行为研究方法发表与实验心理学研究的方法、技术和仪器有关的文章。该杂志特别关注计算机技术在心理学研究中的应用。年度特刊专门针对这一领域。

The journal "Behavior Research Methods" focuses on publishing articles related to methods, techniques, and instruments in experimental psychology research. It particularly emphasizes the application of computer technology in psychological research and publishes annual special issues dedicated to this field.

《Behavior Research Methods》期刊专注于发表与实验心理学研究相关的方法、技术和仪器的文章。该杂志特别关注计算机技术在心理学研究中的应用，并每年出版专门针对这一领域的特刊。

Behavior Research Methods

A Web-based interface has been developed to facilitate researchers in collecting language history information online. Most researchers use their own versions of language history questionnaires for specific studies in second language acquisition. Although these versions of questionnaires all differ from one another in some respects, there is a significant amount of overlap between them. Here we identify the crucial dimensions that most investigators consider important to include in such a questionnaire. We have examined the most commonly asked questions in 41 published questionnaires, and on the basis of our analyses we propose a general L2 language history questionnaire. Subjects can enter some or all of the information on the Web, and the results are automatically generated as an RTF output file on the user's desktop.

Ping Li

Sara Sepanski

Xiaowei Zhao

Behavior research methods

Behav Res Methods

Language history questionnaire: A web-based interface for bilingual research.

语言历史问卷：基于网络的双语研究界面。

The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.

Sacha Epskamp

Denny Borsboom

Eiko I. Fried

Behav Res

Estimating psychological networks and their accuracy: A tutorial paper

心理网络的估计及其准确性：一篇教程论文

In the present study, we collected valence, arousal, concreteness, familiarity, imageability, and context availability ratings for a total of 1,100 Chinese words. The ratings for all variables were collected with 9-point Likert scales. We tested the reliability of the present database by comparing it to the extant Chinese Affective Word System, and performed split-half correlations for all six variables. We then evaluated the relationships between all variables. Regarding the affective variables, we found a typical quadratic relation between valence and arousal, in line with previous findings. Likewise, significant correlations were found between the semantic variables. Importantly, we explored the relationships between ratings for the affective variables (i.e., valence and arousal) and concreteness ratings, suggesting that valence and arousal ratings can predict concreteness ratings. This database of affective norms will be a valuable source of information for emotion research that makes use of Chinese words, and will enable researchers to use highly controlled Chinese verbal stimuli to more reliably investigate the relation between cognition and emotion.

Zhao Yao

Jia Wu

Yanyan Zhang

Zhenhong Wang

Norms of valence, arousal, concreteness, familiarity, imageability, and context availability for 1,100 Chinese words.

1,100个中文词汇的效价、唤醒度、具体性、熟悉度、形象性和语境可用性规范。

Abstract Investigation of affective and semantic dimensions of words is essential for studying word processing. In this study, we expanded Tse et al.’s (Behav Res Methods 49:1503–1519, 2017; Behav Res Methods 55:4382–4402, 2023) Chinese Lexicon Project by norming five word dimensions (valence, arousal, familiarity, concreteness, and imageability) for over 25,000 two-character Chinese words presented in traditional script. Through regression models that controlled for other variables, we examined the relationships among these dimensions. We included ambiguity, quantified by the standard deviation of the ratings of a given lexical variable across different raters, as separate variables (e.g., valence ambiguity) to explore their connections with other variables. The intensity–ambiguity relationships (i.e., between normed variables and their ambiguities, like valence with valence ambiguity) were also examined. In these analyses with a large pool of words and controlling for other lexical variables, we replicated the asymmetric U-shaped valence–arousal relationship, which was moderated by valence and arousal ambiguities. We also observed a curvilinear relationship between valence and familiarity and between valence and concreteness. Replicating Brainerd et al.’s (J Exp Psychol Gen 150:1476–1499, 2021; J Mem Lang 121:104286, 2021) quadratic intensity–ambiguity relationships, we found that the ambiguity of valence, arousal, concreteness, and imageability decreases as the value of these variables is extremely low or extremely high, although this was not generalized to familiarity. While concreteness and imageability were strongly correlated, they displayed different relationships with arousal, valence, familiarity, and valence ambiguity, suggesting their distinct conceptual nature. These findings further our understanding of the affective and semantic dimensions of two-character Chinese words. The normed values of all these variables can be accessed via https://osf.io/hwkv7 .

Yuen-Lai Chan

Chi-Shing Tse

Decoding the essence of two-character Chinese words: Unveiling valence, arousal, concreteness, familiarity, and imageability through word norming

解码双字中文词的本质：通过词规范揭示情感、唤醒、具体性、熟悉度和图像性

The present work collected familiarity norms for 20,275 two-character, 1231 three-character, and 2819 four-character simplified Chinese words from 1300 native speakers of Mandarin Chinese. The familiarity of each word was rated on a 7-point scale by at least 21 participants. The results supported the reliability and validity of the present familiarity ratings, which is the first large familiarity database for Chinese in the field. These familiarity norms can be downloaded from the supplemental materials. Furthermore, the contribution of familiarity to Chinese lexical processing was investigated using the present familiarity ratings and previous data (lexical features and visual lexical decision), mainly from two major Chinese lexicon projects, MELD-SCH and CLP. Regression analysis suggests that familiarity explained a substantial percentage of the variance in lexical processing of both simplified and traditional Chinese words, over and above the effects of word frequency and other lexical features, including age of acquisition (AoA). Further analysis identified a significantly greater familiarity effect for lower-frequency words than that for higher-frequency words. Together, among the first, our findings support the important contribution of familiarity with Chinese words to lexical processing, especially for low-frequency words.

Yongqiang Su

Yixun Li

Hong Li

Familiarity ratings for 24,325 simplified Chinese words.

24,325个简体中文字的熟悉度评分。

Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli. We demonstrated the effectiveness of our approach by extracting and correcting behavior from videos using two computer vision software (OpenPose and OpenFace) and by training two algorithms (XGBoost and long short-term memory [LSTM]) to differentiate between experimental conditions. High classification rates were achieved for "Pers" conditions versus "Snd" or "Rest" (AUC = 0.8-0.9), with explainability revealing actions units and gaze as key features. Additionally, moderate classification rates were attained for "Snd" versus "Rest" (AUC = 0.7), attributed to action units, limbs and head points, as well as for "Self" versus "Ctrl" (AUC = 0.7-0.8), due to facial points. These findings were consistent with a more conventional hypothesis-driven approach. Overall, our study suggests that our tool is well suited for holistic and fine-grained behavioral analysis and offers modularity for extension into more complex naturalistic environments.

Bruno Michelot

Alexandra Corneyllie

Marc Thevenet

Stefan Duffner

Fabien Perrin

A modular machine learning tool for holistic and fine-grained behavioral analysis

模块化机器学习工具用于全面和细粒度的行为分析

Abstract Experimental psychologists and psycholinguists increasingly turn to online research for data collection due to the ease of sampling many diverse participants in parallel. Online research has shown promising validity and consistency, but is it suitable for all paradigms? Specifically, is it reliable enough for individual differences research? The current paper reports performance on 15 tasks from a psycholinguistic individual differences battery, including timed and untimed assessments of linguistic abilities, as well as domain-general skills. From a demographically homogenous sample of young Dutch people, 149 participants participated in the lab study, and 515 participated online. Our results indicate that there is no reason to assume that participants tested online will underperform compared to lab-based testing, though they highlight the importance of motivation and the potential for external help (e.g., through looking up answers) online. Overall, we conclude that there is reason for optimism in the future of online research into individual differences.

Kyla McConnell

Florian Hintz

Antje S. Meyer

Individual differences in online research: Comparing lab-based and online administration of a psycholinguistic battery of linguistic and domain-general skills

在线研究中的个体差异：基于实验室和在线管理的心理语言学及领域通用技能测试的比较

Abstract The growing interest in harnessing natural environments to enhance mental health, including cognitive functioning and mood, has yielded encouraging results in initial studies. Given that images of nature have demonstrated similar benefits, they are frequently employed as proxies for real-world environments. To ensure precision and control, researchers often manipulate images of natural environments. The effectiveness of this approach relies on standardization of imagery, and therefore, inconsistency in methods and stimuli has limited the synthesis of research findings in the area. Responding to these limitations, the current paper introduces the Salford Nature Environments Database (SNED), a standardized database of natural images created to support ongoing research into the benefits of nature exposure. The SNED currently exists as the most comprehensive nature image database available, comprising 500 high-quality, standardized photographs capturing a variety of possible natural environments across the seasons. It also includes normative scores for user-rated (801 participants) characteristics of fascination, refuge and prospect, compatibility, preference, valence, arousal, and approach–avoidance, as well as data on physical properties of the images, specifically luminance, contrast, entropy, CIELAB colour space parameter values, and fractal dimensions. All image ratings and content detail, along with participant details, are freely available online. Researchers are encouraged to use this open-access database in accordance with the specific aims and design of their study. The SNED represents a valuable resource for continued research in areas such as nature-based therapy, social prescribing, and experimental approaches investigating underlying mechanisms that help explain how natural environments improve mental health and wellbeing.

Robert C. A. Bendall

Sam Royle

James Dodds

Hugh Watmough

Jamie C. Gillman

David Beevers

Simon Cassidy

Ben Short

Paige Metcalfe

Michael J. Lomas

Draco Graham-Kevan

Samantha E. A. Gregory

The Salford Nature Environments Database (SNED): an open-access database of standardized high-quality pictures from natural environments

索尔福德自然环境数据库（SNED）：一个开放获取的自然环境标准化高质量图片数据库

Abstract Irrespective of the precision, the inaccuracy of a pupil-based eye tracker is about 0.5 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> </mml:math> . This paper delves into two factors that potentially increase the inaccuracy of the gaze signal, namely, 1) Pupil-size changes and the pupil-size artefact (PSA) and 2) the putative inability of experienced individuals to precisely refixate a visual target. Experiment 1 utilizes a traditional pupil-CR eye tracker, while Experiment 2 employs a retinal eye tracker, the FreezeEye tracker, eliminating the pupil-based estimation. Results reveal that the PSA significantly affects gaze accuracy, introducing up to 0.5 $$^\circ $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> </mml:math> inaccuracies during calibration and validation. Corrections based on the relation between pupil size and apparent gaze shift substantially reduce inaccuracies, underscoring the PSA’s influence on eye-tracking quality. Conversely, Experiment 2 demonstrates humans’ precise refixation abilities, suggesting that the accuracy of the gaze signal is not limited by human refixation inconsistencies.

Ignace T. C. Hooge

Roy S. Hessels

Diederick C. Niehorster

Richard Andersson

Marta K. Skrok

Robert Konklewski

Patrycjusz Stremplewski

Maciej Nowakowski

Szymon Tamborski

Anna Szkulmowska

Maciej Szkulmowski

Marcus Nyström

Eye tracker calibration: How well can humans refixate a target?

眼动仪校准：人类重新注视目标的能力如何？

Abstract The study of large language models (LLMs) and LLM-powered chatbots has gained significant attention in recent years, with researchers treating LLMs as participants in psychological experiments. To facilitate this research, we developed an R package called “MacBehaviour “ ( https://github.com/xufengduan/MacBehaviour ), which interacts with over 100 LLMs, including OpenAI's GPT family, the Claude family, Gemini, Llama family, and other open-weight models. The package streamlines the processes of LLM behavioural experimentation by providing a comprehensive set of functions for experiment design, stimuli presentation, model behaviour manipulation, and logging responses and token probabilities. With a few lines of code, researchers can seamlessly set up and conduct psychological experiments, making LLM behaviour studies highly accessible. To validate the utility and effectiveness of “MacBehaviour,“ we conducted three experiments on GPT-3.5 Turbo, Llama-2-7b-chat-hf, and Vicuna-1.5-13b, replicating the sound-gender association in LLMs. The results consistently demonstrated that these LLMs exhibit human-like tendencies to infer gender from novel personal names based on their phonology, as previously shown by Cai et al. (2024). In conclusion, “MacBehaviour” is a user-friendly R package that simplifies and standardises the experimental process for machine behaviour studies, offering a valuable tool for researchers in this field.

Xufeng Duan

Shixuan Li

Zhenguang G. Cai

MacBehaviour: An R package for behavioural experimentation on large language models

MacBehaviour：一个用于大型语言模型行为实验的R包

Abbreviation	Behav. Res. Methods
Journal Impact	4.52
Quartiles(Global)	PSYCHOLOGY, MATHEMATICAL(Q1)

ISSN	1554-351X, 1554-3528
h-index	171
Top Journals	Yes

Publication Information	Publisher: Springer Nature，Publishing cycle: 6 issues per year，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2004，Proportion of original research papers: 98.21%，Self Citation Rate:8.70%， Gold OA Rate: 52.53%

Behavior Research Methods

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