Large behavioral datasets that provide detailed data on reading processes are valuable resources for a range of researchers working in linguistics, psychology and cognitive science. This paper presents the HeCz corpus, which comprises self-paced reading data for 1919 newspaper headlines (23,634 words) in Czech, with each headline being accompanied by a yes–no comprehension question, resulting in a rich dataset of reading times for each individual word and comprehension accuracy. The corpus is novel in terms of the sheer scale of data collection, with 1872 native Czech speakers, each reading approximately 120 headlines, with 1162 of those participants also completing the experiment again in a re-testing session using the same stimuli approximately 1 month later. There is participant level meta-data also available relating to basic demographic information, reading habits and a profile of their mood state prior to completing the experiment. Beyond the behavioral and demographic data, we also include a range of linguistic annotations for several variables, e.g., frequency, surprisal, morphological tagging. To better understand how these variables might impact processing, we present exploratory analyses where we predicted the reading times for words, with the results indicating important roles for linguistic, demographic, and methodological variables. Given the range of multidisciplinary applications of the HeCz corpus, we hope that it will provide a valuable and unprecedented resource for a range of research applications related to reading processes.

In reading tasks, drift can move fixations from one word to another or even another line, invalidating the eye-tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast – yet limited in accuracy. In this paper, we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye-tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N = 14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through the NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that, on average, the proposed technique was 44% faster than manual correction without any sacrifice of accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye-tracking data, visualization, filters, data converters, and eye-movement analysis in addition to the main contribution in data correction.

Despite extensive research on absolute pitch (AP), there remains no gold-standard task to measure its presence or extent. This systematic review investigated the methods of pitch-naming tasks for the classification of individuals with AP and examined how our understanding of the AP phenotype is affected by variability in the tasks used to measure it. Data extracted from 160 studies (N = 23,221 participants) included (i) the definition of AP, (ii) task characteristics, (iii) scoring method, and (iv) participant scores. While there was near-universal agreement (99%) in the conceptual definition of AP, task characteristics such as stimulus range and timbre varied greatly. Ninety-five studies (59%) specified a pitch-naming accuracy threshold for AP classification, which ranged from 20 to 100% (mean = 77%, SD = 20), with additional variability introduced by 31 studies that assigned credit to semitone errors. When examining participants’ performance rather than predetermined thresholds, mean task accuracy (not including semitone errors) was 85.9% (SD = 10.8) for AP participants and 17.0% (SD = 10.5) for non-AP participants. This review shows that the characterisation of the AP phenotype varies based on methodological choices in tasks and scoring, limiting the generalisability of individual studies. To promote a more coherent approach to AP phenotyping, recommendations about the characteristics of a gold-standard pitch-naming task are provided based on the review findings. Future work should also use data-driven techniques to characterise phenotypic variability to support the development of a taxonomy of AP phenotypes to advance our understanding of its mechanisms and genetic basis.

Multiple systems in the brain track the passage of time and can adapt their activity to temporal requirements. While the neural implementation of timing varies widely between neural substrates and behavioral tasks, at the algorithmic level, many of these behaviors can be described using drift-diffusion models of decision-making. In this work, wedevelop a drift-diffusion model to fit performance in the temporal generalization task, in which participants are required to categorize an interval as being the same or different compared to a standard, or reference, duration. The model includes a drift-diffusion process which starts with interval onset, representing the internal estimate of elapsed duration, and two boundaries. If the drift-diffusion process at interval offset is between the boundaries, the interval is categorized as equal to the standard. If it is below the lower boundary or above the upper boundary, the interval is categorized as different. This model outperformed previous models in fitting the data of single participants and in parameter recovery analyses. We also used the drift-diffusion model to analyze data from two experiments, one comparing performance between vision and audition and another examining the effect of learning. We found that decision boundaries can be modified independently: While the upper boundary was higher in vision than in audition, the lower boundary decreased with learning in the task. In both experiments, timing noise was positively correlated with upper boundaries across participants, which reflects an accuracy-maximizing strategy in the task.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13428-025-02819-8.

Social sciences of all kinds are interested in latent variables, their measurement, and how they differ between groups. The present study argues the importance of analyzing mean differences between groups using the latent variable approach. Using an open-access repository of widely applied personality questionnaires (N = 999,033), we evaluate the extent to which the commonly used observed sum score is susceptible to measurement error. Our findings show that Cohen’s d values based on the observed variance significantly misestimate the true group difference (based on just the factor score variance) in 33 of the 70 studied cases, and by an average of 25.0% (or 0.048 standard deviations). There was no meaningful relationship between the effect size discrepancy and scale reliability as measured by McDonald’s ω. We discuss the implications of these results and outline concrete steps that applied researchers can take to improve their analyses.

Animal behavior analysis software has become an essential tool in the life sciences. However, the currently available tools have some significant shortcomings that limit their use by individuals without programming skills, access to higher informatics resources, or the capability to invest large sums of money. We have developed and validated an easy-to-use and straightforward tracking system named ESTraS to address this issue. This freeware software allows researchers to track and analyze rodent behaviors, offering additional options such as trajectory and angle analysis. Through ESTraS, researchers can utilize unsupervised clustering techniques, such as k-means or hierarchical clustering, to further explore the obtained results. This clustering enables the classification of results based on observed similarities among subjects. The data of this manuscript shows that ESTraS can prove to be extremely valuable, not only by providing essential behavioral analysis tools but also by offering specific data analysis options with just one click.

The importance or centrality of a linguistic unit to a larger unit’s meaning is known to affect reading behavior. However, there is an ongoing debate on how to quantify a unit’s degree of importance or centrality, with previous quantifications using either subjective ratings or computational solutions with limited interpretability. Here we introduce a novel measure, which we term “informativeness”, to assess the significance of a word to the meaning of the sentence in which it appears. Our measure is based on the comparison of vectorial representations of the full sentence with a revised sentence without the target word, resulting in an easily interpretable and objective quantification. We show that our new measure correlates in expected ways with other psycholinguistic variables (e.g., frequency, length, predictability), and, importantly, uniquely predicts eye-movement reading behavior in large-scale datasets of first (L1) and second language (L2) readers (from the Multilingual Eye-tracking Corpus, MECO). We also show that the effects of informativeness generalize to diverse writing systems, and are stronger for poorer than better readers. Together, our work provides new avenues for investigating informativeness effects, towards a deeper understanding of the way it impacts reading behavior.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13428-025-02878-x.

Iconicity, understood as a resemblance relationship between meaning and form, is an important variable that has important psycholinguistic effects in lexical processing and language learning across modalities of language. With the growing interest in iconicity, clear operationalizations in terms of the different ways in which iconicity is construed and measured are critical for establishing its broader psycholinguistic profile. This study reports a normed database of iconicity ratings for the same concepts in British Sign Language (BSL) and German Sign Language (DGS). As a related dimension, we also report the type of iconic mapping strategy, i.e., a nominal variable that reflects the different ways in which signs make form-meaning associations for each sign. Finally, we include concreteness ratings for the same concepts. Data from deaf and hearing signers show that iconicity ratings are strongly correlated across both languages, with different distributions across the different strategies, and skewed towards the iconic end of the scale for all groups except German hearing non-signers. Concreteness ratings in BSL and DGS are correlated, though more weakly, and skewed towards the concrete end of the scale. Interestingly, this differs from findings for spoken languages, where concreteness ratings exhibit substantially stronger correlations and abstract concepts are more predominantly represented. We also find that iconicity and concreteness ratings have a moderate positive and strong positive correlation in BSL and DGS, respectively. These results will be useful in psycholinguistic research and highlight differences that can be attributed to the manual-visual modality of signs.

One major difficulty in conversational research is the time required to segment and transcribe conversational recordings. While recent advances have improved automatic speech recognition technologies, one limitation of current tools is that they are generally catered toward speech that occurs in monologues rather than conversation. Accordingly, the purpose of this project was to develop and validate an automated user-friendly tool for transcribing conversations. This tool, called Autoscribe, converts dyadic conversational audio recordings into Praat TextGrids with time-aligned turn boundaries between speech and non-speech segments and transcripts of all spoken dialogue output. Here we describe the development of this tool as well as its validation on two conversational corpora. Results showed that Autoscribe decreased the amount of active working time needed for TextGrid creation by over 70%. Average transcription accuracy was 92% and average utterance boundary placement of 95%. Thus, Autoscribe affords a practical research tool that drastically reduces the time and resource intensitivity needed for conversational segmentation and transcription.

Ambulatory wearable monitoring of human physiology is increasingly utilized in the fields of psychology, movement sciences, and medicine. With the rapid growth of available consumer- and research-oriented wearables, researchers are faced with a multitude of devices to choose from. It is unfeasible timewise for researchers to determine all relevant technical specifications, available signals, signal sampling details, and (raw) data availability, and conduct a search of studies regarding the reliability, validity, and usability of wearables. Thus, selection of wearables for a given study proves highly challenging and will often be unsystematic and uninformed. The 10-year research program Stress in Action initiated a publicly accessible database of wearable ambulatory monitoring devices. We outline the genesis and final structure of the first version of the Stress in Action Wearables Database (SiA-WD) and a summary of the characteristics of the wearables it currently contains. Furthermore, one short-term (2 days) and one long-term (3 months) scenario from the field of stress research are provided with walkthroughs of how the SiA-WD can help select the optimal wearable for a specific research project. Insights gathered include the scarceness of studies testing wearable user-friendliness, inconsistencies in reported validity statistics, and imprecise manufacturer documentation on recorded physiological data such as sampling rate (or window) of signals and parameter extraction. The SiA-WD is the first open-access database to simultaneously include physiological sampling information and technical specifications along with a systematic reliability, validity, and usability search. It will be iteratively expanded to facilitate informed and time-efficient wearable selection. For access to the database, see the following: https://osf.io/umgvp/.