Decomposing response inhibition: a POMDP model

Inhibition is a core cognitive control function whose competence is distributed across the population, with more extreme impairments 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, the typical focus is on fitting mean reaction times, overlooking trial-by-trial dynamics. To address these limitations, we model the SST as a partially observable Markov decision process. This framework characterizes inhibitory control through distinct components: noisy perceptual inference regarding stimuli, and optimal control balanced against potential costs. To assess the ability of the model to capture the distribution of inhibitory capacities, we fit it to data from the large Adolescent Brain Cognitive Development (ABCD) study baseline cohort (N = 5,114). To do this, we adapted Simulation-Based Inference with a transformer-based encoder. This architecture learns compact, sequence-aware embeddings from raw behavioral data. These embeddings enable amortized inference of individual-level parameter posteriors in an efficient and reliable end-to-end manner, as confirmed by extensive validation. We identified distinct computational phenotypes associated with ADHD traits. Children with higher ADHD scores exhibited greater directional imprecision, a diminished intrinsic penalty for inhibition failures, and a more deterministic response style. Notably, the learned embedding space reveals a continuous manifold where children with the higher ADHD scores are heterogeneously distributed, rather than forming distinct disorder clusters. This indicates that similar clinical traits can emerge from diverse combinations of computational mechanisms, supporting a dimensional perspective on neurodiversity. Our framework can be extended to a broader range of cognitive tasks, offering a scalable solution for fitting complex models to large-scale behavioral data. Author summaryInhibitory control is essential for adjusting thoughts and behavior and is often impaired in conditions like ADHD. Traditional models of the Stop Signal Task (SST) often oversimplify the complex decision-making involved. We formalized these cognitive processes using a more biologically grounded framework (POMDP). This approach separates perceptual processing from control adjustments and remains valid in diverse experimental designs where traditional models fail. To apply the model at scale, we developed a specialized machine learning approach (TeSBI). This allowed us to efficiently reverse-engineer individual cognitive profiles. Applying it to the ABCD dataset (which includes more than 5,000 children), we found that higher ADHD scores are linked to specific computational deficits: noisy sensory processing, a lack of concern for errors, and a deterministic response style. Crucially, children with higher ADHD scores did not form a single disorder cluster but displayed diverse cognitive combinations, supporting a dimensional view of neurodiversity. Our results show that our model effectively captures complex inhibition mechanisms. By combining theory-driven cognitive modeling with scalable data-driven inference, this framework enables the precise analysis of large-scale behavioral datasets. This paves the way for more personalized approaches in computational psychiatry by recognizing the heterogeneity within clinical traits.