Pub Date : 2024-10-01Epub Date: 2024-07-24DOI: 10.3758/s13428-024-02466-5
Audrey Lalancette, Élisabeth Garneau, Alice Cochrane, Maximiliano A Wilson
Body-object interaction (BOI) measures the ease with which the human body can interact with the concept represented by a word. This research focuses on two main objectives: first, to establish French norms for the psycholinguistic variable BOI, and second, to investigate the contribution of BOI to language processing in French. We collected BOI ratings for 3600 French nouns from participants through an online platform. The inter- and intrastudy reliability of these new ratings indicate that the ratings are robust. We then aimed to determine the role of BOI in word recognition. A hierarchical regression analysis was conducted using lexical decision reaction times (RTs) as the dependent variable. BOI was found to be a significant predictor of lexical decision latencies, beyond the contribution of word length, frequency, orthographic distinctiveness, and imageability. Contrary to previous findings in English, higher BOI values were associated with longer RTs in French, indicating an inhibitory effect of BOI on French word processing. Methodological differences may account for this divergent result. Taken together, the results of this study show the independent contribution of BOI to word recognition in French. This supports the notion that sensorimotor information is a crucial component of language processing. By providing a reliable and sizable BOI database for French nouns, we offer a valuable resource for psycholinguistic and language processing research. This research underscores the complex relationship between language, cognition, and sensorimotor experiences, advancing our comprehension of language processing mechanisms.
{"title":"Body-object interaction ratings for 3600 French nouns.","authors":"Audrey Lalancette, Élisabeth Garneau, Alice Cochrane, Maximiliano A Wilson","doi":"10.3758/s13428-024-02466-5","DOIUrl":"10.3758/s13428-024-02466-5","url":null,"abstract":"<p><p>Body-object interaction (BOI) measures the ease with which the human body can interact with the concept represented by a word. This research focuses on two main objectives: first, to establish French norms for the psycholinguistic variable BOI, and second, to investigate the contribution of BOI to language processing in French. We collected BOI ratings for 3600 French nouns from participants through an online platform. The inter- and intrastudy reliability of these new ratings indicate that the ratings are robust. We then aimed to determine the role of BOI in word recognition. A hierarchical regression analysis was conducted using lexical decision reaction times (RTs) as the dependent variable. BOI was found to be a significant predictor of lexical decision latencies, beyond the contribution of word length, frequency, orthographic distinctiveness, and imageability. Contrary to previous findings in English, higher BOI values were associated with longer RTs in French, indicating an inhibitory effect of BOI on French word processing. Methodological differences may account for this divergent result. Taken together, the results of this study show the independent contribution of BOI to word recognition in French. This supports the notion that sensorimotor information is a crucial component of language processing. By providing a reliable and sizable BOI database for French nouns, we offer a valuable resource for psycholinguistic and language processing research. This research underscores the complex relationship between language, cognition, and sensorimotor experiences, advancing our comprehension of language processing mechanisms.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141756838","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}
Pub Date : 2024-10-01Epub Date: 2024-07-24DOI: 10.3758/s13428-024-02457-6
Jose Manuel Rivera Espejo, Sven De Maeyer, Steven Gillis
When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.
{"title":"Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores.","authors":"Jose Manuel Rivera Espejo, Sven De Maeyer, Steven Gillis","doi":"10.3758/s13428-024-02457-6","DOIUrl":"10.3758/s13428-024-02457-6","url":null,"abstract":"<p><p>When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141756839","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}
Pub Date : 2024-10-01Epub Date: 2024-07-30DOI: 10.3758/s13428-024-02471-8
Daniele Marinazzo, Jan Van Roozendaal, Fernando E Rosas, Massimo Stella, Renzo Comolatti, Nigel Colenbier, Sebastiano Stramaglia, Yves Rosseel
Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation.
{"title":"An information-theoretic approach to build hypergraphs in psychometrics.","authors":"Daniele Marinazzo, Jan Van Roozendaal, Fernando E Rosas, Massimo Stella, Renzo Comolatti, Nigel Colenbier, Sebastiano Stramaglia, Yves Rosseel","doi":"10.3758/s13428-024-02471-8","DOIUrl":"10.3758/s13428-024-02471-8","url":null,"abstract":"<p><p>Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141854637","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}
Pub Date : 2024-10-01Epub Date: 2024-03-25DOI: 10.3758/s13428-024-02387-3
Xin Hu, Tanika R Sgherza, Jessie B Nothrup, David M Fresco, Kristin Naragon-Gainey, Lauren M Bylsma
Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.
尽管流动评估越来越受欢迎,但在日常生活环境中,来自可穿戴设备的心理生理信号的可靠性和有效性尚未得到证实。我们以 Mindware 作为实验室黄金标准,评估了从两款可穿戴设备(Movisens EcgMove4 和 Empatica E4)上采集的生理信号(心电图,ECG;光电血压计,PPG;电皮活动,EDA)在实验室(67 人)和日常生活(20 人)中的可靠性和有效性。结果显示,这两款可穿戴设备在日常生活中的有效数据率均低于实验室(分别为 Movisens ECG 82.94 vs. 93.10%,Empatica PPG 8.79 vs. 26.14%,Empatica EDA 41.16 vs. 42.67%)。Empatica PPG 信号在实验室中的有效数据率较低,部分原因可能是参与者的手部运动(r = - .27,p = .03)。在实验室环境中,两种可穿戴设备得出的心率(HR)比心率变异性(HRV)指标表现出更高的并发有效性(ICCs 0.98-1.00 vs. 0.75-0.97)。由 Empatica 得出的皮肤传导反应次数(SCR)的同期有效性高于皮肤传导水平(SCL,ICCs 0.38 vs. 0.09)。在两个实验室中,Movisens EcgMove4 都比 Empatica E4 提供了更可靠和有效的心率变异测量(半分可靠性:0.95-0.99 vs. 0.85-0.98;并发有效性:0.95-1.00 vs. 0.75-0.98;有效数据率:93.10 vs. 26.14%):93.10 vs. 26.14%)和门诊环境(二分之一可靠性:0.99-1.00 vs. 0.89-0.98;有效数据率:82.94 vs. 8.79%):82.94 vs. 8.79%)。虽然可穿戴设备的可靠性和有效性在不断提高,但研究结果表明,研究人员应选择能为他们感兴趣的测量项目提供持续可靠和有效数据的设备。
{"title":"From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings.","authors":"Xin Hu, Tanika R Sgherza, Jessie B Nothrup, David M Fresco, Kristin Naragon-Gainey, Lauren M Bylsma","doi":"10.3758/s13428-024-02387-3","DOIUrl":"10.3758/s13428-024-02387-3","url":null,"abstract":"<p><p>Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288153","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}
Pub Date : 2024-10-01Epub Date: 2024-05-29DOI: 10.3758/s13428-024-02441-0
Alexander Stavropoulos, Damien L Crone, Igor Grossmann
We investigated large language models' (LLMs) efficacy in classifying complex psychological constructs like intellectual humility, perspective-taking, open-mindedness, and search for a compromise in narratives of 347 Canadian and American adults reflecting on a workplace conflict. Using state-of-the-art models like GPT-4 across few-shot and zero-shot paradigms and RoB-ELoC (RoBERTa -fine-tuned-on-Emotion-with-Logistic-Regression-Classifier), we compared their performance with expert human coders. Results showed robust classification by LLMs, with over 80% agreement and F1 scores above 0.85, and high human-model reliability (Cohen's κ Md across top models = .80). RoB-ELoC and few-shot GPT-4 were standout classifiers, although somewhat less effective in categorizing intellectual humility. We offer example workflows for easy integration into research. Our proof-of-concept findings indicate the viability of both open-source and commercial LLMs in automating the coding of complex constructs, potentially transforming social science research.
{"title":"Shadows of wisdom: Classifying meta-cognitive and morally grounded narrative content via large language models.","authors":"Alexander Stavropoulos, Damien L Crone, Igor Grossmann","doi":"10.3758/s13428-024-02441-0","DOIUrl":"10.3758/s13428-024-02441-0","url":null,"abstract":"<p><p>We investigated large language models' (LLMs) efficacy in classifying complex psychological constructs like intellectual humility, perspective-taking, open-mindedness, and search for a compromise in narratives of 347 Canadian and American adults reflecting on a workplace conflict. Using state-of-the-art models like GPT-4 across few-shot and zero-shot paradigms and RoB-ELoC (RoBERTa -fine-tuned-on-Emotion-with-Logistic-Regression-Classifier), we compared their performance with expert human coders. Results showed robust classification by LLMs, with over 80% agreement and F1 scores above 0.85, and high human-model reliability (Cohen's κ Md across top models = .80). RoB-ELoC and few-shot GPT-4 were standout classifiers, although somewhat less effective in categorizing intellectual humility. We offer example workflows for easy integration into research. Our proof-of-concept findings indicate the viability of both open-source and commercial LLMs in automating the coding of complex constructs, potentially transforming social science research.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173767","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}
Pub Date : 2024-10-01Epub Date: 2024-06-25DOI: 10.3758/s13428-024-02433-0
Julia F Christensen, Andrés Fernández, Rebecca A Smith, Georgios Michalareas, Sina H N Yazdi, Fahima Farahi, Eva-Madeleine Schmidt, Nasimeh Bahmanian, Gemma Roig
EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.
{"title":"EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets.","authors":"Julia F Christensen, Andrés Fernández, Rebecca A Smith, Georgios Michalareas, Sina H N Yazdi, Fahima Farahi, Eva-Madeleine Schmidt, Nasimeh Bahmanian, Gemma Roig","doi":"10.3758/s13428-024-02433-0","DOIUrl":"10.3758/s13428-024-02433-0","url":null,"abstract":"<p><p>EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141449505","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}
Pub Date : 2024-10-01Epub Date: 2024-07-26DOI: 10.3758/s13428-024-02442-z
Jianhua Xiong, Zhaosheng Luo, Guanzhong Luo, Xiaofeng Yu, Yujun Li
Cognitive diagnostic assessment (CDA) is widely used because it can provide refined diagnostic information. The Q-matrix is the basis of CDA, and can be specified by domain experts or by data-driven estimation methods based on observed response data. The data-driven Q-matrix estimation methods have become a research hotspot because of their objectivity, accuracy, and low calibration cost. However, most of the existing data-driven methods require known prior knowledge, such as initial Q-matrix, partial q-vector, or the number of attributes. Under the G-DINA model, we propose to estimate the number of attributes and Q-matrix elements simultaneously without any prior knowledge by the sparse non-negative matrix factorization (SNMF) method, which has the advantage of high scalability and universality. Simulation studies are carried out to investigate the performance of the SNMF. The results under a wide variety of simulation conditions indicate that the SNMF has good performance in the accuracy of attribute number and Q-matrix elements estimation. In addition, a set of real data is taken as an example to illustrate its application. Finally, we discuss the limitations of the current study and directions for future research.
{"title":"An exploratory Q-matrix estimation method based on sparse non-negative matrix factorization.","authors":"Jianhua Xiong, Zhaosheng Luo, Guanzhong Luo, Xiaofeng Yu, Yujun Li","doi":"10.3758/s13428-024-02442-z","DOIUrl":"10.3758/s13428-024-02442-z","url":null,"abstract":"<p><p>Cognitive diagnostic assessment (CDA) is widely used because it can provide refined diagnostic information. The Q-matrix is the basis of CDA, and can be specified by domain experts or by data-driven estimation methods based on observed response data. The data-driven Q-matrix estimation methods have become a research hotspot because of their objectivity, accuracy, and low calibration cost. However, most of the existing data-driven methods require known prior knowledge, such as initial Q-matrix, partial q-vector, or the number of attributes. Under the G-DINA model, we propose to estimate the number of attributes and Q-matrix elements simultaneously without any prior knowledge by the sparse non-negative matrix factorization (SNMF) method, which has the advantage of high scalability and universality. Simulation studies are carried out to investigate the performance of the SNMF. The results under a wide variety of simulation conditions indicate that the SNMF has good performance in the accuracy of attribute number and Q-matrix elements estimation. In addition, a set of real data is taken as an example to illustrate its application. Finally, we discuss the limitations of the current study and directions for future research.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765067","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}
Pub Date : 2024-10-01Epub Date: 2024-07-30DOI: 10.3758/s13428-024-02479-0
Lauren E Montgomery, Nora Bradford, Michael D Lee
We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.
{"title":"The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS.","authors":"Lauren E Montgomery, Nora Bradford, Michael D Lee","doi":"10.3758/s13428-024-02479-0","DOIUrl":"10.3758/s13428-024-02479-0","url":null,"abstract":"<p><p>We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141854612","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}
Pub Date : 2024-10-01Epub Date: 2024-03-13DOI: 10.3758/s13428-024-02375-7
Pablo Rogers
Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this 'typical CFA', leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.
确认性因素分析(CFA)是评估测量工具内部结构有效性的一种基本方法。在大多数 CFA 应用中,测量模型只是达到目的的一种手段,而非目的本身。要选择合适的模型,先前的效度证据至关重要,而项目通常是按照应用社会科学中使用的序数量表来评估的。然而,有关结构方程建模(SEM)的教科书往往忽略了这一特殊情况,而将重点放在可使用最大似然法(ML)进行估计的应用上。遗憾的是,一些流行的商业 SEM 软件包缺乏处理这种 "典型 CFA "的合适解决方案,导致在这种情况下进行 CFA 时出现混乱和决策失误。本文从最近的实证研究出发,提出了一套进行典型 CFA 的指导原则,从概念上为这一正在进行的讨论做出了贡献。我们在 JASP 和 lavaan 软件平台上介绍并开发了一个教程示例,为实践做出了贡献。我们还免费提供视频、文件和脚本等补充材料。
{"title":"Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial.","authors":"Pablo Rogers","doi":"10.3758/s13428-024-02375-7","DOIUrl":"10.3758/s13428-024-02375-7","url":null,"abstract":"<p><p>Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this 'typical CFA', leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140118638","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}
Pub Date : 2024-10-01Epub Date: 2024-03-28DOI: 10.3758/s13428-024-02402-7
Marília Prada, David Guedes, Margarida Vaz Garrido, Magda Saraiva
Sounds are important sensory cues for food perception and acceptance. We developed and validated a large-scale database of kitchen and food sounds (180 stimuli) capturing different stages of preparing, cooking, serving, and/or consuming foods and beverages and sounds of packaging, kitchen utensils, and appliances. Each sound was evaluated across nine subjective evaluative dimensions (random order), including stimuli-related properties (e.g., valence, arousal) and food-related items (e.g., healthfulness, appetizingness) by a subsample of 51 to 64 participants (Mdn = 54; N = 332; 69.6% women, Mage = 27.46 years, SD = 10.20). Participants also identified each sound and rated how confident they were in such identification. Results show that, overall, participants could correctly identify the sound or at least recognize the general sound categories. The stimuli of the KFS database varied across different levels (low, moderate, high) of the evaluative dimensions under analysis, indicating good adequacy to a broad range of research purposes. The correlation analysis showed a high degree of association between evaluative dimensions. The sociodemographic characteristics of the sample had a limited influence on the stimuli evaluation. Still, some aspects related to food and cooking were associated with how the sounds are evaluated, suggesting that participants' proficiency in the kitchen should be considered when planning studies with food sounds. Given its broad range of stimulus categories and evaluative dimensions, the KFS database (freely available at OSF ) is suitable for different research domains, from fundamental (e.g., cognitive psychology, basic sensory science) to more applied research (e.g., marketing, consumer science).
{"title":"Normative ratings for the Kitchen and Food Sounds (KFS) database.","authors":"Marília Prada, David Guedes, Margarida Vaz Garrido, Magda Saraiva","doi":"10.3758/s13428-024-02402-7","DOIUrl":"10.3758/s13428-024-02402-7","url":null,"abstract":"<p><p>Sounds are important sensory cues for food perception and acceptance. We developed and validated a large-scale database of kitchen and food sounds (180 stimuli) capturing different stages of preparing, cooking, serving, and/or consuming foods and beverages and sounds of packaging, kitchen utensils, and appliances. Each sound was evaluated across nine subjective evaluative dimensions (random order), including stimuli-related properties (e.g., valence, arousal) and food-related items (e.g., healthfulness, appetizingness) by a subsample of 51 to 64 participants (Mdn = 54; N = 332; 69.6% women, M<sub>age</sub> = 27.46 years, SD = 10.20). Participants also identified each sound and rated how confident they were in such identification. Results show that, overall, participants could correctly identify the sound or at least recognize the general sound categories. The stimuli of the KFS database varied across different levels (low, moderate, high) of the evaluative dimensions under analysis, indicating good adequacy to a broad range of research purposes. The correlation analysis showed a high degree of association between evaluative dimensions. The sociodemographic characteristics of the sample had a limited influence on the stimuli evaluation. Still, some aspects related to food and cooking were associated with how the sounds are evaluated, suggesting that participants' proficiency in the kitchen should be considered when planning studies with food sounds. Given its broad range of stimulus categories and evaluative dimensions, the KFS database (freely available at OSF ) is suitable for different research domains, from fundamental (e.g., cognitive psychology, basic sensory science) to more applied research (e.g., marketing, consumer science).</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140317702","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}