Behavior Research Methods最新文献

PSYCHOACOUSTICS-WEB is an online tool written in JavaScript and PHP that enables the estimation of auditory sensory thresholds via adaptive threshold tracking. The toolbox implements the transformed up-down methods proposed by Levitt (Journal of the Acoustical Society of America, 49, 467-477, (1971) for a set of classic psychoacoustical tasks: frequency, intensity, and duration discrimination of pure tones; duration discrimination and gap detection of noise; and amplitude modulation detection with noise carriers. The toolbox can be used through a common web browser; it works with both fixed and mobile devices, and requires no programming skills. PSYCHOACOUSTICS-WEB is suitable for laboratory, classroom, and online testing and is designed for two main types of users: an occasional user and, above all, an experimenter using the toolbox for their own research. This latter user can create a personal account, customise existing experiments, and share them in the form of direct links to further users (e.g., the participants of a hypothetical experiment). Finally, because data storage is centralised, the toolbox offers the potential for creating a database of auditory skills.

Surface facial electromyography (EMG) is commonly used to detect emotions from subtle facial expressions. Although there are established procedures for collecting EMG data and some aspects of their processing, there is little agreement among researchers about the optimal way to process the EMG signal, so that the study-unrelated variability (noise) is removed, and the emotion-related variability is best detected. The aim of the current paper was to establish an optimal processing pipeline for EMG data for identifying emotional expressions in facial muscles. We identified the most common processing steps from existing literature and created 72 processing pipelines that represented all the different processing choices. We applied these pipelines to a previously published dataset from a facial mimicry experiment, where 100 adult participants observed happy and sad facial expressions, whilst the activity of their facial muscles, zygomaticus major and corrugator supercilii, was recorded with EMG. We used a resampling approach and subsets of the original data to investigate the effect and robustness of different processing choices on the performance of a logistic regression model that predicted the mimicked emotion (happy/sad) from the EMG signal. In addition, we used a random forest model to identify the most important processing steps for the sensitivity of the logistic regression model. Three processing steps were found to be most impactful: baseline correction, standardisation within muscles, and standardisation within subjects. The chosen feature of interest and the signal averaging had little influence on the sensitivity to the effect. We recommend an optimal processing pipeline, share our code and data, and provide a step-by-step walkthrough for researchers.

Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions - dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception.

Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal, 28, 1-14, (2021a, 2021b) proposed a variant of the Wald test that uses Markov chain Monte Carlo machinery to generate a chi-square test statistic for frequentist inference. Because the test's composition does not rely on analytic expressions for sampling variation and covariation, it potentially provides a way to get honest significance tests in cases where the likelihood-based test statistic's assumptions break down (e.g., in small samples). The goal of this study is to use simulation to compare the new MCM Wald test to its maximum likelihood counterparts, with respect to both their type I error rate and power. Our simulation examined the test statistics across different levels of sample size, effect size, and degrees of freedom (test complexity). An additional goal was to assess the robustness of the MCMC Wald test with nonnormal data. The simulation results uniformly demonstrated that the MCMC Wald test was superior to the maximum likelihood test statistic, especially with small samples (e.g., sample sizes less than 150) and complex models (e.g., models with five or more predictors). This conclusion held for nonnormal data as well. Lastly, we provide a brief application to a real data example.

Magnetic resonance imaging (MRI) is a non-invasive technique that requires the participant to be completely motionless. To date, MRI in awake and unrestrained animals has only been achieved with humans and dogs. For other species, alternative techniques such as anesthesia, restraint and/or sedation have been necessary. Anatomical and functional MRI studies with sheep have only been conducted under general anesthesia. This ensures the absence of movement and allows relatively long MRI experiments but it removes the non-invasive nature of the MRI technique (i.e., IV injections, intubation). Anesthesia can also be detrimental to health, disrupt neurovascular coupling, and does not permit the study of higher-level cognition. Here, we present a proof-of-concept that sheep can be trained to perform a series of tasks, enabling them to voluntarily participate in MRI sessions without anesthesia or restraint. We describe a step-by-step training protocol based on positive reinforcement (food and praise) that could be used as a basis for future neuroimaging research in sheep. This protocol details the two successive phases required for sheep to successfully achieve MRI acquisitions of their brain. By providing structural brain MRI images from six out of ten sheep, we demonstrate the feasibility of our training protocol. This innovative training protocol paves the way for the possibility of conducting animal welfare-friendly functional MRI studies with sheep to investigate ovine cognition.

The Diagnostic Statistical Manual of Mental Disorders (DSM-5) recommends diagnosing neurocognitive disorders (i.e., cognitive impairment) when a patient scores beyond - 1 SD below neurotypical norms on two tests. I review how this approach will fail due to cognitive tests' power limitations, validity issues, imperfect reliabilities, and biases, before summarizing their resulting negative consequences. As a proof of concept, I use developmental prosopagnosia, a condition characterized by difficulties recognizing faces, to show the DSM-5 only diagnoses 62-70% (n1 = 61, n2 = 165) versus 100% (n1 = 61) through symptoms alone. Pooling the DSM-5 missed cases confirmed the presence of group-level impairments on objective tests, which were further evidenced through meta-analyses, thus validating their highly atypical symptoms. These findings support a paradigm shift towards bespoke diagnostic approaches for distinct cognitive impairments, including a symptom-based method when validated effective. I reject dogmatic adherence to the DSM-5 approach to neurocognitive disorders, and underscore the importance of a data driven, transdiagnostic approach to understanding patients' subjective cognitive impairments. This will ultimately benefit patients, their families, clinicians, and scientific progress.

Faking in self-report personality questionnaires describes a deliberate response distortion aimed at presenting oneself in an overly favorable manner. Unless the influence of faking on item responses is taken into account, faking can harm multiple psychometric properties of a test. In the present article, we account for faking using an extension of the multidimensional nominal response model (MNRM), which is an item response theory (IRT) model that offers a flexible framework for modeling different kinds of response biases. Particularly, we investigated under which circumstances the MNRM can adequately adjust substantive trait scores and latent correlations for the influence of faking and examined the role of variation in the way item content is related to social desirability (i.e., item desirability characteristics) in facilitating the modeling of faking and counteracting its detrimental effects. Using a simulation, we found that the inclusion of a faking dimension in the model can overall improve the recovery of substantive trait person parameters and latent correlations between substantive traits, especially when the impact of faking in the data is high. Item desirability characteristics moderated the effect of modeling faking and were themselves associated with different levels of parameter recovery. In an empirical demonstration with N = 1070 test-takers, we also showed that the faking modeling approach in combination with different item desirability characteristics can prove successful in empirical questionnaire data. We end the article with a discussion of implications for psychological assessment.

The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.

Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on derived metrics like adult word count (AWC) and child vocalization counts (CVC), which have also more recently been derived using an open-source alternative, the ACLEW pipeline. Yet, there is relatively little work assessing the reliability of long-form metrics in terms of the stability of individual differences across time. Filling this gap, we analyzed eight spoken-language datasets: four from North American English-learning infants, and one each from British English-, French-, American English-/Spanish-, and Quechua-/Spanish-learning infants. The audio data were analyzed using two types of processing software: LENA and the ACLEW open-source pipeline. When all corpora were included, we found relatively low to moderate reliability (across multiple recordings, intraclass correlation coefficient attributed to the child identity [Child ICC], was < 50% for most metrics). There were few differences between the two pipelines. Exploratory analyses suggested some differences as a function of child age and corpora. These findings suggest that, while reliability is likely sufficient for various group-level analyses, caution is needed when using either LENA or ACLEW tools to study individual variation. We also encourage improvement of extant tools, specifically targeting accurate measurement of individual variation.

Face processing is a central component of human communication and social engagement. The present investigation introduces a set of racially and ethnically inclusive faces created for researchers interested in perceptual and socio-cognitive processes linked to human faces. The Diverse Face Images (DFI) stimulus set includes high-quality still images of female faces that are racially and ethnically representative, include multiple images of direct and indirect gaze for each model and control for low-level perceptual variance between images. The DFI stimuli will support researchers interested in studying face processing throughout the lifespan as well as other questions that require a diversity of faces or gazes. This report includes a detailed description of stimuli development and norming data for each model. Adults completed a questionnaire rating each image in the DFI stimuli set on three major qualities relevant to face processing: (1) strength of race/ethnicity group associations, (2) strength of eye gaze orientation, and (3) strength of emotion expression. These validation data highlight the presence of rater variability within and between individual model images as well as within and between race and ethnicity groups.