The Potsdam Textbook Corpus (PoTeC) is a naturalistic eye-tracking-while-reading corpus containing data from 75 participants reading 12 scientific texts. PoTeC is the first naturalistic eye-tracking-while-reading corpus that contains eye-movements from domain experts as well as novices in a within-participant manipulation: It is based on a 2×2×2 fully crossed factorial design, which includes the participants’ level of studies and the participants’ discipline of studies as between-subjects factors and the text domain as a within-subjects factor. The participants’ reading comprehension was assessed by a series of text comprehension questions and their domain knowledge was tested by text-independent background questions for each of the texts. The materials are annotated for a variety of linguistic features at different levels. We envision PoTeC to be used for a wide range of studies including but not limited to analyses of expert and non-expert reading strategies. The corpus and all the accompanying data at all stages of the preprocessing pipeline and all code used to preprocess the data is made available via GitHub: https://github.com/DiLi-Lab/PoTeC and OSF: https://osf.io/dn5hp/. The data is furthermore integrated into the open-source package pymovements, which can be used in Python and R: https://github.com/aeye-lab/pymovements.

To examine how individual differences in language skills affect language processing, it is essential to have good-quality tests that can assess such individual differences accurately. This study introduces a dictation test and a vocabulary knowledge test in Chinese, which aim to measure lexical expertise in proficient Chinese language users like university students. The psychometric properties of the two tests were examined with two groups of participants. In the first group, exploratory factor analyses confirmed that each of these tests was unidimensional, measuring a single underlying construct of lexical expertise. After removing some problematic items, the two tests also demonstrated satisfactory internal reliabilities. Although the test scores were only weakly correlated with self-reported measures of language proficiency, the correlation with word recognition performance was moderate. These results were successfully replicated with the second cross-validation group, confirming the reliability and convergent validity of the tests. An additional dataset further showed that the vocabulary test score was positively correlated with sentence comprehension performance. Taken together, the tests have acceptable psychometric quality and can serve as tools for examining individual differences in Chinese language processing. The tests are freely available online, and normative performance data are provided, facilitating their use in future research.

The Eye movements on Machine-generated Texts Corpus (EMTeC) is a naturalistic eye-movements-while-reading corpus of 107 native English speakers reading machine-generated texts. The texts are generated by three large language models using five different decoding strategies, and they fall into six different text-type categories. EMTeC entails the eye movement data at all stages of pre-processing, i.e., the raw coordinate data sampled at 2000 Hz, the fixation sequences, and the reading measures. It further provides both the original and a corrected version of the fixation sequences, accounting for vertical calibration drift. Moreover, the corpus includes the language models’ internals that underlie the generation of the stimulus texts: the transition scores, the attention scores, and the hidden states. The stimuli are annotated for a range of linguistic features both at text and at word level. We anticipate EMTeC to be utilized for a variety of use cases such as, but not restricted to, the investigation of reading behavior on machine-generated text and the impact of different decoding strategies; reading behavior on different text types; the development of new pre-processing, data filtering, and drift correction algorithms; the cognitive interpretability and enhancement of language models; and the assessment of the predictive power of surprisal and entropy for human reading times. The data at all stages of pre-processing, the model internals, and the code to reproduce the stimulus generation, data pre-processing, and analyses can be accessed via https://github.com/DiLi-Lab/EMTeC/.

Traditional average-based metrics have long been considered the gold standard in behavioral and brain research. However, recent advances emphasize the importance of examining the dispersion around the mean to uncover the nuances of individual differences and challenge simplistic assumptions. Thus, the study of variability is becoming increasingly central across a wide range of domains. Here, we tackle the composite architecture of motor variability by proposing a new geometric method to model it. Three independent gait datasets are used to: i) develop the method (Dataset 1), ii) evaluate its performance when transitioning from optoelectronic cameras to inertial measurement units (Dataset 2), and iii) generalize it in an experimental design with cognitive manipulations (Dataset 3). The method is based on the Procrustes transformation and multidimensional scaling. This geometric approach allows us to define the individual space of variability (i.e., the amount of bidimensional space covered by each individual’s trial-by-trial data). In turn, it provides robust evidence to identify each individual unique and specific motor signature (motor fingerprint). Our approach represents a fundamental shift from previous research: It is not the value of kinematic parameters per se that defines an individual’s motor signature, but rather the distinct way in which each individual varies these parameters, i.e., the dispersion of the distribution of their kinematic data. This novel perspective provides a single-subject-weighted proxy for motor signature, based on the characteristic dispersion of each individual’s data. The potential applications of this new method in research and clinical settings represent a fascinating future challenge.

Publicly available data are required to (1) assess the reproducibility of each individual findings in the literature, and (2) promote the reuse of data for a more efficient use of participants’ time and public resources. Current data-sharing efforts are well suited for the first goal, yet they do not sufficiently address the second goal. Here, we show how structured collections of open data can be useful, as they allow a larger community of researchers easy access to a large body of data from their own research area. We introduce the Attentional Control Data Collection, a SQL database for attentional control experiments. We illustrate the structure of the database, how it can be easily accessed using a Shiny app and an R-package, and how researchers can contribute data from their studies to the database. Finally, we conduct our own initial analysis of the 64 data sets in our database, assessing the reliability of individual differences. The analysis highlights that reliability is generally low, and provides insights into planning future studies. For example, researchers should consider increasing the number of trials per person and condition to at least 400. The analysis highlights how an open database like ACDC can aid meta-analytic efforts as well as methodological innovation.

A growing body of evidence suggests that imprecise multisensory temporal processing is associated with a range of adverse clinical outcomes. Specifically, this research has shown that the temporal binding window of audiovisual integration widens with advancing age, whereas individuals with various neurodevelopmental disorders exhibit distinctive profiles of multisensory processing when compared to their neurotypical counterparts. While this line of research has made significant inroads in emphasising the importance of multisensory processing in both typical and atypical cognition, the field would greatly benefit from the validation of web-based tools for assessing multisensory temporal processing, enabling larger sample sizes and the recruitment of typically hard-to-reach populations. Here, we assessed the reliability of a web-based version of the sound-induced flash illusion (SIFI) and examined the level of agreement between the measurements of the temporal binding window that it produces relative to those derived from the SIFI task performed under standard laboratory conditions. Our results demonstrate that both versions of the task elicit a robust SIFI effect and show a similar dependency on the stimulus onset asynchronies between the audiovisual stimuli. Furthermore, our analysis revealed strong correlations between the estimated measurements of both the width and peak amplitude of the temporal binding window derived from the online and lab-based task versions, confirming the suitability of the web-based task for assessing multisensory function. Future studies should extend the evaluation of this online implementation of the SIFI to clinical populations who exhibit impaired multisensory temporal processing, to assess its suitability for clinical research.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13428-025-02791-3.

This paper introduces feature-based behavior coding (FBBC), an efficient method for exploratory analysis in behavioral research using pose estimation techniques. FBBC addresses the challenges of traditional behavioral coding methods, particularly in the exploratory stages of research when coding schemes are not yet well defined. By leveraging keypoint detection and dimensionality reduction, FBBC transforms video data into interpretable feature time series, enabling researchers to analyze diverse postural patterns more efficiently. Also presented is Behavior Senpai, an open-source software implementation of FBBC that integrates automated feature extraction with human insight. A case study demonstrates FBBC’s ability to classify complex postures by combining multiple features and manual clustering. While the current iteration focuses on instantaneous posture classification, the framework shows potential for expansion to action classification. FBBC offers increased flexibility in developing coding schemes and reduces the time-consuming nature of repetitive observations. This approach represents a considerable advancement in behavioral research, bridging traditional methods with modern machine-learning techniques. As FBBC is adopted and refined, it will contribute to more comprehensive and insightful behavioral analyses across the psychological and behavioral sciences.

Musical stimuli are commonly used in psychological research for investigating a range of emotional, cognitive, and physiological processes. Despite this widespread use, many studies continue to rely on ad hoc music stimulus selection, compromising experimental control, reliability, and comparability across studies. Existing musical stimulus sets tend to be limited in variability in terms of style (e.g. comprising 1–3 genres) and familiarity (e.g. focusing on highly familiar or unfamiliar music). In this paper, we introduce MUSIFEAST-17; a music stimulus set featuring 356 instrumental 30-s clips from commercially released music across 17 genres, aiming to increase the ecological validity of studies tailored for UK and US listeners. Designed to reflect the diversity of everyday Western musical experiences, MUSIFEAST-17 includes art music, popular music, and music composed for media. MUSIFEAST-17 includes normative data from 701 UK and US participants, sampled evenly across adulthood (ages 18–75), on familiarity, enjoyment, emotional expression, perceived contrast, genre recognition, thought types, and contextual associations evoked by each excerpt. Analyses indicated that, while some responses vary by genre, nationality, and age group, MUSIFEAST-17 constitutes a set of stimuli that exhibit stylistic diversity, span familiar to unfamiliar music, and cover a range of emotional expressions. The stimulus set prompted various thought types, including memories and fictional imaginings, and contextual associations such as “movie”, “club”, and “concert”. This resource enables systematic stimuli selection for diverse applications within psychology (e.g. emotion studies, aesthetic experience, music-evoked imaginings) and supports Open Research practices.

High-level cognition, such as intelligence and creativity, are considered the hallmark of human cognition; however, their complexity hinders the identification of underlying common mechanisms. We focus on one such likely mechanism—mental navigation. We utilize converging computational methods to demonstrate how mental navigation—operationalized via verbal fluency tasks—predicts individual differences in creativity, intelligence, and openness to experience (the personality trait most closely related to them). Participants’ (N = 479) responses to two tasks—a 2-min animal fluency task and a 2-min generating synonyms of the word “hot” fluency task—were modeled over a multidimensional model (a cognitive multiplex network) of the mental lexicon. Quantitative measures of their mental navigation were used to build regression models that significantly predicted their assessed high-level cognition (replicating across both fluency tasks). Finally, we developed an online tool that capitalizes on our approach—the High-level Cognitive Prediction tool. Overall, we show how converging computational tools can elucidate the complexity of high-level cognition.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13428-025-02748-6.

Location-scale models in the field of meta-analysis allow researchers to simultaneously study the influence of moderator variables on the mean (location) and variance (scale) of the distribution of true effects. However, the increased complexity of such models can make model fitting challenging. Moreover, the statistical properties of the estimation and inference methods for such models have not been systematically examined in the meta-analytic context. We therefore conducted a Monte Carlo simulation study to compare different estimation methods (maximum or restricted maximum likelihood estimation), significance tests (Wald-type, permutation, and likelihood-ratio tests), and methods for constructing confidence intervals (Wald-type and profile-likelihood intervals) for the scale coefficients of such models. When restricted maximum likelihood estimation was used, slightly closer to nominal rejection rates and narrower confidence intervals were obtained. The permutation test yielded type I error rates closest to the nominal level, whereas the likelihood-ratio test obtained the highest statistical power. In most scenarios, profile-likelihood intervals showed lower coverage probabilities than the Wald-type method but closer to the nominal 95% level. Finally, slightly higher rejection rates and coverage probabilities were obtained when a dichotomous moderator was examined rather than a continuous one. Despite the need to use some constraints on the parameter space for the scale coefficients and the possibility of non-convergence of some procedures that may affect the fitting of the specified models, location-scale models proved to be a valid and useful tool for modeling the heterogeneity parameter in meta-analysis.