Understanding how semantics are processed at the lexical level has been a key aim for the cognitive sciences, with large-scale semantic rating studies in different languages playing a crucial role in facilitating the advancement and development of theories of lexical processing, as well as providing valuable methodological resources that can be used in empirical investigations. In this paper, we present the CzeLex database, a megastudy of 9601 Czech words that have been normed along five psycholinguistic dimensions from a large sample of 4405 Czech-speaking participants. We provide individual rating data at the item and participant levels, as well as aggregated data from both mean ratings and latent means derived using cumulative-link mixed modeling, along with a comprehensive analysis comparing aggregation methods. We demonstrate that the dataset has good reliability and that specific dimensions predict lexical processing measures in a lexical decision task, regardless of the aggregation method used. These norms are the largest available subjective rating resource for Czech and further contribute to the growing body of languages with openly available data on these dimensions. We hope that the dataset will be a valuable resource for researchers working on Czech, in addition to opening up new opportunities for cross-linguistic semantic investigations. All data, code, and analyses are available at https://osf.io/tqxfm .
Empathizing and mentalizing are crucial to social understanding, yet few instruments capture these processes across a broad emotional spectrum. The EmpaToM is a video-based paradigm that independently measures empathy and theory of mind (ToM) and can additionally incorporate a prosociality measure. As its current form is limited to negatively valenced emotional contexts, we introduce two expanded German versions: EmpaToM-pos, featuring positively valenced narrations, and EmpaToM-embarrass, featuring embarrassing narrations, alongside the original neutral and negative conditions. In two pilot studies (Ns = 28/27), we tested novel video stimuli and corresponding questions, revised them based on the data, and subsequently evaluated them in two higher-powered studies (Ns = 60/55). Results confirmed successful empathy induction in all emotional conditions: Participants reported feeling worse after negative, more joyful after positive, and more embarrassed after embarrassing narrations (compared to all other valence conditions, respectively). Emotional valence also influenced prosociality: Negative narrations prompted the highest willingness to invest, but positive and embarrassing narrations also elicited greater prosociality than the neutral condition. Both expanded versions supported reliable ToM assessment without ceiling effects and with similar performance levels in the ToM and control conditions. Analyses further revealed that, on an interindividual level, empathic resonance in both negative and positive contexts was positively correlated with the degree of prosociality, highlighting the interrelation between empathizing and social decision-making. By incorporating positive and embarrassing emotional contexts, the EmpaToM-pos and EmpaToM-embarrass broaden the range of assessable social-emotional experiences, offering new opportunities to investigate empathy, ToM, and prosociality in more ecologically valid and diverse settings.
During visual search and foraging tasks, an observer freely scans a display looking for one or more targets of interest among distractors. One strategy for studying the scan patterns uses the angles between consecutive saccades. The methods used to study scan directions range from descriptive angle analysis to “binning” the direction in discrete spatial categories. Here we present some trigonometry-based indicators to quantitatively account for horizontality and verticality. Furthermore, we propose an indicator to assess the combined trend to scan either horizontally or vertically (as opposed to oblique directions). We demonstrate its usefulness with both horizontal and directional angles, reanalyzing data from a visual foraging task. We also detect a cardinality effect in horizontal angles which would not have been detected by analyzing the angles directly. Furthermore, we show a not-going-forward effect on directional angles, which is more detectable using simple trigonometric functions.
The speed–curvature power law is commonly estimated in two forms: tangential speed regressed on curvature (VC) and angular speed regressed on curvature (AC). Since angular speed is the product of curvature and tangential speed, exponents of the two power laws when fitted to the same trajectory satisfy the exact relation β _VC=β _AC-1 , and the two power laws are generally treated as equivalent and interchangeable. However, this algebraic identity does not make the two formulations equivalent as statistical goodness-of-fit tests. In the AC regression, curvature appears in both the predictor and the outcome ( A = VC ). Because of this, the AC coefficient of determination can be inflated by curvature variance even when movement does not slow in curved segments. I demonstrate this mechanism analytically, in simulations, and in three empirical tasks: elliptical target tracking, self-paced ellipse tracing, and free-form scribbling. In the three datasets, mean AC R^2 was very high: .77, .83, and .85, compared with mean VC R^2 values of .30, .25, and .39. In a randomized-speed check across all Experiment 2 trials, changing the timing of speed reduced VC R^2 from .28 to .017 while AC R^2 remained high, moving from .84 to .79. Therefore, a high AC R^2 is not independent evidence of biological speed–curvature coupling. Researchers should estimate the law with tangential speed and curvature and reexamine conclusions based only on angular-speed goodness-of-fit.
We introduce the Japanese Sentence Corpus (JSC), a sentence corpus containing eye-movement data from young adults reading horizontally and vertically presented sentences, along with large-scale predictability norms for each word. Eye-movement corpora have expanded rapidly, with resources available for alphabetic scripts such as English, German, and French, as well as for logographic Chinese. Japanese stands out for its unique linguistic features, combining a multiscriptal writing system of logographic Kanji and phonographic Hiragana and Katakana with one of the few cases of flexible text direction. This makes Japanese, alongside Chinese, an excellent test case for investigating how readers adapt their visual processing and oculomotor activity to different text orientations. Using predictability norms, we analyzed eye-movement data from 67 young native Japanese readers, examining how properties of preceding, current, and upcoming words influence fixation durations. Together with existing corpora, the JSC adds a typologically distinct language, strengthening cross-linguistic comparisons and identifying language-universal and language-specific effects. Ultimately, JSC provides a benchmark dataset for computational models of Japanese sentence processing, bridging psycholinguistics and large language models.
