Behavior Research Methods最新文献

{"title":"Reliability and reactivity of heart rate variability and pupillometry in response to controlled autonomic perturbations in university students.","authors":"Michael Matyevich, Michael I C Kingsley, Rodney Bice, Michael Mortimer, Ben Horan, Stefan Piantella, Bradley J Wright","doi":"10.3758/s13428-025-02793-1","DOIUrl":"10.3758/s13428-025-02793-1","url":null,"abstract":"<p><p>The aim of this test-retest reliability study was to evaluate the reliability and reactivity of heart rate variability (HRV) and pupillometry metrics under conditions with controlled cognitive stimulation and paced breathing within a virtual reality protocol. After habituation, 30 English-speaking university students completed a four-phase protocol on two occasions separated by 1 week. HRV and pupillometry were continuously measured during the following phases: baseline, cognitive testing, guided breathing with nature immersion, and spontaneous breathing with nature immersion. Strong day-to-day relative reliability was confirmed for both HRV (pooled ICC: 0.75 to 0.83) and pupillometry (pooled ICC: 0.66 to 0.87). HRV metrics of sympathovagal balance in the time, frequency, and non-linear domains showed reactivity with significant differences between all phases. Pupillometry metrics increased progressively from cognitive testing to guided breathing nature immersion to nature immersion, suggesting psychological rather than respiratory influences. Relatively large minimal detectable change values were determined across HRV (22 to 54% deviation from baseline) and pupillometry (33 to 88% deviation from baseline) metrics. Although the relatively large ratio limits of agreement and minimal detectable change values suggest that detecting systematic changes in these metrics over time might be difficult at the individual level, strong relative reliability supports the use of HRV and pupillometry metrics to detect differences in sympathovagal balance between groups. Additionally, the responsiveness of these metrics demonstrates the efficacy of the proposed virtual reality protocol in inducing detectable physiological reactivity across HRV and pupillometry metrics.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"267"},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated machine learning for classification and regression: A tutorial for psychologists.","authors":"Chaewon Lee, Kathleen M Gates","doi":"10.3758/s13428-025-02684-5","DOIUrl":"10.3758/s13428-025-02684-5","url":null,"abstract":"<p><p>Machine learning (ML) has extended the scope of psychological research by enabling data-driven discovery of patterns in complex datasets, complementing traditional hypothesis-driven approaches and enriching individual-level prediction. As a principal subfield, supervised ML has advanced mental health diagnostics and behavior prediction through classification and regression tasks. However, the complexity of ML methodologies and the absence of established norms and standardized pipelines often limit its adoption among psychologists. Furthermore, the black-box nature of advanced ML algorithms obscures how decisions are made, making it difficult to identify the most influential variables. Automated ML (AutoML) addresses these challenges by automating key steps such as model selection and hyperparameter optimization, while enhancing interpretability through explainable artificial intelligence (XAI). By streamlining workflows and improving efficiency, AutoML empowers users of all technical levels to implement advanced ML methods effectively. Despite its transformative potential, AutoML remains underutilized in psychological research, with no dedicated educational material available. This tutorial aims to bridge the gap by introducing AutoML to psychologists. We cover advanced AutoML methods, including combined algorithm selection and hyperparameter optimization (CASH), stacked ensemble generalization, and XAI. The utility of AutoML is demonstrated using the \"H2O AutoML\" R package with publicly available psychological datasets, performing regression on multi-individual cross-sectional data and classification on single-individual time-series data. We also provide practical workarounds for ML methods not currently supported in the package, so researchers can adopt alternative solutions when needed. These examples illustrate how AutoML democratizes ML, making it more accessible while providing advanced methodologies for psychological research.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"262"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The forced-response method: Urgency rediscovered.","authors":"Christian H Poth, Anika Krause","doi":"10.3758/s13428-025-02788-y","DOIUrl":"10.3758/s13428-025-02788-y","url":null,"abstract":"<p><p>Lee et al. (2025) presented their \"forced-response method\", in which performance in a cognitive control task is assessed as a function of the time a target stimulus is visible (within a reaction time), in a situation with time pressure for responding. In the existing literature, this experimental approach has been referred to as the compelled-response paradigm or the urgent paradigm. Replicating earlier findings by also applying the method to the flanker task of cognitive control, Lee et al. found that urgency opens up a time window in which stimulus-driven processing overtakes cognitively controlled processing and leads to actions against current intentions (as per task instructions). Taken together, this demonstrates how the method captures the temporal evolution of performance in high resolution, providing a window into the dynamics of cognitive processing in cognitive control tasks. However, urgency may induce a specific mental state, akin to phasic alertness as a state of response and perceptual readiness. Therefore, it is an open question how the method differs from standard assessments of cognitive control and in what way it provides a better alternative.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"263"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the reliability of an online measure of the temporal binding window of audiovisual integration.","authors":"Leon Flanagan, Nina M Zumbrunn, Rebecca J Hirst, David P McGovern","doi":"10.3758/s13428-025-02791-3","DOIUrl":"10.3758/s13428-025-02791-3","url":null,"abstract":"<p><p>A growing body of evidence suggests that imprecise multisensory temporal processing is associated with a range of adverse clinical outcomes. Specifically, this research has shown that the temporal binding window of audiovisual integration widens with advancing age, whereas individuals with various neurodevelopmental disorders exhibit distinctive profiles of multisensory processing when compared to their neurotypical counterparts. While this line of research has made significant inroads in emphasising the importance of multisensory processing in both typical and atypical cognition, the field would greatly benefit from the validation of web-based tools for assessing multisensory temporal processing, enabling larger sample sizes and the recruitment of typically hard-to-reach populations. Here, we assessed the reliability of a web-based version of the sound-induced flash illusion (SIFI) and examined the level of agreement between the measurements of the temporal binding window that it produces relative to those derived from the SIFI task performed under standard laboratory conditions. Our results demonstrate that both versions of the task elicit a robust SIFI effect and show a similar dependency on the stimulus onset asynchronies between the audiovisual stimuli. Furthermore, our analysis revealed strong correlations between the estimated measurements of both the width and peak amplitude of the temporal binding window derived from the online and lab-based task versions, confirming the suitability of the web-based task for assessing multisensory function. Future studies should extend the evaluation of this online implementation of the SIFI to clinical populations who exhibit impaired multisensory temporal processing, to assess its suitability for clinical research.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"261"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic evaluation of Dutch large language models' surprisal estimates in sentence, paragraph and book reading.","authors":"Sam Boeve, Louisa Bogaerts","doi":"10.3758/s13428-025-02774-4","DOIUrl":"10.3758/s13428-025-02774-4","url":null,"abstract":"<p><p>Studies using computational estimates of word predictability from neural language models have garnered strong evidence in favour of surprisal theory. Upon encountering a word, readers experience a processing difficulty that is a linear function of that word's surprisal. Evidence for this effect has been established in the English language or using multilingual models to estimate surprisal across languages. At the same time, many language-specific models of unknown psychometric quality are made openly available. Here, we provide a systematic evaluation of the surprisal estimates of a collection of large language models, specifically designed for Dutch, examining how well they account for reading times in corpora of sentence, paragraph and book reading. We compare their performance to multilingual models and an N-gram model. While models' predictive power for reading times varied considerably across corpora, GPT-2-based models demonstrated superior overall performance. We show that Dutch large language models exhibit the same inverse scaling trend observed for English, with the surprisal estimates of smaller models showing a better fit to reading times than those of the largest models. We also replicate the linear effect of surprisal on reading times for Dutch. Both effects, however, depended on the corpus used for evaluation. Overall, these results offer a psychometric leaderboard of Dutch large language models and challenge the notion of a one-size-fits-all language model for psycholinguistic research. The surprisal estimates derived from all neural language models across the three corpora, along with the code to extract the surprisal, are made publicly available ( https://osf.io/wr4qf/ ).</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"266"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publisher Correction: Praditor: A DBSCAN-based automation for speech onset detection.","authors":"Zhengyuan Liu, Xinqi Yu, Wing Chung Hu, Yunxiao Ma, Ruiming Wang, Haoyun Zhang","doi":"10.3758/s13428-025-02799-9","DOIUrl":"10.3758/s13428-025-02799-9","url":null,"abstract":"","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"260"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informative data visualization with raincloud plots in JASP.","authors":"Vincent L Ott, Don van den Bergh, Bruno Boutin, Johnny van Doorn, František Bartoš, Nicholas Judd, Jordy van Langen, Luke Korthals, Rogier Kievit, Laura Groot, Eric-Jan Wagenmakers","doi":"10.3758/s13428-025-02773-5","DOIUrl":"10.3758/s13428-025-02773-5","url":null,"abstract":"<p><p>Proper data visualization helps researchers draw correct conclusions from their data and facilitates a more complete and transparent report of the results. In factorial designs, so-called raincloud plots have recently attracted attention as a particularly informative data visualization technique; raincloud plots can simultaneously show summary statistics (i.e., a box plot), a density estimate (i.e., the cloud), and the individual data points (i.e., the raindrops). Here we first present a 'raincloud quartet' that underscores the added value of raincloud plots over the traditional presentation of means and confidence intervals. The added value of raincloud plots appears to be increasingly recognized: a focused literature review of plots in Psychonomic Bulletin & Review shows that 9% of plots in 2023 were raincloud plots. Another 29% of plots (vs. 2% in 2013) contained individual data points (i.e., raindrops), indicating a strong trend towards transparent and informative data visualization. To further encourage this trend and make raincloud plotting easy and practical for a broader group of researchers and students, we implemented a comprehensive suite of raincloud plots in JASP, an open-source statistics program with an intuitive graphical user interface. Examples from two factorial research designs illustrate how the JASP raincloud plots support a correct and comprehensive interpretation of the data.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"265"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAP: The creativity assessment platform for online testing and automated scoring.","authors":"John D Patterson, Jimmy Pronchick, Ruchi Panchanadikar, Mark Fuge, Janet G van Hell, Scarlett R Miller, Dan R Johnson, Roger E Beaty","doi":"10.3758/s13428-025-02761-9","DOIUrl":"10.3758/s13428-025-02761-9","url":null,"abstract":"<p><p>Creativity is increasingly recognized as a core competency for the 21st century, making its development a priority in education, research, and industry. To effectively cultivate creativity, researchers and educators need reliable and accessible assessment tools. Recent software developments have significantly enhanced the administration and scoring of creativity measures; however, existing software often requires expertise in experiment design and computer programming, limiting its accessibility to many educators and researchers. In the current work, we introduce CAP-the Creativity Assessment Platform-a free web application for building creativity assessments, collecting data, and automatically scoring responses (cap.ist.psu.edu). CAP allows users to create custom creativity assessments in ten languages using a simple, point-and-click interface, selecting from tasks such as the Short Story Task, Drawing Task, and Scientific Creative Thinking Test. Users can automatically score task responses using machine learning models trained to match human creativity ratings-with multilingual capabilities, including the new Cross-Lingual Alternate Uses Scoring (CLAUS), a large language model achieving strong prediction of human creativity ratings in ten languages. CAP also provides a centralized dashboard to monitor data collection, score assessments, and automatically generate text for a Methods section based on the study's tasks, metrics, and instructions-with a single click-promoting transparency and reproducibility in creativity assessment. Designed for ease of use, CAP aims to democratize creativity measurement for researchers, educators, and everyone in between.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"264"},"PeriodicalIF":3.9,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabla: A voice-based ecological assessment method for securely collecting spoken responses to researcher questions.","authors":"Deanna M Kaplan, Santiago J Arconada Alvarez, Roman Palitsky, Hyoann Choi, Gari D Clifford, Melese Crozier, Boadie W Dunlop, George H Grant, Morgan N Greenleaf, Leslie M Johnson, Jessica Maples-Keller, Holly F Levin-Aspenson, Jennifer S Mascaro, Ariel McDowall, Nicole S Pozzo, Charles L Raison, Ali John Zarrabi, Barbara O Rothbaum, Wilbur A Lam","doi":"10.3758/s13428-025-02777-1","DOIUrl":"10.3758/s13428-025-02777-1","url":null,"abstract":"<p><p>This article reports on the validation of Fabla, a researcher-developed and university-hosted smartphone app that facilitates naturalistic and secure collection of participants' spoken responses to researcher questions. Fabla was developed to meet the need for tools that (a) collect longitudinal qualitative data and (b) capture speech biomarkers from participants' natural environments. This study put Fabla to its first empirical test using a repeated-measures experimental design in which participants (n = 87) completed a 1-week voice daily diary via the Fabla app, and an identical 1-week text-entry daily diary administered via Qualtrics, with diary method order counterbalanced and randomized. A preregistered analysis plan investigated (1) adherence, usability, and acceptability of Fabla, (2) concurrent validity of voice diaries (vs. text-entry diaries) by comparing linguistic features obtained via each diary method, and (3) differences in the strength of the association between linguistic features and their known psychological correlates when assessed by voice versus text-entry diary. Voice diaries yielded more than double the mean daily language volume (word count) compared to text-entry diaries and received high usability and acceptability ratings. Linguistic markers consistently associated with depression in prior research were significantly associated with depression symptoms when assessed via voice but not text-entry diaries, and the difference in correlation magnitude was significant. Word-count-adjusted linguistic patterns were highly correlated between diary methods, with statistically significant mean differences observed for some linguistic dimensions in the presence of these associations. Fabla is a promising tool for collecting high-quality speech data from participants' naturalistic environments, overcoming multiple limitations of text-entry responding.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"257"},"PeriodicalIF":3.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single point estimation of a decision space.","authors":"Harinder Aujla","doi":"10.3758/s13428-025-02768-2","DOIUrl":"10.3758/s13428-025-02768-2","url":null,"abstract":"<p><p>Signal detection theory (SDT) was developed to provide independent measures of sensitivity and bias for an observer asked to discriminate a signal stimulus against background noise. The sensitivity measure, <math><msup><mi>d</mi> <mo>'</mo></msup> </math> , achieves this goal when the underlying decision space consists of two Gaussian distributions of equal variance. However, <math><msup><mi>d</mi> <mo>'</mo></msup> </math> fails to provide a stable measure of sensitivity when the distributions are of unequal variance. In addition, unequal base rates of stimulus presentations or asymmetries in the payoff matrix for decision outcomes further shift criterion placement and estimations of sensitivity. Alternative sensitivity metrics that attempt to consider these scenarios either require information across multiple confidence levels or make implicit assumptions about the underlying decision space a priori. I propose an optimization approach that accurately estimates information about the underlying decision space without requiring information over multiple confidence levels. The proposed approach requires <math><mi>β</mi></math> and a single false-alarm and hit rate pair. The reliance on <math><mi>β</mi></math> limits the proposed method to providing a normative model of performance where the researcher is operating under a theoretical framework of decision. Simulations illustrate that in cases where <math><mi>β</mi></math> is known, or can be reasonably estimated, the optimization approach is successful in recovering the critical characteristics of the decision space.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 9","pages":"258"},"PeriodicalIF":3.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}