Psychological methods最新文献_第7页

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters. 将认知建模扩展到大数据：研究证据积累模型参数个体差异的深度学习方法。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-06-18 DOI: 10.1037/met0000842

Mischa von Krause, Stefan T Radev

{"title":"Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.","authors":"Mischa von Krause, Stefan T Radev","doi":"10.1037/met0000842","DOIUrl":"10.1037/met0000842","url":null,"abstract":"Recent advances in Bayesian modeling and deep learning have enabled scalable estimation of cognitive process models. In this article, we present a fully Bayesian workflow that leverages amortized inference with neural networks to rapidly estimate individual parameters and compare models from big behavioral data. Using data from a large online implicit association test sample (N > 5,000,000), we investigate how latent parameters, such as drift rate, boundary separation, nondecision times, and their variabilities, relate to key socioeconomic variables. Our exploratory findings reveal small but consistent associations of cognitive model parameters with socioeconomic covariates. Notably, trial-by-trial variability in drift rate, often ignored in prior work, emerged as the strongest predictor across all socioeconomic covariates. Our primary contribution lies in illustrating how deep learning-based Bayesian estimation and model comparison can be applied to mine robust insights from large and noisy behavioral data sets. We discuss limitations and implications for modeling individual differences in large-scale data sets and provide an open pipeline for future use. This work exemplifies how the emerging field of behavioral data science can extend cognitive modeling to new domains and support data-driven hypothesis generation targeting the cognitive underpinnings of individual differences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148278623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supplemental Material for Scaling Cognitive Modeling to Big Data: A Deep Learning Approach to Studying Individual Differences in Evidence Accumulation Model Parameters 将认知模型扩展到大数据：一种研究证据积累模型参数个体差异的深度学习方法

IF 7 1区心理学

Psychological methods Pub Date : 2026-06-15 DOI: 10.1037/met0000842.supp

引用次数: 0

Structured hierarchical regression for Likert scales including dispersion effects: Models and fitting tools. 包括分散效应的李克特量表的结构层次回归：模型和拟合工具。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-06-11 DOI: 10.1037/met0000843

Gerhard Tutz, Moritz Berger

引用次数: 0

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model. 实验心理学中心理测量学的统一框架：标准化广义层次因子模型。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-06-11 DOI: 10.1037/met0000846

Ricardo Rey-Sáez, Alicia Franco-Martínez, Javier Revuelta, Miguel A Vadillo

{"title":"A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.","authors":"Ricardo Rey-Sáez, Alicia Franco-Martínez, Javier Revuelta, Miguel A Vadillo","doi":"10.1037/met0000846","DOIUrl":"10.1037/met0000846","url":null,"abstract":"To address Cronbach's longstanding call to unify experimental and correlational psychology, hierarchical factor models (HFMs) have emerged as a promising approach to estimate individual differences while accounting for trial-level noise. As formalized by Rouder et al. (2025), this framework assumes Gaussian response times and operates in raw units. Here, we generalize this framework by introducing the standardized generalized HFM (GenHFM), which incorporates two natural extensions: Explicitly modeling asymmetric distributions and implementing a fully standardized latent structure. First, modeling asymmetry allows us to capture the true shape of response times and, crucially, quantify the bias introduced by fitting Gaussian HFMs to skewed data. Second, the standardized parameterization ensures transparent prior specification and facilitates the analysis of factor loadings, enabling researchers to directly compare the degree of association between experimental effects and common factors, regardless of their original metrics. Consequently, this allows researchers to evaluate the discriminative capacity of each task regarding the latent process, mirroring the assessment of construct validity in classical psychometrics within experimental settings. We conducted a simulation study showing that Gaussian HFMs can underestimate true correlations by up to 50%, whereas GenHFM yields unbiased and more efficient estimates. Finally, we reanalyze data from two published studies using executive control tasks to illustrate the empirical application of these models. Furthermore, we show that GenHFM achieves better predictive accuracy than alternative models typically used in the field. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148219446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supplemental Material for A Unified Framework for Psychometrics in Experimental Psychology: The Standardized Generalized Hierarchical Factor Model 实验心理学心理测量学统一框架补充材料：标准化广义层次因子模型

IF 7 1区心理学

Psychological methods Pub Date : 2026-06-08 DOI: 10.1037/met0000846.supp

引用次数: 0

Scoring assessments in multisite randomized control trials: Examining the sensitivity of treatment effect estimates to measurement choices. 多地点随机对照试验中的评分评估：研究治疗效果估计值对测量选择的敏感性。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-06-01 Epub Date: 2023-12-21 DOI: 10.1037/met0000633

Megan Kuhfeld, James Soland

引用次数: 0

Unlocking nonlinear dynamics and multistability from intensive longitudinal data: A novel method. 从密集纵向数据中揭示非线性动态和多稳定性：一种新方法

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-06-01 Epub Date: 2023-12-14 DOI: 10.1037/met0000623

Jingmeng Cui, Fred Hasselman, Anna Lichtwarck-Aschoff

{"title":"Unlocking nonlinear dynamics and multistability from intensive longitudinal data: A novel method.","authors":"Jingmeng Cui, Fred Hasselman, Anna Lichtwarck-Aschoff","doi":"10.1037/met0000623","DOIUrl":"10.1037/met0000623","url":null,"abstract":"The availability of smart devices has made it possible to collect intensive longitudinal data (ILD) from individuals, providing a unique opportunity to study the complex dynamics of psychological systems. Existing time-series methods often have limitations, such as assuming linear interactions or having restricted forms, leading to difficulties in capturing the complex nature of these systems. To address this issue, we introduce fitlandr, a method with implementation as an R package that integrates nonparametric estimation of the drift-diffusion function and stability landscape. The drift-diffusion function is estimated using the multivariate kernel estimator (MVKE; Bandi & Moloche, 2018), and the stability landscape is estimated through Monte-Carlo estimation of the steady-state distribution (Cui et al., 2021; Cui, Lichtwarck-Aschoff, et al., 2023). Using a simulated emotional system, we demonstrate that fitlandr can effectively recover bistable dynamics from data, even in the presence of moderate noise, and that it primarily relies on dynamic information from the system instead of distributional information. We then apply the method to two empirical single-participant experience sampling method data sets and compared the results with the simulation data sets. Whereas both data sets show a bimodal distribution, fitlandr only revealed bistability in one of them, indicating that bimodality in ILD does not necessarily imply the existence of bistability in the underlying system. These results demonstrate the potential of fitlandr as a tool for uncovering the rich, nonlinear dynamics of psychological systems from ILD. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"421-437"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A general Monte Carlo method for sample size analysis in the context of network models. 网络模型中样本大小分析的一般蒙特卡罗方法。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-06-01 Epub Date: 2023-07-10 DOI: 10.1037/met0000555

Mihai A Constantin, Noémi K Schuurman, Jeroen K Vermunt

{"title":"A general Monte Carlo method for sample size analysis in the context of network models.","authors":"Mihai A Constantin, Noémi K Schuurman, Jeroen K Vermunt","doi":"10.1037/met0000555","DOIUrl":"10.1037/met0000555","url":null,"abstract":"We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hypothesized network structure or desired characteristics of that structure, (2) an estimation performance measure and its corresponding target value (e.g., a sensitivity of 0.6), and (3) a statistic and its corresponding target value that determines how the target value for the performance measure be reached (e.g., reaching a sensitivity of 0.6 with a probability of 0.8). The method consists of a Monte Carlo simulation step for computing the performance measure and the statistic for several sample sizes selected from an initial candidate sample size range, a curve-fitting step for interpolating the statistic across the entire candidate range, and a stratified bootstrapping step to quantify the uncertainty around the recommendation provided. We evaluated the performance of the method for the Gaussian Graphical Model, but it can easily extend to other models. The method displayed good performance, providing sample size recommendations that were, on average, within three observations of a benchmark sample size, with the highest standard deviation of 25.87 observations. The method discussed is implemented in the form of an R package called powerly, available on GitHub and CRAN. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"385-405"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9823285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A graph theory based similarity metric enables comparison of subpopulation psychometric networks. 基于图论的相似性度量可用于比较子群心理测量网络。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-06-01 Epub Date: 2023-12-21 DOI: 10.1037/met0000625

Esther Ulitzsch, Saurabh Khanna, Mijke Rhemtulla, Benjamin W Domingue

{"title":"A graph theory based similarity metric enables comparison of subpopulation psychometric networks.","authors":"Esther Ulitzsch, Saurabh Khanna, Mijke Rhemtulla, Benjamin W Domingue","doi":"10.1037/met0000625","DOIUrl":"10.1037/met0000625","url":null,"abstract":"Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks' weighted adjacency matrices. To assess this measure's utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"438-460"},"PeriodicalIF":7.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138831212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attributing individual-level causal effects using experimental and observational data: A primer. 使用实验和观察数据归因个人水平的因果效应：引物。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-06-01 Epub Date: 2026-04-30 DOI: 10.1037/met0000808

Tim Kaiser, Stephen G West, Steffi Pohl

{"title":"Attributing individual-level causal effects using experimental and observational data: A primer.","authors":"Tim Kaiser, Stephen G West, Steffi Pohl","doi":"10.1037/met0000808","DOIUrl":"10.1037/met0000808","url":null,"abstract":"Causal inference of the effect of a treatment on an outcome is usually done on the group or subgroup level. Although the typically reported average treatment effect may be positive, suggesting that the treatment is effective, at the level of individual participants, the treatment effect may be zero or even negative-the treatment may even harm some individuals. For making decisions on whether a specific person should take the treatment, information on the probability of benefiting or being harmed by the treatment for a single person is necessary. Estimating the probability of possible benefit or possible harm for a person involves counterfactual reasoning and thus strong assumptions about unobservable events. Precise statements about the causal effect of a treatment for an individual are only possible to a limited extent. This tutorial introduces the method of causal attribution to psychology that allows for estimating bounds in which the probability of benefit or harm lies. These bounds can be calculated using data at the group level, which can come from experimental or observational studies. The bounds can be narrowed by simultaneously using data from both randomized trials and observational studies and by using information from pretreatment covariates. R functions are provided for calculating these bounds from binary data and are illustrated with examples from basic laboratory research and clinical intervention research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"569-583"},"PeriodicalIF":7.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0