Behavior Research Methods最新文献

Interval scales are frequently assumed in educational and psychological research involving latent variables, but are rarely verified. This paper outlines methods for investigating the interval scale assumption when fitting the Rasch model to item response data. We study a Bayesian method for evaluating an item response dataset's adherence to the cancellation axioms of additive conjoint measurement under the Rasch model, and compare the extent to which the axiom of double cancellation holds in the data at sample sizes of 250 and 1000 with varying test lengths, difficulty spreads, and levels of adherence to the Rasch model in the data-generating process. Because the statistic produced by the procedure is not directly interpretable as an indicator of whether an interval scale can be established, we develop and evaluate procedures for bootstrapping a null distribution of violation rates against which to compare results. At a sample size of 250, the method under investigation is not well powered to detect the violations of interval scaling that we simulate, but the procedure works quite consistently at N = 1000. That is, at moderate but achievable sample sizes, empirical tests for interval scaling are indeed possible.

When using the Property Listing Task (PLT) to collect semantic content for a set of concepts (Concept Property Norms, CPNs), coding raw properties into standardized labels poses significant challenges. In this work, we address these challenges by enhancing the Assisted Coding for Property Listing Task (AC-PLT) framework, which facilitates the coding process. The current work conducts an ablation study to optimize AC-PLT by evaluating combinations of text cleaning, embedding models (e.g., Word2Vec, E5, LaBSE), and classification methods (e.g., kNN, SVM, XGBoost). Results show that normalization with the E5 embedding model and kNN classification achieves the highest accuracy, with top-1 test accuracies of 0.523 for CPN27 and 0.608 for CPN120 datasets, outperforming the original AC-PLT baseline. Comparisons with ChatGPT (fine-tuned and one-shot) reveal AC-PLT's superior stability and cost-effectiveness, despite ChatGPT's competitive performance in some cases. The improved AC-PLT framework offers a scalable, efficient solution to manual coding challenges, reducing variability and time constraints. Future work will explore its role as a recommender system for human coders, further enhancing its practical utility in cognitive psychology and psycholinguistics research.

This paper revisits the age-of-acquisition (AoA) norms of Kuperman et al. (2012). Three studies were conducted. Study 1 reports a crowdsourcing 'megastudy' obtaining 790,024 estimates from participants with the age they could first read and write 11,074 early acquired words from Kuperman et al. (2012). The study aimed to differentiate between oral language receptive AoA and print-based AoA. The results correlate well with the original estimates, offering, as hypothesized, higher AoAs for reading/writing. These are released as supplements to the original norms. Study 2 explored the potential of large language models (LLMs), specifically GPT-4o, to replicate these crowdsourced AoA estimates. The findings indicated a strong correlation between AI-generated estimates and human judgments, showing the utility of AI in estimating AoA and developing norms for psycholinguistic and educational research in lieu of crowdsourcing. Study 3 leveraged AI to extend estimates to all well-known words in Kuperman et al. (2012) and the English Crowdsourcing Project (ECP). Study 3 also investigated a trained model fine-tuned on 2000 ratings from Kuperman et al. (2012). Fine-tuning increased alignment with human ratings, though comparisons with untrained models suggested that fine-tuning is not essential in English for obtaining useful AoA estimates. Both trained and untrained AI-generated norms correlated highly with human ratings and performed well in accounting for word processing times and accuracy in regressions. Uses and limitations of the AI estimates are discussed. All resources are made available in the Open Science Framework and can be used freely for research and education.

Neurodevelopmental disorders like ADHD can affect rhythm perception and production, impacting performance in attention and sensorimotor tasks. Improving rhythmic abilities through targeted training might compensate for these cognitive functions. We introduce a novel protocol for training rhythmic skills via a tablet-based, serious game called Rhythm Workers (RW). This proof-of-concept study tested the feasibility of using RW in children with ADHD. We administered an at-home longitudinal protocol across Canada. A total of 27 children (7 to 13 years) were randomly assigned to either a finger-tapping rhythmic game (RW) or a control game with comparable auditory-motor demands but without beat synchronization (active control condition). Participants played the game for 300 min over 2 weeks. We collected data (self-reported and logged onto the device) on game compliance and acceptance. Further, we measured rhythmic abilities using the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA). The current findings show that both games were equally played in duration, rated similarly for overall enjoyment, and relied on similar motor activity (finger taps). The children who played RW showed improved general rhythmic abilities compared to the controls; these improvements were also positively correlated with the playing duration. We also present evidence that executive functioning improved in those who played RW, but not in the controls. These findings indicate that both games are well matched. RW demonstrates efficacy in enhancing sensorimotor skills in children with ADHD, which may benefit their executive functioning. A future RCT with extended training and sample size could further validate these skill transfer effects.

Many studies have used images of novel objects as experimental materials. Existing novel object databases do not provide diverse exemplars, and many studies need to manipulate or examine the diversity of exemplars. To fill this gap in experimental materials, the present study introduces the Novel Prototype and Exemplar (NPE) database. This database contains 108 prototypes and 2592 exemplars of viewpoint and shape variants. The present study conducted four experiments to standardize the database and validate nine dimensions of the novel objects in the NPE database. Experiment 1 standardized familiarity, visual complexity, naming difficulty, reality, and comfort. The results revealed that the prototypes in the NPE database performed well in these attributes. Experiment 2 used the same methodology and reported that the prototypes have high absolute novelty, high relative novelty, low name agreement, and low identity agreement. Experiment 3 used multidimensional scaling (MDS) and Monte Carlo simulations and revealed that the similarity between prototypes was low. Experiment 4 generated exemplars by varying viewpoints and shapes and used the same methodology to evaluate the similarity between prototypes and exemplars and between different exemplars. Finally, Experiment 4 used these evaluation results to select appropriate typical and atypical exemplars. In conclusion, the NPE database contains the largest number of novel 3D object images, controls for the largest number of subjective dimensions, and is the first to develop abundant exemplars. As an open-source novel object database, the NPE database will greatly contribute to research in psychology, linguistics, cognitive science, ergonomics, artificial intelligence (AI), etc.

Latent class analysis (LCA) can help identify unobserved classes of individuals in a population based on collected categorical data. It is commonly used in psychology to test hypotheses about sources of heterogeneity and class characteristics. However, careful decision-making is required in the modeling process. Its flexibility may explain why it is becoming more commonly used in psychology; however, it also highlights that there are many decision points in the modeling process, thus warranting a systematic literature review to document the use of LCA in psychology, mapping both the prevalence and quality of LCA studies. This systematic review followed the PRISMA guidelines and involved a comprehensive search across multiple databases, yielding 7,580 records related to latent class analysis. After removing duplicates and selecting a representative subsample, 377 documents were assessed for eligibility. Of these, 251 publications (comprising 313 LCAs) met the inclusion and exclusion criteria and were reviewed for this study. Each study was meticulously coded to map how the authors performed and reported each step of the LCA. Our analysis of these studies, in comparison with published guidelines, revealed notable discrepancies in how LCA is applied and reported. To support researchers in enhancing the quality of future LCA applications, we summarize key recommendations in a final section that outlines best practices for future LCA applications. The findings indicate a growing use of LCA in psychology but also highlight the need for greater methodological rigor and transparency in its implementation.

Vigilance decrement refers to the decline in sustained attention over time during prolonged tasks, which often leads to increased errors and accidents. However, to date, there are no experimental tasks that simultaneously measure changes in vigilance, cognitive control, and mind-wandering (MW) across time-on-task. We adapted the Attentional Network Test for Interactions and Vigilance-executive and arousal components (ANTI-Vea) task to integrate mind-wandering measures along with assessments of vigilance and cognitive control. By inserting thought probes (TPs) at different frequencies per block, we aimed to identify the optimal TP rate to capture mind-wandering changes without interfering with the measurement of vigilance, thereby providing an integrative assessment of changes in mind-wandering, cognitive control, and vigilance across time. We conducted two experiments: one in the laboratory with 90 students from the National University of Córdoba, Argentina, and another online, as a replication, with 180 students from the University of Granada, Spain. Participants were divided into three groups (4, 8, 12 TPs per block) and completed the ANTI-Vea-TP task. The results revealed that the inclusion of TPs was effective in detecting changes in mind-wandering over time-on-task. Moreover, TP frequency did not have a significant effect on mind-wandering reports, vigilance, or cognitive control over time-on-task. We discuss the potential suitability of this tool for investigating the interaction between vigilance, cognitive control, and mind-wandering, in both laboratory and online environments, which is essential for evaluating different theories of vigilance decrement.

This paper describes a dataset consisting of manually annotated nouns from a corpus of longitudinal day-long audio and hour-long video recordings collected monthly from 44 babies from age 6 months to age 17 months. This dataset was created as part of a larger project, called SEEDLingS, that examines the development of infants' language comprehension before and after their first birthday, from earliest comprehension to the early days of word production. This paper provides an overview of the corpus, describes how and why the nouns from the corpus were annotated, and discusses considerations for the reuse of this dataset for future work. The described annotations and relevant metadata are publicly available alongside this manuscript.

Research into dyadic social interactions has expanded significantly, enabling a deeper understanding of the dynamic processes underlying interpersonal communication. As the use of larger datasets becomes increasingly common in this field, the need for scalable and efficient analytical tools has grown. Automated coding methods, such as those provided by OpenPose, an open-source software for detecting and tracking human motion, offer significant advantages for analysing the movement of two individuals during an interaction. However, the processing and analysis of large quantities of JSON output files generated by OpenPose remain a considerable challenge. To address this, we introduce duet, an R package designed to streamline the processing and analysis of OpenPose output data, particularly in the context of dyadic interactions. The package provides a suite of functions for data cleaning, interpolation, kinematic analysis, and visualisation, offering researchers a comprehensive and user-friendly workflow. By simplifying the handling of OpenPose data, duet aims to facilitate large-scale, automated analysis of dyadic social interactions, with minimal coding experience, thereby advancing methodological capabilities in social and behavioural sciences.

Optional stopping refers to the practice of repeatedly performing a statistical analysis on a dataset as new data are collected until a pre-specified decision criterion is reached. This procedure is often adopted because of its effectiveness in optimizing data collection. Discussions of optional stopping to date have primarily centred around statistical issues, with relatively little consideration of any methodological implications of this procedure. Building on recent work drawing attention to methodological biases arising from the use of optional stopping, we highlight experimenter awareness of the current evidence state during data collection (experimenter evidence unmasking) as a salient methodological confound of optional stopping. We argue that experimenter evidence unmasking has the potential to influence an experimenter to implicitly or explicitly modify their behaviour in ways that can reduce the internal validity of an experiment. We conclude by offering recommendations for circumventing this confound and for the transparent reporting of experimenter evidence masking procedures.