults "pushed away" negative stimuli by perceiving them as more distant in time, and "kept closer" positive stimuli by perceiving them as more recent. In comparison, younger adults "kept closer" positive stimuli. These findings add to the existing literature on the positivity effect on temporal source memory and highlighted the importance of considering the POV in relation to the emotional valence.

The effects of prefrontal tDCS and hf-tRNS on the processing of positive and negative emotions evoked by video clips in first- and third-person

The causal role of the cerebral hemispheres in positive and negative emotion processing remains uncertain. The Right Hemisphere Hypothesis proposes right hemispheric superiority for all emotions, while the Valence Hypothesis suggests the left/right hemisphere's primary involvement in positive/negative emotions, respectively. To address this, emotional video clips were presented during dorsolateral prefrontal cortex (DLPFC) electrical stimulation, incorporating a comparison of tDCS and high frequency tRNS stimulation techniques and manipulating perspective-taking (first-person vs third-person Point of View, POV). Four stimulation conditions were applied while participants were asked to rate emotional video valence: anodal/cathodal tDCS to the left/right DLPFC, reverse configuration (anodal/cathodal on the right/left DLPFC), bilateral hf-tRNS, and sham (control condition). Results revealed significant interactions between stimulation setup, emotional valence, and POV, implicating the DLPFC in emotions and perspective-taking. The right hemisphere played a crucial role in both positive and negative valence, supporting the Right Hemisphere Hypothesis. However, the complex interactions between the brain hemispheres and valence also supported the Valence Hypothesis. Both stimulation techniques (tDCS and tRNS) significantly modulated results. These findings support both hypotheses regarding hemispheric involvement in emotions, underscore the utility of video stimuli, and emphasize the importance of perspective-taking in this field, which is often overlooked.

The effect of aging and emotions on time processing

BACKGROUND

Time perception is an automatic process that can be influenced by intrinsic and extrinsic factors.

AIMS

This study aimed to investigate the effect of age and emotions on the ability to keep track of short suprasecond intervals.

METHODS

Younger adults (N = 108, age range: 18-35) and older adults (N = 51, age range: 65-87) were asked to reproduce, bisect, or double the duration of facial stimuli randomly presented for 1500, 3000, and 4500 ms. The experiment included facial stimuli with positive, negative, or neutral expressions.

RESULTS

The participants across age correctly reproduced intervals but overestimated and underestimated them when asked to bisect and double the intervals, respectively. Overall, when faces were presented with a positive or negative expression, an overestimation of time intervals emerged compared to faces with neutral expressions. Emotions had a greater effect on older adults, who showed a greater overestimation of positive facial expressions and an underestimation of sad, but not angry, facial expressions.

DISCUSSION

The results provide evidence that time perception is influenced by age and emotions, with older adults showing a greater effect of emotions on time processing.

CONCLUSION

The study suggests an interaction among time processing, age, and emotions, highlighting an automatic relationship among these domains, often considered independent.

Affective health and countermeasures in long-duration space exploration

Background

Space research is shifting attention toward interplanetary expeditions. Therefore, whether long-duration spaceflight may influence affective health is becoming an urgent issue.

Method

To this end, we undertook a literature search and reviewed several behavioral simulation studies on Earth that focused on affective components in space. We concluded with studies showing how spaceflight can impact on affective health of astronauts with a positively laden trajectory.

Results

By analyzing the multifaceted theoretical concept of affective health, we show that there is a variety of affective states (e.g., stress, coping, adaptation, and resilience) that can be differently affected by spaceflight.

Conclusions

Countermeasures geared toward promoting positive emotions could play a key role in positive adaptation to extreme environments and thus during long-duration space missions may benefit. Subjective resilience plays a mediating role in adaptation, but its definition needs to be deepened in order to develop robust countermeasures that may prevent the onset of emotional disorders.

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

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.