On the neural implementation of the speed-accuracy trade-off

Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This phenomenon is referred to as the speed-accuracy trade-off (SAT). Behavioral studies of the SAT have a long history, and the data from these studies are well characterized within the framework of bounded integration. According to this framework, decision makers accumulate noisy evidence until the running total for one of the alternatives reaches a bound. Lower and higher bounds favor speed and accuracy respectively, each at the expense of the other. Studies addressing the neural implementation of these computations are a recent development in neuroscience. In this review, we describe the experimental and theoretical evidence provided by these studies. We structure the review according to the framework of bounded integration, describing evidence for (1) the modulation of the encoding of evidence under conditions favoring speed or accuracy, (2) the modulation of the integration of encoded evidence, and (3) the modulation of the amount of integrated evidence sufficient to make a choice. We discuss commonalities and differences between the proposed neural mechanisms, some of their assumptions and simplifications, and open questions for future work. We close by offering a unifying hypothesis on the present state of play in this nascent research field.

[1]  Rune W. Berg,et al.  Influence of Phasic and Tonic Dopamine Release on Receptor Activation , 2010, The Journal of Neuroscience.

[2]  Jonathan D. Cohen,et al.  The neural basis of error detection: conflict monitoring and the error-related negativity. , 2004, Psychological review.

[3]  Scott D. Brown,et al.  Cortico-striatal connections predict control over speed and accuracy in perceptual decision making , 2010, Proceedings of the National Academy of Sciences.

[4]  Paul Cisek,et al.  Decision making by urgency gating: theory and experimental support. , 2012, Journal of neurophysiology.

[5]  Philip Holmes,et al.  Rapid decision threshold modulation by reward rate in a neural network , 2006, Neural Networks.

[6]  Leslie G. Ungerleider,et al.  A general mechanism for perceptual decision-making in the human brain , 2004, Nature.

[7]  D. Durstewitz,et al.  Beyond bistability: Biophysics and temporal dynamics of working memory , 2006, Neuroscience.

[8]  Wayne A. Wickelgren,et al.  Speed-accuracy tradeoff and information processing dynamics , 1977 .

[9]  Richard P. Heitz,et al.  Neurally constrained modeling of perceptual decision making. , 2010, Psychological review.

[10]  Marius Usher,et al.  Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[11]  D. Koshland Frontiers in neuroscience. , 1988, Science.

[12]  F. Blankenburg,et al.  Causal Role of Dorsolateral Prefrontal Cortex in Human Perceptual Decision Making , 2011, Current Biology.

[13]  Michael J. Frank,et al.  Hold your horses: A dynamic computational role for the subthalamic nucleus in decision making , 2006, Neural Networks.

[14]  Daniel Durstewitz,et al.  Neural representation of interval time , 2004, Neuroreport.

[15]  Xiao-Jing Wang Neural dynamics and circuit mechanisms of decision-making , 2012, Current Opinion in Neurobiology.

[16]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[17]  M. Shadlen,et al.  Decision-making with multiple alternatives , 2008, Nature Neuroscience.

[18]  Scott D. Brown,et al.  The Optimality of Sensory Processing during the Speed–Accuracy Tradeoff , 2012, The Journal of Neuroscience.

[19]  Jonathan D. Cohen,et al.  The Quarterly Journal of Experimental Psychology Do Humans Produce the Speed–accuracy Trade-off That Maximizes Reward Rate? , 2022 .

[20]  P. Fitts Cognitive aspects of information processing. 3. Set for speed versus accuracy. , 1966, Journal of experimental psychology.

[21]  P. Goldman-Rakic,et al.  Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. , 1989, Journal of neurophysiology.

[22]  Iain D. Gilchrist,et al.  Consistent Implementation of Decisions in the Brain , 2012, PloS one.

[23]  R. H. S. Carpenter,et al.  Neural computation of log likelihood in control of saccadic eye movements , 1995, Nature.

[24]  Richard P. Heitz,et al.  Neural mechanisms of saccade target selection: gated accumulator model of the visual–motor cascade , 2011, The European journal of neuroscience.

[25]  Xiao-Jing Wang Synaptic reverberation underlying mnemonic persistent activity , 2001, Trends in Neurosciences.

[26]  J. Gold,et al.  Banburismus and the Brain Decoding the Relationship between Sensory Stimuli, Decisions, and Reward , 2002, Neuron.

[27]  Hongzhi You,et al.  Trading Speed and Accuracy by Coding Time: A Coupled-circuit Cortical Model , 2013, PLoS Comput. Biol..

[28]  M. Shadlen,et al.  Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task , 2002, The Journal of Neuroscience.

[29]  M. Shadlen,et al.  Decision Making as a Window on Cognition , 2013, Neuron.

[30]  Martin Paré,et al.  Persistent storage capability impairs decision making in a biophysical network model , 2011, Neural Networks.

[31]  R. Poldrack,et al.  Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus , 2006, The Journal of Neuroscience.

[32]  Lars Chittka,et al.  Speed-accuracy tradeoffs in animal decision making. , 2009, Trends in ecology & evolution.

[33]  Philip L. Smith,et al.  Psychology and neurobiology of simple decisions , 2004, Trends in Neurosciences.

[34]  Karl F. Stock,et al.  A COMPUTATIONAL MODEL , 2011 .

[35]  Morten H. Christiansen,et al.  A computational model , 2014 .

[36]  Peter Redgrave,et al.  A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour , 2001, Biological Cybernetics.

[37]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[38]  Hongzhi You,et al.  Dynamics of Multiple-Choice Decision Making , 2013, Neural Computation.

[39]  J. Lygeros,et al.  Decision Making I , 2014 .

[40]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[41]  Xiao-Jing Wang Decision Making in Recurrent Neuronal Circuits , 2008, Neuron.

[42]  M. Shadlen,et al.  Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque , 1999, Nature Neuroscience.

[43]  A. Grace Phasic versus tonic dopamine release and the modulation of dopamine system responsivity: A hypothesis for the etiology of schizophrenia , 1991, Neuroscience.

[44]  Patrick Simen,et al.  Evidence Accumulator or Decision Threshold – Which Cortical Mechanism are We Observing? , 2012, Front. Psychology.

[45]  K. H. Britten,et al.  Responses of neurons in macaque MT to stochastic motion signals , 1993, Visual Neuroscience.

[46]  Timothy D. Hanks,et al.  Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated by the Environment , 2008, The Journal of Neuroscience.

[47]  A. Nambu Somatotopic Organization of the Primate Basal Ganglia , 2011, Front. Neuroanat..

[48]  R. Bogacz Optimal decision-making theories: linking neurobiology with behaviour , 2007, Trends in Cognitive Sciences.

[49]  E. Miller,et al.  Prospective Coding for Objects in Primate Prefrontal Cortex , 1999, The Journal of Neuroscience.

[50]  Mark F Bear,et al.  Reward timing in the primary visual cortex. , 2006, Science.

[51]  D. Munoz,et al.  Saccadic Probability Influences Motor Preparation Signals and Time to Saccadic Initiation , 1998, The Journal of Neuroscience.

[52]  J. Gold,et al.  Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. , 2012, Cerebral cortex.

[53]  M. Bauer,et al.  Neural Characterization of the Speed–Accuracy Tradeoff in a Perceptual Decision-Making Task , 2011, The Journal of Neuroscience.

[54]  Timothy D. Hanks,et al.  Elapsed Decision Time Affects the Weighting of Prior Probability in a Perceptual Decision Task , 2011, The Journal of Neuroscience.

[55]  M. Shadlen,et al.  The effect of stimulus strength on the speed and accuracy of a perceptual decision. , 2005, Journal of vision.

[56]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[57]  Anders Ledberg,et al.  Neurobiological Models of Two-Choice Decision Making Can Be Reduced to a One-Dimensional Nonlinear Diffusion Equation , 2008, PLoS Comput. Biol..

[58]  Masataka Watanabe,et al.  Prefrontal and cingulate unit activity during timing behavior in the monkey , 1979, Brain Research.

[59]  Roger Ratcliff,et al.  Aging and response times: a comparison of sequential sampling models , 2005 .

[60]  Dhanistha Panyasak,et al.  Circuits , 1995, Annals of the New York Academy of Sciences.

[61]  Xiao-Jing Wang,et al.  Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks , 2006, Nature Neuroscience.

[62]  R. Ivry,et al.  The neural representation of time , 2004, Current Opinion in Neurobiology.

[63]  Cameron S. Carter,et al.  The Neural and Computational Basis of Controlled Speed-Accuracy Tradeoff during Task Performance , 2008, Journal of Cognitive Neuroscience.

[64]  M. Dorris,et al.  Role of the Superior Colliculus in Choosing Mixed-Strategy Saccades , 2009, The Journal of Neuroscience.

[65]  Anil Bollimunta,et al.  Local computation of decision-relevant net sensory evidence in parietal cortex. , 2012, Cerebral cortex.

[66]  M. Gallagher Psychology and neurobiology: memory and brain. , 1987, Science.

[67]  L. Abbott,et al.  A model of multiplicative neural responses in parietal cortex. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[68]  Hauke R Heekeren,et al.  Changes in Neural Connectivity Underlie Decision Threshold Modulation for Reward Maximization , 2012, The Journal of Neuroscience.

[69]  P. Glimcher,et al.  The Neurobiology of Decision: Consensus and Controversy , 2009, Neuron.

[70]  Braden A. Purcell,et al.  From Salience to Saccades: Multiple-Alternative Gated Stochastic Accumulator Model of Visual Search , 2012, The Journal of Neuroscience.

[71]  Refractor Vision , 2000, The Lancet.

[72]  J. Assad,et al.  A cognitive signal for the proactive timing of action in macaque LIP , 2006, Nature Neuroscience.

[73]  Xiao-Jing Wang,et al.  Erratum to: Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition , 2014, Journal of Computational Neuroscience.

[74]  Scott D. Brown,et al.  Neural Correlates of Trial-to-Trial Fluctuations in Response Caution , 2011, The Journal of Neuroscience.

[75]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[76]  James V. Stone Using reaction times and binary responses to estimate psychophysical performance: an information theoretic analysis , 2014, Front. Neurosci..

[77]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[78]  J. Serences,et al.  Adaptive Allocation of Attentional Gain , 2009, The Journal of Neuroscience.

[79]  Richard P. Heitz,et al.  Cooperation and Competition among Frontal Eye Field Neurons during Visual Target Selection , 2010, The Journal of Neuroscience.

[80]  K. R. Ridderinkhof,et al.  Striatum and pre-SMA facilitate decision-making under time pressure , 2008, Proceedings of the National Academy of Sciences.

[81]  KongFatt Wong-Lin,et al.  Neural Circuit Dynamics Underlying Accumulation of Time-Varying Evidence During Perceptual Decision Making , 2007, Frontiers Comput. Neurosci..

[82]  Larissa Albantakis,et al.  The encoding of alternatives in multiple-choice decision-making , 2009, Proceedings of the National Academy of Sciences.

[83]  Richard P. Heitz,et al.  Neural Mechanisms of Speed-Accuracy Tradeoff , 2012, Neuron.

[84]  R. Carpenter,et al.  The influence of urgency on decision time , 2000, Nature Neuroscience.

[85]  P. Cisek,et al.  Decisions in Changing Conditions: The Urgency-Gating Model , 2009, The Journal of Neuroscience.

[86]  T. Ono,et al.  Retrospective and prospective coding for predicted reward in the sensory thalamus , 2001, Nature.

[87]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[88]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[89]  M. Paré,et al.  Temporal processing of saccade targets in parietal cortex area LIP during visual search. , 2007, Journal of neurophysiology.

[90]  Jochen Ditterich Stochastic models of decisions about motion direction: behavior and physiology , 2006 .

[91]  Jochen Ditterich,et al.  Stochastic models of decisions about motion direction: Behavior and physiology , 2006, Neural Networks.

[92]  J. Fuster Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. , 1973, Journal of neurophysiology.

[93]  Wulfram Gerstner,et al.  Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking , 2000, Neural Computation.

[94]  R. Marois,et al.  fMRI Evidence for a Dual Process Account of the Speed-Accuracy Tradeoff in Decision-Making , 2008, PloS one.

[95]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[96]  Michael J. Frank,et al.  Understanding decision-making deficits in neurological conditions: insights from models of natural action selection , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[97]  A. Pouget,et al.  The Cost of Accumulating Evidence in Perceptual Decision Making , 2012, The Journal of Neuroscience.

[98]  R. Bogacz,et al.  The neural basis of the speed–accuracy tradeoff , 2010, Trends in Neurosciences.

[99]  Xiao-Jing Wang,et al.  A Recurrent Network Mechanism of Time Integration in Perceptual Decisions , 2006, The Journal of Neuroscience.

[100]  Xiao-Jing Wang,et al.  Similarity Effect and Optimal Control of Multiple-Choice Decision Making , 2008, Neuron.

[101]  R. Miall,et al.  Distinct systems for automatic and cognitively controlled time measurement: evidence from neuroimaging , 2003, Current Opinion in Neurobiology.

[102]  Jeffrey D. Schall,et al.  Neural basis of deciding, choosing and acting , 2001, Nature Reviews Neuroscience.

[103]  J. Ditterich Evidence for time‐variant decision making , 2006, The European journal of neuroscience.

[104]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.