JOURNAL OF NONVERBAL BEHAVIOR 对非语言行为的所有主要领域进行了同行评审的原创理论和实证研究。具体主题包括副语言、近义词、面部表情、眼神交流、面对面互动和非语言情感表达，以及有助于科学理解非语言过程和行为的其他主题。

The Journal of Nonverbal Behavior is a peer-reviewed journal that focuses on all major areas of nonverbal behavior, publishing original theoretical and empirical research. Specific topics include paralinguistics, synonyms, facial expressions, eye contact, face-to-face interactions, and nonverbal emotional expression, as well as other themes that contribute to the scientific understanding of nonverbal processes and behaviors.

《Journal of Nonverbal Behavior》是一本经过同行评审的期刊，专注于非语言行为的各个主要领域，发表原创的理论和实证研究。具体主题包括副语言、同义词、面部表情、眼神交流、面对面互动以及非语言情感表达等。此外，期刊还涵盖其他有助于科学理解非语言过程和行为的相关主题。

Journal of Nonverbal Behavior

Susanne A. Denham

Leslie Grout

J Nonverbal Behav

Socialization of emotion: Pathway to preschoolers' emotional and social competence

情绪社会化：通往学龄前儿童情绪与社交能力的路径

Michael Harris Bond

Emotions and their expression in Chinese culture

中国文化中的情感及其表达

Antal Haans

Renske de Bruijn

Wijnand A. IJsselsteijn

A Virtual Midas Touch? Touch, Compliance, and Confederate Bias in Mediated Communication

虚拟点金术？媒介传播中的触觉、顺从性与同谋偏见

Nathan T. Kline

Fran C. Blumberg

Young Adults with Autism May Possess Typical-Level Skills in Identifying Emotions from Person-in-Context Images

自闭症青年在识别情境中人物情感方面可能具备典型水平的能力

John R. Aiello

Environmental Psychology and Nonverbal Behavior

A further look at equilibrium theory: Visual interaction as a function of interpersonal distance

再探平衡理论：人际距离对视觉互动的影响

McLean G. Morgan

Nicholas O. Rule

Accuracy, Bias, and Overgeneralization: Perceived Aggression Guides Threat Detection and Punishment of Female Criminal Offenders

准确性、偏见与过度概括：感知到的攻击性指导对女性犯罪者的威胁检测与惩罚

Abstract Nonverbal vocalisations like laughter, sighs, and groans are a fundamental part of everyday communication. Yet surprisingly little is known about the social norms concerning which vocalisations are considered appropriate to express in which context (i.e., display rules). Here, in two pre-registered studies, we investigate how people evaluate the appropriateness of different nonverbal vocalisations across locations and relationships with listeners. Study 1, with a U.S. sample ( n = 250), showed that certain vocalisations (e.g., laughter, sighs, cries) are consistently viewed as more socially acceptable than others (e.g., roars, groans, moans). Additionally, location (private vs. public) and interpersonal closeness (close vs. not close) significantly influenced these perceptions, with private locations and close relationships fostering greater expressive freedom. Study 2 extended this investigation across four societies with divergent cultural norms ( n = 1120 in total): the U.S. (for direct replication), Türkiye, China, and the Netherlands. Findings largely replicated those from Study 1 and supported the existence of cross-culturally consistent patterns in display rules for nonverbal vocalisations, though with some variation across cultures. This research expands our understanding of how social norms affect auditory communication, extending beyond the visual modality of facial expressions to encompass the rich world of nonverbal vocalisations.

Roza G. Kamiloğlu

Kunalan Manokara

Joshua M. Tybur

Disa A. Sauter

When to Laugh, When to Cry: Display Rules of Nonverbal Vocalisations Across Four Cultures

何时笑，何时哭：四种文化中的非言语发声表现规则

Abstract An important task in criminal justice is to evaluate the accuracy of eyewitness testimony. In this study, we examined if machine learning could be used to detect accuracy. Specifically, we examined if support vector machines (SVMs) could accurately classify testimony statements as correct or incorrect based purely on the nonverbal aspects of the voice. We analyzed 3,337 statements (76.61% accurate) from 51 eyewitness testimonies along 94 acoustic variables. We also examined the relative importance of each of the acoustic variables, using Lasso regression. Results showed that the machine learning algorithms were able to predict accuracy between 20 and 40% above chance level (AUC = 0.50). The most important predictors included acoustic variables related to the amplitude (loudness) of speech and the duration of pauses, with higher amplitude predicting correct recall and longer pauses predicting incorrect recall. Taken together, we find that machine learning methods are capable of predicting whether eyewitness testimonies are correct or incorrect with above-chance accuracy and comparable to human performance, but without detrimental human biases. This offers a proof-of-concept for machine learning in evaluations of eyewitness accuracy, and opens up new avenues of research that we hope might improve social justice.

Philip U. Gustafsson

Tim Lachmann

Petri Laukka

Machine Learning Predicts Accuracy in Eyewitnesses’ Voices

机器学习预测目击者声音的准确性

Valérie Coulombe

Vincent Martel-Sauvageau

Laura Monetta

The Expression of Vocal Emotions in Cognitively Healthy Adult Speakers: Impact of Emotion Category, Gender, and Age

认知健康成年说话者语音情感的表达：情感类别、性别和年龄的影响

Abbreviation	J. Nonverbal Behav.
Journal Impact	1.26
Quartiles(Global)	PSYCHOLOGY, SOCIAL(Q4)

ISSN	0191-5886, 0361-3496, 1573-3653
h-index	75

Publication Information	Publisher: Kluwer Academic/Human Sciences Press Inc.，Publishing cycle: 4 issues per year，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1979，Proportion of original research papers: 96.15%，Self Citation Rate:0.00%， Gold OA Rate: 37.04%

Journal of Nonverbal Behavior

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Socialization of emotion: Pathway to preschoolers' emotional and social competence

Emotions and their expression in Chinese culture

A Virtual Midas Touch? Touch, Compliance, and Confederate Bias in Mediated Communication

Young Adults with Autism May Possess Typical-Level Skills in Identifying Emotions from Person-in-Context Images

A further look at equilibrium theory: Visual interaction as a function of interpersonal distance

Young Adults with Autism May Possess Typical-Level Skills in Identifying Emotions from Person-in-Context Images

Accuracy, Bias, and Overgeneralization: Perceived Aggression Guides Threat Detection and Punishment of Female Criminal Offenders

When to Laugh, When to Cry: Display Rules of Nonverbal Vocalisations Across Four Cultures

Machine Learning Predicts Accuracy in Eyewitnesses’ Voices

The Expression of Vocal Emotions in Cognitively Healthy Adult Speakers: Impact of Emotion Category, Gender, and Age