Behavior Research Methods最新文献

We present PupEyes, an open-source Python package for preprocessing and visualizing pupil size and fixation data. PupEyes supports data collected from EyeLink and Tobii eye-trackers as well as any generic dataset that conforms to minimal formatting standards. Developed with current best practices, PupEyes provides a comprehensive pupil preprocessing pipeline and interactive tools for data exploration and diagnosis. In addition to pupil size data, PupEyes provides interactive tools for visualizing fixation data, drawing areas of interest (AOIs), and computing AOI-based metrics. PupEyes uses the pandas data structure and can work seamlessly with other data analysis packages within the Python ecosystem. Overall, PupEyes (1) ensures that pupil size data are preprocessed in a principled, transparent, and reproducible manner, (2) helps researchers better understand their data through interactive visualizations, and (3) enables flexible extensions for further analysis tailored to specific research goals. To ensure computational reproducibility, we provide detailed, executable tutorials ( https://pupeyes.readthedocs.io/ ) that allow users to reproduce and modify the code examples in a virtual environment.

Assessing the effectiveness of behavioral interventions in rare diseases is challenging due to extremely limited sample sizes and ethical challenges with withholding intervention when limited treatment options are available. The multiple baseline design (MBD) is commonly used in behavioral science to assess interventions, while allowing all individuals to receive the intervention. MBD is primarily used to evaluate a single intervention so an alternative strategy is needed when evaluating more than one intervention. In this case, a factorial design may be recommended, but a standard factorial design may not be feasible in rare diseases due to extremely limited sample sizes. To address this challenge, we propose the individually randomized multiple baseline factorial design (MBFD), which requires fewer participants but can attain sufficient statistical power for evaluating at least two interventions and their combinations. Furthermore, by incorporating randomization, we enhance the internal validity of the design. This study describes the design characteristics of a standard MBFD, clarifies estimands, and introduces three statistical models under different assumptions. Through simulations, we analyze data from MBFD using linear mixed effect models (LMM) and generalized estimating equations (GEE) to compare biases, sizes, and power of detecting the main effects from the models. We recommend using GEE to mitigate potential random effect misspecifications and suggest small sample corrections, such as Mancl and DeRouen variance estimator, for sample sizes below 120.

Familiarity, aptness, concreteness, metaphoricity, and structural norms for 300 two-word English metaphorical expressions (e.g., broken heart, early bird), presented in sentence context and in isolation, were obtained from 164 participants. Familiarity was conceived as the extent to which participants had previously heard or read that expression. Aptness was conceived as the extent to which the vehicle captured important features of the topic. Concreteness was conceived as the extent to which the meaning conveyed by the vehicle could be perceived through the senses or actions. Metaphoricity was conceived as the extent to which the expression was perceived as figuratively rather than literally true. Metaphor constituent structure was conceived as a graded measure indicating whether the metaphorical content is carried by the first word, the second word, or distributed across both words. In addition to these variables, which are known to play a key role in metaphor comprehension, we provide frequency scores for the whole expression as well as for each constituent separately from the Corpus of Contemporary American English (COCA) database. Cumulative link mixed-effects models were used to examine the effects of context and vehicle position on participants' ratings, and to assess whether familiarity, aptness, and concreteness predicted perceived metaphoricity. This set of norms, the first of its kind, serves as a resource for research employing a variety of computational, behavioral, and neuroimaging methods to examine the nature of metaphor comprehension and semantic composition.

Time-series data have become ubiquitous in psychological research, allowing us to study detailed within-person dynamics and their heterogeneity across persons. Vector autoregressive (VAR) models have become a popular choice as a first approximation of these dynamics. The VAR model for each person and heterogeneity across persons can be jointly modeled using a hierarchical model that treats heterogeneity as a latent distribution. Currently, the most popular choice for this is the multilevel VAR model, which models heterogeneity across persons as quantitative variation through a multivariate Gaussian distribution. Here, we discuss an alternative, the latent class VAR model, which models heterogeneity as qualitative variation using a number of discrete clusters. While this model has been introduced before, it has not been readily accessible to researchers. Here we address this issue by providing an accessible introduction to latent class VAR models; a simulation evaluating how well this model can be estimated in situations resembling applied research; introducing a new R package ClusterVAR, which provides easy-to-use functions to estimate the model; and providing a fully reproducible tutorial on modeling emotion dynamics, which walks the reader through all steps of estimating, analyzing, and interpreting latent class VAR models.

Eye tracking in reading research requires high spatial accuracy due to small, densely arranged areas of interest. However, despite widespread use of pupil-based eye trackers in reading studies, a major source of systematic inaccuracy remains largely unaddressed: the pupil size artifact (PSA) - apparent gaze shift in fact caused by pupil dilation even when the eye remains stationary. Using pupillary light reflex manipulations and reading tasks under controlled luminance, we quantified gaze inaccuracy due to the PSA and compared correction methods. We observed systematic apparent gaze shift of up to 2 $_{}^{\circ }$ as pupil sizes varied from 2 to 6 mm. Horizontal PSA showed contralateral patterns (median slopes: 0.38 $_{}^{\circ }$ /mm), while vertical PSA increased with pupil size (up to 0.86 $_{}^{\circ }$ /mm for larger pupils). Even under constant luminance, pupil size varied substantially during reading (median 95% ranges 0.78-1.38 mm). We compared two correction approaches: line assignment (standard in reading research) and PSA recalibration (modeling pupil-size-induced apparent gaze shift across the screen). Both methods effectively corrected vertical apparent gaze shift, showing similar average correction offsets and sensitivity of correction to pupil size, suggesting that line assignment implicitly compensates for vertical PSA effects. However, only PSA recalibration addressed horizontal apparent gaze shift, reducing overall gaze shift by over 50% and improving the performance of line assignment algorithms. Our findings underscore the importance of accounting for PSA in reading research. We offer practical recommendations for improving gaze accuracy in eye-tracking reading studies.

Eye-tracking corpora have advanced our understanding of reading processes by providing large-scale datasets of naturalistic reading behavior. However, existing corpora have almost exclusively sampled from typically hearing readers of spoken languages. Here, we present the Signers' Eye-movements in English Reading (SEER) Corpus, a dataset of eye-movement behaviors from 41 skilled deaf adult readers who are early signers of American Sign Language (ASL), as well as a comparative group of 101 typically hearing monolingual English readers. Participants read 200 English sentences presented one at a time. In addition to eye-tracking data, the corpus includes detailed participant information: a standardized measure of reading proficiency, spelling recognition, and nonverbal intelligence for all participants. Information for the deaf participants include ASL comprehension scores, age of ASL acquisition, and phonological awareness scores (for a subset of participants). We report comparative analyses of reading behaviors at both the word level and sentence level. We also examine group differences in the effects of word length, frequency, and surprisal on local measures. The results indicate stronger effects of length and surprisal, but equivalent frequency effects (on content words) for deaf compared to hearing readers. The SEER Corpus offers researchers the opportunity to test hypotheses about reading development and efficiency in bimodal bilinguals who are first language users of ASL and skilled readers of English, supporting broader investigations of visual language processing. The corpus is preregistered and publicly available ( https://doi.org/10.17605/OSF.IO/7P4F2 ) to facilitate replication, cross-study comparisons, and exploration of preliminary hypotheses in this understudied population.

Naturalistic scenes are of key interest for visual perception, but controlling their perceptual and semantic properties is challenging. Previous work on naturalistic scenes has frequently focused on collections of discrete images with considerable physical differences between stimuli. However, it is often desirable to assess representations of naturalistic images that vary along a continuum. Traditionally, perceptually continuous variations of naturalistic stimuli have been obtained by morphing a source image into a target image. This produces transitions driven mainly by low-level physical features and can result in semantically ambiguous outcomes. More recently, generative adversarial networks (GANs) have been used to generate continuous perceptual variations within a stimulus category. Here, we extend and generalize this approach using a different machine learning approach, a text-to-image diffusion model (Stable Diffusion XL), to generate a freely customizable stimulus set of photorealistic images that are characterized by gradual transitions, with each image representing a unique exemplar within a prompted category. We demonstrate the approach by generating a set of 108 object scenes from six categories. For each object scene, we generate ten variants that are ordered along a perceptual continuum. This ordering was first estimated using a machine learning model of perceptual similarity (LPIPS) and then subsequently validated with a large online sample of human participants. In a subsequent experiment, we show that this ordering is also predictive of stimulus confusability in a working memory task. Our image set is suited for studies investigating the graded encoding of naturalistic stimuli in visual perception, attention, and memory.

Researchers often require validated and well-rounded sets of image stimuli. For those interested in understanding the various visual attentional biases toward threatening stimuli, a dataset containing a variety of such objects is urgently needed. Here, our goal was to create an image database of animate and inanimate objects, including those that people find threatening and those that are visually similar to them but are not considered threatening. To do this, we recruited participants (N = 77) for an online survey in which they were asked to name threatening objects and try to come up with a visually similar counterpart. We then used the survey results to create a list of 32 objects, including eight from each crossing of threatening versus nonthreatening and animate versus inanimate. We obtained 20 exemplar images from each category (640 unique images in total, all copyright-free and openly shared). An independent sample of participants (N = 191) judged the similarity of these images using the spatial arrangement method. Data were then modeled using multidimensional scaling. Our results present modeling outcomes using a "map" of animate and inanimate objects (separately) that spatially conveys the perceived similarity relationships between them. We expect that this image set will be widely used in future visual attention studies and more.

Ceiling or floor effects pose analytic challenges in behavioral and psychological research. In this study, we developed novel Tobit modeling approaches, estimated using maximum likelihood (ML) or Bayesian methods, to address these effects for widely used statistical analyses, including the dependent-sample t-test and moderated regression. Simulation studies were conducted to compare the performance of the proposed modeling approaches to the conventional approach where ceiling or floor data are treated as if true values. The conventional approach was found to yield biased estimates, inflated Type I error rates, and poor confidence interval coverage, even with as little as 10% ceiling data. In contrast, the proposed approaches with either ML or Bayesian estimation provided accurate estimates and inference results across most studied conditions (e.g., with 30% ceiling data). Real data examples further illustrated the impact of modeling choices. To facilitate implementations of the proposed Tobit modeling approaches, we provide simulated datasets along with R and Mplus scripts online. Implications of the findings and future research directions were discussed.

When we encounter words, we activate not only the social information provided by the speaker, but also the rich semantics of the words' meaning, and quantifying this information is a key challenge for the cognitive and behavioural sciences. Although there are many resources available that quantify affective and sensorimotor information, there are relatively few resources available that provide information on social dimensions of meaning. We present the SocioLex-CZ norms, where the primary focus is on socio-semantic dimensions of meaning. Across two experiments, we introduce normative estimates along five dimensions-gender, political alignment, location, valence and age-for a large set of Czech words (Experiment 1) and images (Experiment 2) from 1,709 participants. We provide a series of analyses demonstrating that the norms have good reliability, and present exploratory analyses examining how the variables interact with one another within and between words/images. These norms present a valuable dataset that quantifies socio-semantic representations at scale, which we hope will be used for a range of novel and multidisciplinary applications, thereby opening up new pathways for innovative research. We make the data, code and analysis available at https://osf.io/pv9md/ and also provide an interactive web app at https://tinyurl.com/sociolex-cz-app .