An optimality framework for understanding inhibitory control in countermanding tasks

Abstract

Event Abstract Back to Event An optimality framework for understanding inhibitory control in countermanding tasks The flexible and timely ability to stop or alter a planned course of action in the face of changing demands is a hallmark of intelligent behavior. The stop-signal or countermanding task, in which an initial “go” signal is later countered by a “stop” signal inhibiting the response, is a classic paradigm used to examine the behavioral strategy and neural processing underlying inhibitory control across normal and psychiatric populations (notably ADHD patients), and across species. The prevalent theoretical model of inhibitory control in this task posits a race between two noisy processes, those associated with “go” and “stop” respectively (Logan & Cowan, 1984). This model (Hanes and Schall, 1995) and its extensions (Boucher et al, 2007) have been used to account for some key behavioral measures and neurophysiological data. However, more recent behavioral experimental results, indicating behavioral adjustments at both short- and long-term timescales, pose a serious challenge for the race model (Emeric et al, 2007). Although the race model can be augmented mechanistically to incorporate these adjustments (Emeric et al, 2007), the computational provenance and import of such an ad-hoc extension would be largely missing. In this work, we present an alternative theory of inhibitory control in countermanding tasks, encouched in an optimality framework. Using a combination of Bayesian probability theory and stochastic control theory, we demonstrate that provably optimal action policies, based on clearly specified assumptions about task demands, neural noise, and behavioral objectives, not only account for the basic behavioral and neurophysiological data in the countermanding task, but also the more challenging ones related to history dependence. In particular, our work provides a common framework that reconciles apparently contradictory experimental data, revealing important behavioral consequences of subtle differences in experimental design or task instructions. Our model also makes quantitative predictions regarding behavior and neurobiology in novel variations of the task. In summary, this alternative theory provides a rigorous framework for explaining not only “what” the behavioral strategy is and “how” the underlying neural machinery implements the necessary computations, but a normative account of “why” the behavioral strategy and neural responses are what they appear to be for countermanding tasks. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). An optimality framework for understanding inhibitory control in countermanding tasks. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.151 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.