Aug 2026· Journal of Experimental Psychology. Learning, Memory and Cognition· 0 citations
Medicine
Abstract
"List as many words as you can that start with M." The verbal fluency (VF) task is simple, yet even a typical university student only manages to produce about 15 words within 1 min, and there is substantial variability around this mean. The present study examined how linguistic and domain-general abilities contributed to VF performance in a large sample of healthy adult native speakers of Dutch (N = 571). We assessed the effects of linguistic knowledge, processing speed, short-term/working memory, and fluid intelligence on performance in the VF task. To examine whether linguistic and domain-general abilities contribute differently across VF task types, we included semantic trials (category-based generation: animals, food) and phonemic trials (letter-based generation: words beginning with S or M). We assessed the total number of correct words produced and the time to first response. Mixed-effects modeling showed that linguistic knowledge predicted the total number of correct responses in both semantic and phonemic VF. Short-term/working memory and processing speed were also significant predictors, but with smaller estimated effect sizes. Time to first response showed little effect of linguistic skills. We discuss how linguistic knowledge shapes the structure of the mental lexicon such that it affects both meaning-driven and form-driven access to lexical items. In addition, we provide updated norms for VF performance in Dutch and practical suggestions for using the task. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
Autistic children experience disproportionately high rates of anxiety, yet common interventions such as cognitive behavioural therapy and traditional mindfulness practices may be less effective due to their cognitive and abstract demands. Yoga nidra, a form of guided meditation using concrete visualisations and breath awareness, may offer a more accessible alternative. This mixed methods study evaluated the feasibility and pilot efficacy of a co-designed six-week online yoga nidra intervention targeting anxiety in autistic children aged 8-14 years. Neurophysiological and psychological data were collected from 13 participants using parent- and self-reported measures of anxiety (ASC-ASD), intolerance of uncertainty (IUSC), and emotion dysregulation (EDI), alongside heart rate variability (HRV). Notably, the cohort included a minimally speaking child, which demonstrates the potential for the intervention to extend to autistic children with language support needs, a group who are often underrepresented in similar research. Results indicated no statistically significant changes in anxiety over time; however, medium effect sizes were observed in self- (Hedge's g = 0.55) and parent-reported anxiety (Hedge's g = 0.45), with three participants moving from clinically significant to non-significant anxiety levels post-intervention. Additionally, intolerance of uncertainty and emotion dysregulation demonstrated small to medium effect size reductions (IUSC, Hedge's g = 0.50; EDI-Reactivity, Hedge's g = 0.55; EDI-Dysphoria, Hedge's g = 0.25), suggesting potential benefits of yoga nidra in these areas. Greater attendance was significantly associated with self-reported reductions in anxiety (ASC-ASD-SR, r = .757, p < .05), but not the other outcome measures. Unexpectedly, HRV outcomes indicated reduced autonomic functioning post-intervention. Additionally, qualitative data from semi-structured interviews with three child participants and their mothers from the study were analysed using reflexive thematic analysis, revealing five key themes of their experience: Thinking About the Body is Hard; We Want More; Homework Sucks; Making Mindfulness Concrete; and On-Screen and At Home is Convenient. While the online format was appreciated for its accessibility, challenges with adherence and scheduling highlighted the need for more flexible delivery models. This study advances upon prior research by being the first to develop a co-designed yoga nidra intervention specifically for autistic children. Findings support the feasibility of yoga nidra as a complementary intervention for autistic children and suggest directions for future research, including larger trials and further co-design with the autistic community.
Tundi Loftus, Shu H Yau, Sophia Soares et al.· Research in Developmental Di...· 1 citation
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
MEPO-SLM is presented, a framework that reformulates prompt engineering for SLMs as a four-objective Pareto problem over task inaccuracy, and Phi-3-mini and Gemma-2B on English TriviaQA and Arabic medical QA, and TinyLlama-1.1B on TriviaQA only are evaluated.
Yousef K. Sanjalawe, Salam R. Al-E’mari, S. Makhadmeh· Evolutionary Intelligence· 0 citations
Results show that a small box-level module can reconcile question understanding with precise localization without retraining either backbone, and introduce RefineRank, which closes this gap at the candidate-box level.
Linzhe Jiang, Jiayuan Huang, Changhao Zhang et al.· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
