2026-10-13T16:29:48Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34000368 2026-09-25T17:54:35Z category_7 category_15 category_45 category_734 category_122 portal_5 item_type_3 month_year_09_2026

<p>Observation likelihoods for the go process.</p> Wenting Wang (2274556) Tobias Kaufmann (297371) Peter Dayan (26734) Medicine Neuroscience Sociology Biological Sciences not elsewhere classified Mental Health widely used paradigm study baseline cohort similar clinical characteristics raw behavioral data optimal control balanced modern experimental designs mean reaction times impairments often observed extensive validation confirms decomposing response inhibition stop signal task overlooking crucial trial independent race model higher adhd scores trial dynamics stop error adhd questionnaires adhd ). two components time costs tesbi ). robust shifts psychiatric conditions potential costs pomdp ). medication status level posteriors introduce transformer identifiable parameters heterogeneously distributed go error frequently violated extracts reliable encoded simulation enables efficient driven solution diverse combinations dimensional perspective conventional formalizations continuous manifold computational mechanisms computational attributes cognitive tasks broader range blunted sensitivity based inference aware embeddings analyzing large amortized inference 567 ), <div><p>Inhibitory control is a core cognitive function whose competence varies across the population, with impairments often observed in psychiatric conditions such as attention deficit hyperactivity disorder (ADHD). The Stop Signal Task (SST) is a widely used paradigm for assessing this ability. However, conventional formalizations of SST performance, such as the independent race model, rely on assumptions that are frequently violated in modern experimental designs. Furthermore, they typically fit only mean reaction times, overlooking crucial trial-by-trial dynamics. To address these limitations, we formalize the SST as a partially observable Markov decision process (POMDP). This framework characterizes inhibitory control with two components: noisy perceptual inference regarding stimuli and optimal control balanced against potential costs. To fit this model to the Adolescent Brain Cognitive Development (ABCD) study baseline cohort (N = 3,567), we introduce Transformer-encoded Simulation-Based Inference (TeSBI). This end-to-end architecture learns compact, sequence-aware embeddings from raw behavioral data. It enables efficient, amortized inference of individual-level posteriors. Extensive validation confirms it extracts reliable and identifiable parameters. We identify distinct latent computational attributes associated with scores on ADHD questionnaires. Controlling for sex, IQ, and medication status, children with higher ADHD scores exhibit subtle but robust shifts in computational attributes. They show a reduction in go cue directional precision, alongside a blunted sensitivity to stop error, go error and time costs. The learned embedding space reveals a continuous manifold in which children with higher ADHD scores are heterogeneously distributed, rather than forming distinct disorder clusters. This indicates that similar clinical characteristics can emerge from diverse combinations of computational mechanisms, supporting a dimensional perspective on neurodiversity. Our end-to-end framework can be extended to a broader range of cognitive tasks. It offers a scalable, theory-driven solution for analyzing large-scale behavioral data.</p></div> 2026-09-25T17:54:21Z Dataset Dataset 10.1371/journal.pcbi.1014068.t003 https://figshare.com/articles/dataset/_p_Observation_likelihoods_for_the_go_process_p_/34000368 CC BY 4.0