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

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          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.

          Graphical Abstract


          • 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


          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 references 50

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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 physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

            In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality. Copyright 2006 APA.
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              Signal-dependent noise determines motor planning.

              When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical 'two-thirds power law. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.

                Author and article information

                Curr Biol
                Curr. Biol
                Current Biology
                Cell Press
                29 June 2015
                29 June 2015
                : 25
                : 13
                : 1707-1716
                [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@
                © 2015 The Authors

                This is an open access article under the CC BY license (


                Life sciences

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


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