Journal of Nonverbal Behavior最新文献

The present study examines how supportive touch impacts evaluations of esteem support content containing high emotion-focused (HEF) or high problem-focused (HPF) messages during observed esteem support interactions. A 2 (verbal content; i.e., HEF or HPF) by 2 (nonverbal content; i.e., presence or absence of supportive tactile communication) experiment was conducted to test for main and interactional effects. Results revealed that HEF conditions were perceived to be more effective by observers at enhancing the recipient’s state self-esteem, state self-efficacy, and alleviating distress compared to HPF conditions. The supportive tactile communication conditions were perceived as better at enhancing state self-esteem and alleviating distress compared to the no supportive tactile communication conditions by observers. However, these main effects were qualified by significant two-way interactions between message content and nonverbal behavior on ratings of state self-esteem and distress alleviation, such that the addition of supportive tactile communication enhanced the effectiveness of HPF message content but not HEF content.

The growth of machine learning and artificial intelligence has made it possible for automatic lie detection systems to emerge. These can be based on a variety of cues, such as facial features. However, there is a lack of knowledge about both the development and the accuracy of such systems. To address this lack, we conducted a review of studies that have investigated automatic lie detection systems by using facial features. Our analysis of twenty-eight eligible studies focused on four main categories: dataset features, facial features used, classifier features and publication features. Overall, the findings showed that automatic lie detection systems rely on diverse technologies, facial features, and measurements. They are mainly based on factual lies, regardless of the stakes involved. On average, these automatic systems were based on a dataset of 52 individuals and achieved an average accuracy ranging from 61.87% to 72.93% in distinguishing between truth-tellers and liars, depending on the types of classifiers used. However, although the leakage hypothesis was the most used explanatory framework, many studies did not provide sufficient theoretical justification for the choice of facial features and their measurements. Bridging the gap between psychology and the computational-engineering field should help to combine theoretical frameworks with technical advancements in this area.

The aim of the current study was to examine facial and body posture emotion recognition among deaf and hard-of-hearing (DHH) and hearing young adults. Participants were (N = 126) DHH (n = 48) and hearing (n = 78) college students who completed two emotion recognition tasks in which they were shown photographs of faces and body postures displaying different emotions of both high and low intensities and had to infer the emotion being displayed. Compared to hearing participants, DHH participants performed worse on the body postures emotion task for both high and low intensities. They also performed more poorly on the facial emotion task, but only for low-intensity emotional facial expressions. On both tasks, DHH participants whose primary mode of communication was Signed English performed significantly more poorly than those whose primary mode was American Sign Language (ASL) or spoken English. Moreover, DHH participants who communicated using ASL performed similarly to hearing participants. This suggests that difficulties in affect recognition among DHH individuals occur when processing both facial and body postures that are more subtle and reflective of real-life displays of emotion. Importantly, this also suggests that ASL as a primary form of communication in this population may serve as a protective factor against emotion recognition difficulties, which could, in part, be due to the complex nature of this language and its requirement to perceive meaning through facial and postural expressions with a wide visual lens.

Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks important sources of variability resulting in potentially misleading estimates and may conflate response bias with participants’ underlying sensitivity to detect lies from truths. We showcase an alternative approach using a signal detection theory (SDT) with generalized linear mixed models framework to address these limitations. This SDT approach incorporates individual differences from both judges and senders, which are a principal source of spurious findings in deception research. By avoiding data transformations and aggregations, this methodology outperforms traditional methods and provides more informative and reliable effect estimates. This well-established framework offers researchers a powerful tool for analyzing deception data and advances our understanding of veracity judgments. All code and data are openly available.

Ekman and Friesen’s (1969) seminal theoretical paper on the leakage hierarchy sparked decades of research on the relationship between nonverbal cues and deception. Yet skepticism over the strength and reliability of behavioral cues to deception has been building over the years (DePaulo et al., 2003; Patterson et al., 2023; Vrij et al., 2019). However, the last two decades have seen dramatic growth in research paradigms, interviewing techniques, integration of technology, automated coding methods, and facial research, suggesting a need for reexamination of the current state of the field. This special issue includes theoretical and empirical papers that advance our understanding of the link between nonverbal cues and deception. This collection of papers suggests there is cause for some optimism in the field of nonverbal deception detection and signals some fruitful avenues for future research. Specifically, deception research in ecologically valid, high-stakes lie-detection situations using a multi-modal approach has good promise for differentiating truth-tellers from liars.

Interpersonal touch buffers against stress under challenging conditions, but this effect depends on familiarity. People benefit from receiving touch from their romantic partners, but the results are less consistent in the context of receiving touch from an opposite-gender stranger. We propose that there may be important gender differences in how people respond to touch from opposite-gender strangers. Specifically, we propose that touch from an opposite-gender stranger may only have stress-buffering effects for men, not women. Stress was induced as participants took part in an emotion recognition task in which they received false failure feedback while being touched by a romantic partner or stranger. We measured subjective and physiological markers of stress (i.e., reduced heart rate variability) throughout the experiment. Neither stranger’s nor partner’s touch had any effect on subjective or physiological markers of stress for men. Women, however, subjectively experienced a stress-buffering effect of partner and stranger touch, but showed increased physiological markers of stress when receiving touch from an opposite-gender stranger. These results highlight the importance of considering gender when investigating touch as a stress buffer.