Detecting recollection: Human evaluators can successfully assess the veracity of others' memories.

Humans have the highly adaptive ability to learn from others' memories. However, because memories are prone to errors, in order for others' memories to be a valuable source of information, we need to assess their veracity. Previous studies have shown that linguistic information conveyed in self-reported justifications can be used to train a machine-learner to distinguish true from false memories. But can humans also perform this task, and if so, do they do so in the same way the machine-learner does? Participants were presented with justifications corresponding to Hits and False Alarms and were asked to directly assess whether the witness's recognition was correct or incorrect. In addition, participants assessed justifications' recollective qualities: their vividness, specificity, and the degree of confidence they conveyed. Results show that human evaluators can discriminate Hits from False Alarms above chance levels, based on the justifications provided per item. Their performance was on par with the machine learner. Furthermore, through assessment of the perceived recollective qualities of justifications, participants were able to glean more information from the justifications than they used in their own direct decisions and than the machine learner did.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

Is it better to be happy or right? Examining the relative role of the pragmatic and epistemic imperatives in momentary affective evaluations.

According to research highlighting the importance of predictions, the confirmation of expectations may be a positively-laden experience. A strong test of this principle is the case of the "doomsayer's delight": the possibility that belief confirmation can be rewarding even when negative expectations are realized. In order to investigate this idea, we conducted two high-powered experiments examining people's immediate affective reactions following exposure to expected or unexpected positive and negative stimuli. The results show that people feel significantly worse when their pessimistic expectations are confirmed than when their optimistic expectations are violated. This finding was not moderated by several theoretically relevant individual difference measures or temporal dynamics. Findings from this study contribute to our understanding of the interplay between epistemic and pragmatic motivations in guiding emotional responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The poetry of psychological distance: Bidirectional associations between stimulus speed and its psychological distance and construal level.

Based on the cognitive-ecological approach and on logical-functional principles, in 12 studies (11 preregistered), we examine the novel hypotheses that psychological distance and construal level (CL) are associated in people's minds with stimulus speed: the psychologically distant/abstract is slow, and the psychologically close/concrete is fast. The findings support our expectations. Study Set I examined the association between psychological distance and speed. Findings show that psychological distance is implicitly and explicitly associated with speed (Study 1), that psychological distance is seen as compatible with slow and proximity with fast (Study 2), that stimulus psychological distance affects its perceived speed (Study 3), and that stimulus speed affects its psychological distance (Study 4). Study Set II examined the association between construal level and speed. Findings show that construal level is explicitly associated with speed (Study 5), that abstract is seen as compatible with slow and concrete with fast (Study 6), that natural language word distribution structures reflect an association between abstractness and speed (Study 7), that construal level affects speed (Study 8), and that speed affects stimulus construal level (Study 9). Study Set III examined implications for communication and person perception. Findings suggest that slow-paced (vs. fast-paced) speech is associated with larger perceived spatial and social distance between speaker and audience and larger audiences (Studies 10a, 10b) and that people infer an expansive (contractive) regulatory scope from slow-paced (fast-paced) spoken messages (Study 11). We elaborate on possible mechanisms and their theoretical and practical implications in domains including decision making and urban design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).