Behavior Research Methods最新文献

We present VOC-ADO, a database of the written vocabulary of French adolescents between the ages of 11 and 15 (French secondary school students). VOC-ADO provides a wealth of lexical information for 110,338 words listed in school textbooks of all disciplines (i.e., academic vocabulary), as well as novels, comics, and magazines (i.e., non-academic vocabulary). For each word, several indexes of frequency and lexical dispersion are reported, as well as word length, syntactic categories, orthographic neighborhood size, and lemma frequency. Each analysis is presented separately for the Academic and Non-academic subcorpora, as well as for the overall Global corpus. Analyses of the corpora indicate that the Academic subcorpus contains a smaller variety of unique words than the Non-academic subcorpus and exhibits higher lexical sophistication. By contrast, there is a larger proportion of content words in non-academic media than in school textbooks. Finally, VOC-ADO shows a strong frequency correlation with Manulex, a French database of elementary school vocabulary, and Lexique, a lexical database of adult vocabulary. However, many words present in VOC-ADO are not found in elementary school vocabulary. These results underscore the need to examine lexical development beyond elementary school, considering the unique characteristics of the written vocabulary encountered by French-speaking adolescents. In this regard, VOC-ADO provides researchers, educators, and clinicians interested in adolescent literacy with a valuable tool to select and analyze words based on specific characteristics. The database is freely available and can be downloaded by clicking on the following link: VOC-ADO Database link.

We present SingleMALD, a large-scale auditory lexical decision study in English with a fully crossed design. SingleMALD is freely available and includes over 2 million trials in which 40 native speakers of English responded to over 26,000 different words and over 9000 different pseudowords, each in 67 balanced sessions. SingleMALD features a large number of responses per stimulus, but a smaller number of participants, thus complementing the Massive Auditory Lexical Decision (MALD) dataset which features many listeners but fewer responses per stimulus. In the present report, we also use SingleMALD data to explore how extensive testing affects performance in the auditory lexical decision task. SingleMALD participants show signs of favoring speed over accuracy as the sessions unfold. Additionally, we find that the relationship between participant performance and two lexical predictors - word frequency and phonological neighborhood density - changes as sessions unfold, especially for certain lexical predictor values. We note that none of the changes are drastic, indicating that data collected from participants that have been extensively tested is usable, although we recommend accounting for participant experience with the task when performing statistical analyses of the data.

To evaluate model fit in structural equation modeling, researchers commonly compare fit indices against fixed cutoff values (e.g., CFI ≥ .950). However, methodologists have cautioned against overgeneralizing cutoffs, highlighting that cutoffs permit valid judgments of model fit only in empirical settings similar to the simulation scenarios from which these cutoffs originate. This is because fit indices are not only sensitive to misspecification but are also susceptible to various model, estimation, and data characteristics. As a solution, methodologists have proposed four principal approaches to obtain so-called tailored cutoffs, which are generated specifically for a given setting. Here, we review these approaches. We find that none of these approaches provides guidelines on which fit index (out of all fit indices of interest) is best suited for evaluating whether the model fits the data in the setting of interest. Therefore, we propose a novel approach combining a Monte Carlo simulation with receiver operating characteristic (ROC) analysis. This so-called simulation-cum-ROC approach generates tailored cutoffs and additionally identifies the most reliable fit indices in the setting of interest. We provide R code and a Shiny app for an easy implementation of the approach. No prior knowledge of Monte Carlo simulations or ROC analysis is needed to generate tailored cutoffs with the simulation-cum-ROC approach.

The ability to perceive others' emotions and one's own interoceptive states has been the subject of extensive research. Very little work, however, has investigated the ability to recognise others' interoceptive states, such as whether an individual is feeling breathless, nauseated, or fatigued. This is likely owing to the dearth of stimuli available for use in research studies, despite the clear relevance of this ability to social interaction and effective caregiving. This paper describes the development and validation of two stimulus sets for use in research into the perception of others' interoceptive states. The Interoceptive States Vocalisations (ISV) database and the Interoceptive States Point Light Displays (ISPLD) database include 191 vocalisation and 159 point light display stimuli. Both stimulus sets underwent two phases of validation, and all stimuli were scored in terms of their quality and recognisability, using five different measures. The ISV also includes control stimuli featuring non-interoceptive vocalisations. Some interoceptive states were consistently recognised better than others, but variability was observed within, as well as between, stimulus categories. Stimuli are freely available for use in research, and are presented alongside all stimulus quality scores, in order for researchers to select the most appropriate stimuli based on individual research questions.

Visually observing a touch quickly reveals who is being touched, how it might feel, and the broader social or emotional context, shaping our interpretation of such interactions. Investigating these dimensions is essential for understanding how tactile experiences are processed individually and how we empathise with observed sensations in others. Here, we expand available resources for studying visually perceived touch by providing a wide-ranging set of dynamic interactions that specifically focus on the sensory qualities of touch. The Validated Touch-Video Database (VTD) consists of a set of 90 videos depicting tactile interactions with a stationary left hand, viewed from a first-person perspective. In each video, a second hand makes contact either directly (e.g., with fingers or an open palm) or using an object (e.g., a soft brush or scissors), with variations across dimensions such as hedonic qualities, arousal, threat, touch type, and the object used. Validation by 350 participants (283 women, 66 men, 1 non-binary) involved categorising the videos as 'neutral', 'pleasant', 'unpleasant', or 'painful' and rating arousal and threat levels. Our findings reveal high inter-subject agreement, with painful touch videos eliciting the highest arousal and threat ratings, while neutral touch videos serve as a baseline. Exploratory analyses indicate that women rated the videos as more threatening and painful than men, suggesting potential gender differences in the visual perception of negatively valenced touch stimuli. The VTD provides a comprehensive resource for researchers investigating the sensory and emotional dimensions of observed touch.

As a method for developing and testing hypotheses, meta-analytic structural equation modeling, or MASEM, has drawn the interest of scholars. However, challenges remain in how we can model and explain meaningful heterogeneity in structural equation modeling (SEM) parameters. To address this issue, two novel methods have recently been proposed in the literature: Bayesian MASEM (BMASEM) and one-stage MASEM (OSMASEM). How the two methods can be applied to address actual psychological research questions involving heterogeneity is a topic of debate and confusion. In this study, we describe and compare the two methods using two illustrations on the mediating mechanism of mindfulness-based intervention and the factor structure of Rosenberg Self-Esteem Scale. In the illustrations, both methods were used to test the moderating effect of a covariate, to build a prediction equation for effect sizes in specific populations, and to evaluate the equivalence of standardized factor loadings of a scale. The study ends with a discussion of practical issues that may arise when applying BMASEM and OSMASEM.

Colorimetry is a technology capable of quantifying human color perception. It is of great importance in vision science and color psychology, helping to develop color standards, color measurement devices, and color management systems. Nowadays, the basic method of colorimetry implies comparing two simultaneously presented stimuli (semi-fields). In this case, two different retinal areas are exposed to colors to be matched. This drawback is not inherent to the strict substitution method, which involves comparing the perception of two color stimuli alternately presented on the same area of the retina. This method was proposed almost 70 years ago by Bongard and Smirnov, but has not been developed since then because of its apparent complexity. Today, light sources with a controlled emission spectrum have come into use so much that it becomes much easier to make a colorimetric installation that implements the strict substitution method. In this paper, we propose a comprehensive procedure for implementing strict substitution colorimetry, aimed at collecting data on threshold perceptual color differences in humans. Our pilot experimental results show good consistency and repeatability. We believe that the suggested technique will allow collecting the lacking color difference data at the periphery of the wide color gamut (WCG) as well as for extremely high dynamic range (HDR) colors.

The Bayes factor is often proposed as a superior replacement to p values in testing null hypotheses for various reasons, with the availability of many user-friendly and easily accessible statistical software tools facilitating the use of Bayesian tests. Meanwhile, Bayes factor design analysis (BFDA), the counterpart of power analysis, is also proposed to ensure the maximum efficiency and informativeness of a study. Despite tools for conducting BFDA being limited and mostly relying on Monte Carlo methodology, methods based on root-finding algorithms have been recently developed (e.g., Pawel and Held, 2025), overcoming weaknesses of simulation approaches. This paper builds on these advancements by presenting a method generalizing the existing approach for conducting BFDA for sample size determination in t tests. The major advantage of the current method is that it does not assume normality of the effect size estimate, allowing more flexibility in the specification of the design and analysis priors. We developed and showcase a user-friendly Shiny app for facilitating the use of BFDA, illustrated with an empirical example. Furthermore, using our method, we explore the operating characteristics of the Bayes factors using various priors.

Deep learning methods have significantly advanced the field of gaze estimation, yet the development of these algorithms is often hindered by a lack of appropriate publicly accessible training datasets. Moreover, models trained on the few available datasets often fail to generalize to new datasets due to both discrepancies in hardware and biological diversity among subjects. To mitigate these challenges, the research community has frequently turned to synthetic datasets, although this approach also has drawbacks, such as the computational resource and labor-intensive nature of creating photorealistic representations of eye images to be used as training data. In response, we introduce "Light Eyes" (LEyes), a novel framework that diverges from traditional photorealistic methods by utilizing simple synthetic image generators to train neural networks for detecting key image features like pupils and corneal reflections, diverging from traditional photorealistic approaches. LEyes facilitates the generation of synthetic data on the fly that is adaptable to any recording device and enhances the efficiency of training neural networks for a wide range of gaze-estimation tasks. Presented evaluations show that LEyes, in many cases, outperforms existing methods in accurately identifying and localizing pupils and corneal reflections across diverse datasets. Additionally, models trained using LEyes data outperform standard eye trackers while employing more cost-effective hardware, offering a promising avenue to overcome the current limitations in gaze estimation technology.

The diffuse discrepancy model (DiffDiscM) has proven to be a valuable tool for simulating both Pavlovian and operant conditioning phenomena. However, its original implementation in Pascal (SelNet1© interface) has limitations regarding accessibility and ease of use. This paper presents DDM-UI, a new user interface developed in R for the DiffDiscM. DDM-UI offers an intuitive, open-source platform that enables researchers to configure, run, and analyze DiffDiscM simulations more efficiently. The main features of DDM-UI are described, including network architecture setup, trial and contingency definition, and result visualization. Three use cases demonstrate the practical application of DDM-UI in simulating various conditioning experiments, including superstition, Pavlovian/autoshaped impulsivity, and complex phenomena such as blocking, compound conditioning, and successive conditioning. The validation process highlights DDM-UI's ability to replicate previous findings while offering enhanced data visualization and analysis capabilities. DDM-UI represents a significant advancement in the accessibility of the DiffDiscM, facilitating its use in behavioral research and promoting reproducibility in the field. The paper also discusses the limitations of the current implementation and suggests future developments to further enhance the tool's capabilities in exploring complex learning and behavioral phenomena.