An optimality framework for understanding the psychology and neurobiology of inhibitory control
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1
University of Washington, United States
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2
University of California San Diego, Department of Cognitive Science , United States
Inhibitory control, defined as the ability to withhold or modify actions that may no longer be appropriate, is a critical aspect of human behavior. Deficits in behavioral inhibition have been implicated in a variety of psychiatric conditions such as ADHD and addiction. The classic stop signal or countermanding task, where an initial movement command is occasionally countered by a "stop" signal instructing response inhibition, has been used to study many aspects of behavioral and neural processing that may relate to a more general notion of inhbitory control. We propose an optimality based framework for explaining behavior and neural mechanism in the stop signal task. Two characteristic behavioral outcomes are the lower reaction times on trials where response inhibition fails, and higher rates of inhibition failure with later stop signals. The prevalent theoretical model (the race model; Logan & Cowan, 1984) provides a mechanistic description of this behavior as being the outcome of a race betwen two independent processes, but does not directly explain behavior as a consequence of task demands and experimental parameters. The race model, therefore, is silent on how behaviour should change as a consequence of experimental manipulations such as long-term and short-term statistics of the relative frequency of stop signals (Emeric et al., 2007). We overcome these limitations with the application of two principled computational tools. First, we use Bayesian probability theory and a model of subjective sensory processing to explicitly compute and track the state and history of the environment. Second, we define an objective function that directly encodes experimentally enforced constraints such as reward, punishment and deadlines. Together, this formal framework enables us to concretely test the hypothesis that subjects’ behavior in this task is optimal in terms of maximizing the objective function given the estimated state of the environment. We show that our model captures classic behavioral results in the countermanding paradigm, by comparing it to monkey behavioral data (Hanes & Schall, 1995). We also successfully model aspects of behavior unexplained by the race model, such as the effect of the fraction of stop signal trials and immediate trial history on reaction times and success at inhibtion (Emeric et al., 2007). Thus, on a behavioral level, our framework motivates and generalizes the assumed features of the race model, while providing insight into the computational and behavioral import of empirical measures such as the stop signal reaction time (SSRT). Various neural populations, including the frontal eye field and superior colliculus, have been implicated in countermanding (Hanes et al., 1998, Pare & Hanes, 2003). Neurons in these regions show differential activity after a delay when comparing movement and successfully withheld movements. The race model offers a mechanistic connection between behavior and such activity, since the SSRT approximates the delay before the neural activities diverge. We show that computational variables in our model show a very similar temporal profile to neural activity in the FEF. Our optimality model therefore provides a unified framework for understanding behavior, optimal computation and neural processing underlying inhibitory control.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session III
Citation:
Shenoy
P,
Rao
RP and
Yu
AJ
(2010). An optimality framework for understanding the psychology and neurobiology of inhibitory control.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00134
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Received:
01 Mar 2010;
Published Online:
01 Mar 2010.
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Correspondence:
Pradeep Shenoy, University of Washington, Seattle, United States, pshenoy@cs.washington.edu