Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder

Gili Katabi*, Nitzan Shahar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is characterized by difficulty in acting in a goal-directed manner. While most environments require a sequence of actions for goal attainment, ADHD was never studied in the context of value-based sequence learning. Here, we made use of current advancements in hierarchical reinforcement-learning algorithms to track the internal value and choice policy of individuals with ADHD performing a three-stage sequence learning task. Specifically, 54 participants (28 ADHD, 26 controls) completed a value-based reinforcement-learning task that allowed us to estimate internal action values for each trial and stage using computational modeling. We found attenuated sensitivity to action values in ADHD compared to controls, both in choice and reaction-time variability estimates. Remarkably, this was found only for first-stage actions (i.e., initiatory actions), while for actions performed just before outcome delivery the two groups were strikingly indistinguishable. These results suggest a difficulty in following value estimation for initiatory actions in ADHD.

Original languageEnglish
Article number10
JournalTranslational Psychiatry
Volume14
Issue number1
DOIs
StatePublished - Dec 2024

Funding

FundersFunder number
Minducate Center for the Science of Learning
Israel Science Foundation2536/20
Tel Aviv University
Ministry of Science and Technology, Israel

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