Behavior Research Methods最新文献

In this study, we present age-of-acquisition (AoA) ratings for 885 Kannada and Filipino words as a new resource for research and education purposes. Beyond this, we consider the methodological and theoretical considerations of measuring AoA in morphologically rich, specifically agglutinative, languages, to study child language acquisition. Parents, teachers, and experts provided subjective ratings of when they thought a child acquired each word. Results were generally consistent between the two languages. Mixed-effects models demonstrated that word characteristics, including parts-of-speech category, word length, and age band of first occurrence in a print corpus, were significantly related to AoA ratings, whereas rater characteristics, including participant type, age, gender, and number of languages spoken, had generally non-significant associations with AoA ratings. The number of morphemes was significantly associated with AoA ratings in some analyses; however, crosslinguistic differences in the directionality of the relationships suggested the need to investigate underlying drivers of morphological complexity such as morpheme frequency, transparency/consistency, and function. The age-of-acquisition ratings were internally reliable and demonstrated consistency with the first occurrences of words in print and known trends in child language research. The results demonstrate the potential of these resources and open new directions for AoA research in morphologically rich languages.

Estimating quantitative cognitive models from data is a staple of modern psychological science, but can be difficult and inefficient. Particle Metropolis within Gibbs (PMwG) is a robust and efficient sampling algorithm that supports model estimation in a hierarchical Bayesian framework. This tutorial shows how cognitive modeling can proceed efficiently using pmwg, a new open-source package for the R language. We step through implementing the pmwg package with simple signal detection theory models, to more complex cognitive models in which two tasks are jointly modeled together. Through this process, we also address questions of model adequacy and model selection, which must be solved in order to answer meaningful psychological questions. PMwG, and the pmwg package, has the potential to move the field of psychology ahead in new and interesting directions, and to resolve questions that were once too hard to answer with previously available sampling methods.

Data from rating scales underlie very specific restrictions: They have a lower limit, an upper limit, and they only consist of a few integers. These characteristics produce particular dependencies between means and standard deviations. A mean that is a non-integer, for example, can never be associated with zero variability, while a mean equal to one of the scale's limits can only be associated with zero variability. The relationship can be described by umbrella plots for which we present a formalization. We use that formalization to explore implications for statistical power and for the relationship between heterogeneity in unstandardized and standardized effect sizes. The analysis illustrates that power is not only affected by the mean difference and sample size, but also by the position of a mean within the respective scale. Further, the umbrella restrictions of rating scales can impede interpretability of meta-analytic heterogeneity. Estimations of relative heterogeneity can diverge between unstandardized and standardized effects, raising questions about which of the two patterns of heterogeneity we would want to explain (for example, through moderators). We reanalyze data from the Many Labs projects to illustrate the issue and finally discuss the implications of our observations as well as ways to utilize these properties of rating scales. To facilitate in-depth exploration and practical application of our formalization, we developed the Shiny Umbrellas app, which is publicly available at https://www.apps.meta-rep.lmu.de/shiny_umbrellas/ .

This study explores expert-novice differences in anticipation under uncertainty by combining partially observable Markov decision process (POMDP) modeling with machine learning classification. Forty-eight participants (24 experts, 24 novices) completed a basketball pass/shot anticipation task. Through POMDP modeling, two cognitive parameters-sensory precision (SP) and prior belief (pB)-were extracted to capture internal decision processes. Results showed that experts fit the POMDP model more closely, requiring more iterations for parameter convergence and achieving higher pseudo R² values than novices. Experts demonstrated significantly higher SP, indicating superior ability to filter key cues under noisy conditions. Their pB values remained closer to neutral, suggesting flexible reliance on prior knowledge. In contrast, novices exhibited more biased priors and a lower, more dispersed SP. Machine learning analyses revealed that SP and pB jointly formed distinct clusters for experts and novices in a two-dimensional parameter space, with classification accuracies exceeding 90% across multiple methods. These findings indicate that expertise entails both enhanced perceptual precision and adaptive prior calibration, reflecting deeper cognitive reorganization rather than simple skill increments. Our dual-parameter approach offers a model-based perspective on expert cognition and may inform future research on the multifaceted nature of expertise.

The measurement of pupil size has become a topic of interest in psychology research over the past two decades due to its sensitivity to psychological processes such as arousal or cognitive load. However, pupil measurements have been limited by the necessity to conduct experiments in laboratory settings using high-quality and costly equipment. The current article describes the development and use of a jsPsych plugin and extension that incorporates an existing software that estimates pupil size using consumer-grade hardware, such as a webcam. We validated this new program (js-mEye) across two separate studies, which each manipulated screen luminance and color using a novel luminance task, as well as different levels of cognitive load using the N-back and the Stroop tasks. Changes in luminance and color produced significant changes in pupil size in the hypothesized direction. Changes in cognitive load induced in the N-back and Stroop tasks produced less clear findings; however, these findings were explained to some extent when participant engagement - indexed by task performance - was controlled for. Most importantly, all data were at least moderately correlated with data simultaneously recorded using an EyeLink 1000, suggesting that mEye was able to effectively substitute for a gold-standard eye-tracking device. This work presents an exciting future direction for pupillometry and, with further validation, may present a platform for measuring pupil size in online research studies, as well as in laboratory-based experiments that require minimal equipment.

Animal research has shown that repeatedly performing a rewarded action leads to its transition into a habit-an inflexible response controlled by stimulus-response associations. Efforts to reproduce this principle in humans have yielded mixed results. Only two laboratory paradigms have demonstrated behavior habitualization following extensive instrumental training compared to minimal training: the forced-response task and the "aliens" outcome-devaluation task. These paradigms assess habitualization through distinct measures. The forced-response task focuses on the persistence of a trained response when a reversal is required, whereas the outcome-devaluation task measures reaction time switch costs-slowdowns in goal-directed responses conflicting with the trained habit. Although both measures have produced results consistent with the learning theory-showing stronger evidence of habits in overtrained conditions-their construct validity remains insufficiently established. In this study, participants completed 4 days of training in each paradigm. We replicated previous results in the forced-response task; in the outcome-devaluation task, a similar pattern emerged, observing the loss of a response speed advantage gained through training. We then examined the reliability of each measure and evaluated their convergent validity. Habitual responses in the forced-response task and reaction time switch costs in the outcome-devaluation task demonstrated good reliability, allowing us to assess whether individual differences remained stable. However, the two measures were not associated, providing no evidence of convergent validity. This suggests that these measures capture distinct aspects of the balance between habitual and goal-directed control. Our results highlight the need for further evaluation of the validity and reliability of current measures of habitual control in humans.

Visual attention plays a critical role when our visual system executes active visual tasks by interacting with the physical scene. However, how to encode visual object relationships in the psychological world of the brain deserves exploration. Predicting visual fixations or scanpaths is a usual way to explore the visual attention and behaviors of human observers when viewing a scene. Most existing methods encode visual attention using individual fixations or scanpaths derived from raw gaze-shift data collected from human observers. This may not capture the common attention pattern well, because without considering the semantic information of the viewed scene, raw gaze shift data alone contain high inter- and intra-observer variability. To address this issue, we propose a new attention representation, called visual attention graph (VAG), to simultaneously code the visual saliency and scanpath in a graph-based representation and better reveal the common attention behavior of human observers. In the visual attention graph, the semantic-based scanpath is defined by the path on the graph, while the saliency of objects can be obtained by computing fixation density on each node. Systemic experiments demonstrate that the proposed attention graph combined with our new evaluation metrics provides a better benchmark for evaluating attention prediction methods. Meanwhile, extra experiments demonstrate the promising potential of the proposed attention graph in assessing human cognitive states, such as autism spectrum disorder screening and age classification.

Epistemic emotions, and in particular curiosity, seem to enhance memory for both the specific information that stimulates the individual's curiosity and information presented in close temporal proximity. Most studies on memory and curiosity have adopted trivia questions to elicit curiosity. However, the amount and range of interest that trivia questions elicit are unclear, and there is no established, universal trivia item pool guaranteed to elicit comparable levels of curiosity across individuals of all ages. Thus, one substantial challenge when studying curiosity is systematically inducing it in controlled experimental settings. Recently, an innovative database called Magic Curiosity Arousing Tricks (MagicCATs) has been published. This database includes 166 short magic-trick video clips that adopt different materials and is designed to induce curiosity, surprise, and interest. Here, we aimed to validate this dataset in the Italian population by reporting the basic characteristics and the norms of the magic-trick video clips in younger and middle-aged adults. We also carried out association rule learning, a rule-based machine learning and data mining method to aid understanding of the co-occurrences between the different epistemic emotions and aid researchers in stimulus selection. Association rules underline relationships or associations between the variables in our datasets and can be used in association with descriptive statistics for stimulus selection in psychological experiments.

Video recordings are commonplace for observing human and animal behaviours, including interindividual interactions. In studies of humans, analyses for clinical applications remain particularly cumbersome, requiring human-based annotation that is time-consuming, bias-prone, and cost-ineffective. Attempts to use machine learning to address these limitations still oftentimes require highly standardised environments, scripted scenarios, and forward-facing individuals. Here, we provide the ADVANCE toolkit, an automated video annotation pipeline. The versatility of ADVANCE is demonstrated with schoolchildren and adults in an unscripted clinical setting within an art classroom environment that included 2-5 individuals, dynamic occlusions, and large variations in actions. We accurately detected each individual, tracked them simultaneously throughout the duration of the recording (including when an individual left and re-entered the field of view), estimated the position of their skeletal joints, and labelled their poses. By resolving challenges of manual annotation, we radically enhance the ability to extract information from video recordings across different scenarios and settings. This toolkit reduces clinical workload and enhances the ethological validity of video-based assessments, offering scalable solutions for behaviour analyses in naturalistic contexts.