Filip Děchtěrenko

Retrieval practice is effective for learning, and is often implemented in digital applications through multiple‐choice questions (MCQs). Stepwise presentation, with answer options appearing after a brief delay, was recently shown to make MCQs more effective. We tested its effects in an authentic setting: a popular TV website with learning games. Users ( N = 13,060) who started a quiz were randomly assigned to standard MCQs, learner‐paced stepwise MCQs, or system‐paced stepwise MCQs (fixed 6 s delay), all with feedback. Learner‐pacing had a (small) positive effect on persistence as measured by the proportion of children completing the entire quiz compared to the other two conditions. Learner‐paced delay times suggested that (most) learners processed the question before revealing the answers. The MCQ‐condition did not affect short‐term learning gains. Overall, learner‐paced stepwise MCQs seem preferable in self‐study settings, to allow learners to recall information from memory but not frustrate them by enforcing a fixed delay.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1–3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, where for one claim per study, at least five re-analysts independently re-analysed the original data. The statistical appropriateness of the re-analyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent re-analyses yielded the same result (within a tolerance region of +/- 0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator rose to 57%. Regarding the conclusions drawn, 74% of analyses were reported to arrive at the same conclusion as in the original investigation; 24% to no effects/inconclusive result, and 2% to the opposite effect as in the original investigation. This exploratory study suggests that the common single-path analyses in social and behavioural research should not simply be assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

Feedback is a critical resource to support the development of researchers and the quality of their work. However, many established feedback mechanisms within research rely upon privilege and gatekeeping and are therefore hard to understand and access, particularly to Early Career Researchers (ECRs) and marginalised communities. Facilitated through the Framework for Open and Reproducible Research Training (FORRT) community, a global transdisciplinary survey (n=881) was designed to capture the feedback processes adopted by researchers and map accessible feedback opportunities from across the research cycle. Fifty-two distinct feedback mechanisms were identified, with the majority used throughout the research cycle. Fundamentally, the results suggest the feedback landscape remains marked by structural inequalities, over-reliance on informal networks, and underuse of open and institutional mechanisms. Findings underscore the greatest potential for cultural and structural change: normalising feedback-seeking behaviours, investing in collaborative infrastructures, embedding open research practices, and critically reflecting on current use of artificial intelligence (AI). We can build a more equitable and rigorous research environment by investing in, and diversifying feedback avenues, as well as reinforcing inclusive mentorship and institutional support.

Some images are consistently remembered better than others, suggesting that memorability reflects intrinsic image properties. We tested whether within-category distinctiveness underlies this effect. Across three experiments ( N = 477), participants categorized indoor scenes previously rated for subjective typicality and then completed recognition memory tests. Typical scenes were categorized faster and more accurately, but were remembered worse and showed a more liberal response bias than atypical scenes. These opposing effects were robust across categories. To link subjective typicality to visual representations, we quantified image distinctiveness using a convolutional neural network (CNN). Across layers, CNN-derived distinctiveness closely tracked human typicality judgments and predicted both categorization speed and memorability, with strongest effects in higher, semantic layers. Critically, the memory advantage for atypical scenes persisted even when most images were atypical, ruling out rarity within the experimental context. Together, the results show that intrinsic scene memorability reflects an image’s position within a category-specific representational space.

Visualizing numerical information in graphs to facilitate processing from text is now standard practice. This study investigated how congruency between information in real-life newspaper articles and accompanying graphs affects memory and learning. Using articles from the Czech newspaper Deník N, the experiment compared three conditions: congruent (data in text and graph aligned), incongruent (data in text and graph differed), and graph-only (numerical data removed from text). Participants read three articles and were assessed for information gain via multiple-choice tests. Findings suggest that congruent conditions facilitated the retention of numerical data, whereas incongruent and graph-only conditions impaired performance. These results have practical implications for media and education; ensuring information congruency is vital for effective data acquisition and the successful use of visualizations.

Psychiatric intensive care units provide care for mental health patients in psychiatric crises, often involving violent behaviour. This study aimed to examine the ability of the Dynamic Appraisal of Situational Aggression (DASA) to predict violent acts of different proximal causes in patients. We collected 2467 DASA predictions from 352 patients who had committed 366 violent incidents. All incidents were recorded using a Modified Overt Aggression Scale (MOAS). Receiver operating characteristic (ROC) curve analysis was used to assess the DASA predictive values. DASA demonstrated an overall area under the curve (AUC) of 0.76 (95% confidence interval [CI] [0.72, 0.79]), with similar values for impulsive (AUC = 0.75, 95% CI [0.70, 0.81]) and psychotic (AUC = 0.75, 95% CI [0.70, 0.79]) aetiologies. Notably, the AUC for females was 0.80 (95% CI [0.75, 0.86]), indicating a particularly high predictive validity for women. We also observed that higher DASA scores predict higher incident severity assessed by MOAS scores (Spearman’s ρ = 0.13, p = 0.03). Most employed nursing interventions included de‐escalation techniques (18%) and one‐to‐one nursing. In patients with high‐risk scores, interventions also involved psychopharmacological or even coercive methods. The DASA shows satisfactory predictive validity for both psychotic and impulsive aetiological incidents and demonstrates better predictive validity for women. This study evaluated the predictive validity of DASA for different types of violent acts (psychotic, impulsive and predatory) in both sexes and implemented a set of corresponding interventions. These interventions should be customised to address the specific risk factors in each patient.

The article is focused on the economic performance of Czech companies in the accommodation, catering and hospitality sector during the COVID-19 pandemic. The aim is to verify the influence of selected factors on their level and development using data from the Albertina database for the period 2018–2021 was used for the analysis. Return on assets (ROA) was selected as the main performance indicator and the indicators monitored included indebtedness, liquidity, size, age of enterprise and the sub-sector to which the enterprise belonged. The results showed that larger businesses and those operating in the hospitality and catering sub-sector managed the difficult situation caused by the pandemic better than those operating in the accommodation sub-sector. The factors of company size and sector therefore had the greatest influence on the value of the ROA economic performance indicator.

The relationship between personality and brain activity has been an increasingly popular topic of neuroscientific research. Naturalistic viewing has been shown to enhance individual differences and might, therefore, be particularly useful for exploring this relationship. Here, we thus examine neural signatures of personality using naturalistic fMRI of 82 healthy subjects. We implemented a simple dimensionality reduction method to characterize brain activity by its “typicality,” assessed a range of personality traits using widely‐used personality inventories, and tested the relationship between the two. We found that there is, indeed, a relationship between personality and the typicality of brain activity, which appears to be most consistently manifested by lower typicality in subjects with higher Neuroticism/Harm Avoidance. Our results highlight the usefulness of naturalistic viewing data for exploring the relationship between individual differences in personality and brain activity.

Digital representation of materials is crucial in fields such as virtual reality, industrial design and quality control. However, predicting human perception of materials from image data is challenging due to the complexity of material appearances and the intricacies of human vision. This study introduces a perceptual representation termed the ‘visual fingerprint’, linking image-based measurements of materials to intuitive, human-understandable attributes. We conducted psychophysical studies using standardized video sequences of 347 diverse real-world materials, including fabrics and wood, selected to encompass a broad spectrum of textures, colours and reflective properties. Sixteen key appearance attributes were identified, and over 110 000 human ratings were collected to map perceptual attributes across material categories. By integrating CLIP-derived image features with a multi-layer perceptron model, we developed a predictive framework for material perception. Our results demonstrate that human judgements of appearance and similarity can be accurately predicted using only two images of a material. This work offers a practical and interpretable approach to material representation, enabling intuitive comparisons and retrievals in applications where material appearance is crucial. The proposed material fingerprint and its prediction directly from image data represent a significant step towards simplifying the understanding and interoperability of material properties in diverse digital environments.