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.

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.

Eye-tracking has gained considerable attention across multiple research domains. Recently, web-based eye-tracking has become feasible, demonstrating reliable performance in perceptual and cognitive tasks. However, its systematic evaluation in decision-making remains unknown. Here we compare a laboratory-based eye tracker (the EyeLink 1000 Plus) with a webcam-based method (WebGazer) across two discrete-choice experiments. We systematically manipulated display size to approximate common device classes (monitor, laptop, tablet, mobile) and task complexity (simple vs. complex choice matrices). We find that on larger displays and simpler tasks, WebGazer produces gaze patterns and parameter inferences from computational models of behavior comparable to EyeLink. However, reliability diminishes on smaller displays and with more complex choice matrices. These results provide the first systematic evaluation of web-based eye tracking for decision-making research and offer practical guidance regarding its viability for online behavioral studies.

Natural language is a primary medium for expressing thoughts and emotions, making text analysis a vital tool in psychological research. It enables insights into personality traits, mental health, and sentiment in interpersonal communication. Traditional approaches - such as human coding, dictionary-based methods, or training models from scratch - often suffer from limitations, including inefficiency, incomplete coverage, or high data requirements. This tutorial introduces the pretrain-finetune paradigm, a transformative approach in text analysis and natural language processing (NLP) that leverages large pretrained language models. Unlike conventional methods, this paradigm enables efficient fine-tuning even with limited labeled data, making it particularly valuable for social science research where annotated samples are scarce. Our tutorial offers a comprehensive introduction to the pretrain-finetune framework, beginning with core concepts of pretraining and fine-tuning, followed by hands-on exercises with real-world applications, the introduction of finetuneR, an R package developed to make these methods accessible to R users, and concluding with a discussion of common misconceptions in existing resources and best practices. We demonstrate its effectiveness across diverse tasks, including multi-class classification and regression, showing its advantages over traditional methods, feature extraction-based approaches, and GPT-based strategies. By emphasizing its efficiency, accessibility, and superior performance, this tutorial aims to encourage broader adoption of the pretrain-finetune paradigm in psychological and behavioral research.

Flanker tasks are among the most popular paradigms for studying conflict resolution, particularly the intuitive "arrows" flanker task. To date, there is no auditory equivalent of this task, potentially due to the risk of perceptual masking. Using the strategy of reducing masking through vocal differences between two competing talkers, the present study explores the validity of a speech-based flanker task with co-located talkers. One hundred and forty-two healthy young participants (aged 18-24) participated in three auditory flanker tasks using naturally-produced speech with increasing spectral separation. Stimuli were the command words 'left' and 'right,' as well as two neutral flankers designed to impose masking effects, one containing only acoustic-phonetic information, and another containing lexical information. On each trial, the target and flanker words were heard concurrently, with participants responding to the direction spoken by the target talker. Classic conflict effects were observed to the incongruent flanker, which could not be attributed to masking. Both masking and conflict effects were somewhat reduced by spectral separation. A final experiment with 62 healthy adults (aged 18-59) compared the test-retest reliability and distributional properties of this auditory directional command flanker task with the visual arrows flanker task. The auditory task demonstrated similar test-retest reliability and evolution of conflict effects over time to the visual task. This pattern of results suggests that the directional command flanker task is comparable to the arrow flanker task and can be utilized to quantify inhibitory control in the auditory modality.

Face stimuli used in face perception research often focus on between-model variability, underrepresenting within-model variability across conditions and time. However, exposure to within-model variability is crucial for developing stable representations of faces. Here, we introduce the Chinese Face and Body Dataset (CFBD), a publicly accessible resource that captures within-model variability to represent a broad spectrum of appearance and image variations in laboratory and natural settings. The CFBD comprises 2,195 images from 117 models, including both laboratory photos taken by researchers and personal photos donated by models. Each model is depicted in 10 to 31 photos, coded for attributes such as the time photos were taken, facial expressions, viewing angles, and environmental contexts. Independent participants also rated these photos based on facial attractiveness, trustworthiness, and distinctiveness. The results revealed that the CFBD captures a wide range of variations across appearances and image attributes, and the within-model variances in trait ratings are comparable to, if not greater than, the between-model variances. Moreover, within-model variances in the trait ratings differ by image type, with personal photos being rated as more attractive, distinctive, and trustworthy than their laboratory counterparts. By capturing a diverse range of appearances and images of Chinese individuals, the CFBD provides valuable resources that expand face datasets, potentially advancing our understanding of robust face representation.

Multi-group growth curve models with variant factor structure across classes can be used as the basis for growth mixture models. Such models identify qualitatively different patterns of growth/decline within class. Initial assessment of the dimensionality and patterning of growth factor loadings, prior to determining the functional form of growth, prevents the identification of artifactual latent classes as well as the use of overly complex or overly simple models. Simulated data sets illustrate the estimation of such loading variant mixture models and the comparison of candidate models. The ability of a variety of fit indices to correctly identify the correct model is explored under several sample size conditions. Analysis of a real-world data set is considered in which a two-factor growth model provides both superior fit and conceptually different classes than the "cat's cradle" pattern identified by linear growth or quadratic growth mixture models.

While conventional behavioural tests offer valuable insight into rodent behaviour in specific paradigms, rodents spend most of their time in a safe, undisturbed environment-their home cage. However, home cage monitoring (HCM) is often impractical at scale over prolonged periods of time without significant loss of data. To achieve this, we developed MIROSLAV, the Multicage InfraRed Open-Source Locomotor Activity eValuator, a home cage activity and environmental monitoring device designed to be open, adaptable, and robust. Its transparent and modular design allows the device to be tailored to varying experimental requirements and environmental conditions, while its wireless operation and multiple redundancies minimise loss of data and animal disturbance. Data quality is maintained by a modular software workflow for data preparation (Prepare-a-SLAV) and cleaning (TidySLAV), followed by exploratory (MIROSine-MIRO The Explorer) and statistical (MIROSine-StatistiSLAV) analysis of circadian periodicity. Here, using MIROSLAV, we demonstrate circadian dysrhythmia and a disrupted response to regular stimuli (e.g. behavioural testing, bedding change) in a rat model of sporadic Alzheimer's disease, showcasing MIROSLAV's utility in a typical animal study of disease. In accordance with the 3Rs (replacement, reduction, and refinement), every rodent study and laboratory performing them can benefit from HCM, provided the existence of transparent and customisable tools which allow robust deployment to tens or hundreds of cages in various conditions. MIROSLAV provides this opportunity to researchers in a cost-effective manner.

Crowdsourced normative ratings have benefited psycholinguistic research considerably. Yet, Modern Standard Arabic (MSA) has scarce word norms. To address this scarcity, the current study developed and validated the Kalimah norms in two experiments. In experiment 1, 803 native Arabic speakers provided age of acquisition (AOA) and concreteness (CNC) ratings for 2,467 MSA words, a subset of which was lexically ambiguous (N = 60). Correlations with 12 Arabic and international norms established the sufficient validity of the Kalimah norms. We also observed variations in the ratings for the distinct meanings of the same word form. In experiment 2, we show that our crowdsourced norms can be used to validate large language model(LLM)-generated norm ratings for MSA words. Based on this, we obtained LLM-generated CNC ratings for an additional set of 30,000 MSA words. We make both the Kalimah norms and LLM-generated CNC ratings freely available for research purposes.