Decomposing response inhibition: A POMDP model

The useful part

However, conventional formalizations of SST performance, such as the independent race model, rely on assumptions that are frequently violated in modern experimental designs. 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).

How it works

• This framework characterizes inhibitory control with two components: noisy perceptual inference regarding stimuli and optimal control balanced against potential costs.
• 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.

What to take from it

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). Extensive validation confirms it extracts reliable and identifiable parameters.

Details worth keeping

Task (SST) is a widely used paradigm for assessing this ability. It enables efficient, amortized inference of individual-level posteriors.