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      Reward Pays the Cost of Noise Reduction in Motor and Cognitive Control

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          Summary

          Speed-accuracy trade-off is an intensively studied law governing almost all behavioral tasks across species. Here we show that motivation by reward breaks this law, by simultaneously invigorating movement and improving response precision. We devised a model to explain this paradoxical effect of reward by considering a new factor: the cost of control. Exerting control to improve response precision might itself come at a cost—a cost to attenuate a proportion of intrinsic neural noise. Applying a noise-reduction cost to optimal motor control predicted that reward can increase both velocity and accuracy. Similarly, application to decision-making predicted that reward reduces reaction times and errors in cognitive control. We used a novel saccadic distraction task to quantify the speed and accuracy of both movements and decisions under varying reward. Both faster speeds and smaller errors were observed with higher incentives, with the results best fitted by a model including a precision cost. Recent theories consider dopamine to be a key neuromodulator in mediating motivational effects of reward. We therefore examined how Parkinson’s disease (PD), a condition associated with dopamine depletion, alters the effects of reward. Individuals with PD showed reduced reward sensitivity in their speed and accuracy, consistent in our model with higher noise-control costs. Including a cost of control over noise explains how reward may allow apparent performance limits to be surpassed. On this view, the pattern of reduced reward sensitivity in PD patients can specifically be accounted for by a higher cost for controlling noise.

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          Highlights

          • The speed-accuracy trade-off in motor and cognitive control can be broken by reward

          • Apparent limits of performance can be overcome by motivation

          • A cost for reducing intrinsic neural noise quantitatively explains such improvements

          • Reduced reward effects in Parkinson’s disease suggest an increased cost of control

          Abstract

          Manohar et al. investigate how motivation by reward can improve both speed and accuracy, apparently exceeding the limits of the speed-accuracy trade-off. They propose a cost for reducing intrinsic neural noise. Optimizing this cost predicts both motor and cognitive performance. The cost of control may be increased in Parkinson’s disease.

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          Most cited references58

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          Noise in the nervous system.

          Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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            The expected value of control: an integrative theory of anterior cingulate cortex function.

            The dorsal anterior cingulate cortex (dACC) has a near-ubiquitous presence in the neuroscience of cognitive control. It has been implicated in a diversity of functions, from reward processing and performance monitoring to the execution of control and action selection. Here, we propose that this diversity can be understood in terms of a single underlying function: allocation of control based on an evaluation of the expected value of control (EVC). We present a normative model of EVC that integrates three critical factors: the expected payoff from a controlled process, the amount of control that must be invested to achieve that payoff, and the cost in terms of cognitive effort. We propose that dACC integrates this information, using it to determine whether, where and how much control to allocate. We then consider how the EVC model can explain the diverse array of findings concerning dACC function. Copyright © 2013 Elsevier Inc. All rights reserved.
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              A computational neuroanatomy for motor control.

              The study of patients to infer normal brain function has a long tradition in neurology and psychology. More recently, the motor system has been subject to quantitative and computational characterization. The purpose of this review is to argue that the lesion approach and theoretical motor control can mutually inform each other. Specifically, one may identify distinct motor control processes from computational models and map them onto specific deficits in patients. Here we review some of the impairments in motor control, motor learning and higher-order motor control in patients with lesions of the corticospinal tract, the cerebellum, parietal cortex, the basal ganglia, and the medial temporal lobe. We attempt to explain some of these impairments in terms of computational ideas such as state estimation, optimization, prediction, cost, and reward. We suggest that a function of the cerebellum is system identification: to build internal models that predict sensory outcome of motor commands and correct motor commands through internal feedback. A function of the parietal cortex is state estimation: to integrate the predicted proprioceptive and visual outcomes with sensory feedback to form a belief about how the commands affected the states of the body and the environment. A function of basal ganglia is related to optimal control: learning costs and rewards associated with sensory states and estimating the "cost-to-go" during execution of a motor task. Finally, functions of the primary and the premotor cortices are related to implementing the optimal control policy by transforming beliefs about proprioceptive and visual states, respectively, into motor commands.
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                Author and article information

                Contributors
                Journal
                Curr Biol
                Curr. Biol
                Current Biology
                Cell Press
                0960-9822
                1879-0445
                29 June 2015
                29 June 2015
                : 25
                : 13
                : 1707-1716
                Affiliations
                [1 ]Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK
                [2 ]Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK
                [3 ]Institute of Neurology, University College London, London WC1N 3BG, UK
                [4 ]Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, UK
                [5 ]National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
                Author notes
                []Corresponding author sanjay.manohar@ 123456ndcn.ox.ac.uk
                Article
                S0960-9822(15)00612-0
                10.1016/j.cub.2015.05.038
                4557747
                26096975
                486043c2-9791-47f8-aea4-fa39953b17e0
                © 2015 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 9 February 2015
                : 7 April 2015
                : 19 May 2015
                Categories
                Article

                Life sciences
                motivation,speed-accuracy trade-off,decision-making,dopamine,drift-diffusion model
                Life sciences
                motivation, speed-accuracy trade-off, decision-making, dopamine, drift-diffusion model

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