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Decision-making in sensorimotor control

Nature Reviews Neuroscience volume 19, pages 519–534 (2018)Cite this article

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Abstract

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

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Fig. 1: Corrective motor responses are tuned to task features.
Fig. 2: Multiple potential actions can be specified before target selection.
Fig. 3: Rapid switching between sensorimotor decisions on the basis of context.
Fig. 4: Evidence for changes of mind.
Fig. 5: Optimized sensorimotor decisions for sequences of actions.

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References

  1. Woodworth, R. S. The accuracy of voluntary movement. Psychol.Rev. 3, i114 (1899).

    Google Scholar 

  2. Jeannerod, M. The Neural and Behavioural Organization of Goal-directed Movements. (Oxford Univ. Press, Oxford, 1990).

    Google Scholar 

  3. Rosenbaum, D. A. in Human Motor Control. 79–118 (Academic Press, Cambridge, 1991).

    Book  Google Scholar 

  4. Glover, S. Separate visual representations in the planning and control of action. Behav. Brain Sci. 27, 3–24 (2004). discussion 24–78.

    PubMed  Google Scholar 

  5. Harris, C. M. & Wolpert, D. M. Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998). This paper provides a unifying theory of eye and arm movements — the minimum variance theory — which posits that the CNS selects movement trajectories that minimize post-movement variance in the presence of signal-dependent noise in the neural control signal.

    Article  PubMed  CAS  Google Scholar 

  6. Flash, T. & Hogan, N. The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985).

    Article  PubMed  CAS  Google Scholar 

  7. Neyedli, H. F. & Welsh, T. N. Optimal weighting of costs and probabilities in a risky motor decision-making task requires experience. J. Exp. Psychol. Hum. Percept. Perform. 39, 638–645 (2013).

    Article  PubMed  Google Scholar 

  8. Trommershäuser, J., Landy, M. & Maloney, L. Statistical decision theory and trade-offs in the control of motor response. Spat. Vis. 16, 255–275 (2003).

    Article  PubMed  Google Scholar 

  9. Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002). This paper introduces the OFC framework as a theory for understanding motor control.

    Article  PubMed  CAS  Google Scholar 

  10. Scott, S. H. Optimal feedback control and the neural basis of volitional motor control. Nat. Rev. Neurosci. 5, 532–546 (2004).

    Article  PubMed  CAS  Google Scholar 

  11. Todorov, E. Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Crevecoeur, F. & Scott, S. H. Beyond muscles stiffness: importance of state-estimation to account for very fast motor corrections. PLoS Comput. Biol. 10, e1003869 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Cluff, T. & Scott, S. H. Apparent and actual trajectory control depend on the behavioral context in upper limb motor tasks. J. Neurosci. 35, 12465–12476 (2015).

    Article  PubMed  CAS  Google Scholar 

  15. Nashed, J. Y., Crevecoeur, F. & Scott, S. H. Influence of the behavioral goal and environmental obstacles on rapid feedback responses. J. Neurophysiol. 108, 999–1009 (2012).

    Article  PubMed  Google Scholar 

  16. Reichenbach, A., Franklin, D. W., Zatka-Haas, P. & Diedrichsen, J. A dedicated binding mechanism for the visual control of movement. Curr. Biol. 24, 780–785 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. de Brouwer, A. J., Jarvis, T., Gallivan, J. P. & Flanagan, J. R. Parallel specification of visuomotor feedback gains during bimanual reaching to independent goals. eNeuro 4, ENEURO.0026-17.2017 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nashed, J. Y., Kurtzer, I. L. & Scott, S. H. Context-dependent inhibition of unloaded muscles during the long-latency epoch. J. Neurophysiol. 113, 192–202 (2015).

    Article  PubMed  Google Scholar 

  19. Pruszynski, J. A., Johansson, R. S. & Flanagan, J. R. A. Rapid tactile-motor reflex automatically guides reaching toward handheld objects. Curr. Biol. 26, 788–792 (2016). This study describes a rapid tactile-motor reflex in which spatial information about a change in target location provided by tactile feedback in one hand can elicit a fast correction of the opposite hand’s trajectory to reach the target.

    Article  PubMed  CAS  Google Scholar 

  20. Pruszynski, J. A. & Scott, S. H. Optimal feedback control and the long-latency stretch response. Exp. Brain Res. 218, 341–359 (2012).

    Article  PubMed  Google Scholar 

  21. Bedingham, W. & Tatton, W. G. Dependence of EMG responses evoked by imposed wrist displacements on pre-existing activity in the stretched muscles. Can. J. Neurol. Sci. 11, 272–280 (1984).

    Article  PubMed  CAS  Google Scholar 

  22. Kurtzer, I., Pruszynski, J. A. & Scott, S. H. Long-latency responses during reaching account for the mechanical interaction between the shoulder and elbow joints. J. Neurophysiol. 102, 3004–3015 (2009).

    Article  PubMed  Google Scholar 

  23. Kurtzer, I. L., Pruszynski, J. A. & Scott, S. H. Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Curr. Biol. 18, 449–453 (2008).

    Article  PubMed  CAS  Google Scholar 

  24. Diamond, J. S., Nashed, J. Y., Johansson, R. S., Wolpert, D. M. & Flanagan, J. R. Rapid visuomotor corrective responses during transport of hand-held objects incorporate novel object dynamics. J. Neurosci. 35, 10572–10580 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Crevecoeur, F., Kurtzer, I. & Scott, S. H. Fast corrective responses are evoked by perturbations approaching the natural variability of posture and movement tasks. J. Neurophysiol. 107, 2821–2832 (2012).

    Article  PubMed  CAS  Google Scholar 

  26. Dimitriou, M., Franklin, D. W. & Wolpert, D. M. Task-dependent coordination of rapid bimanual motor responses. J. Neurophysiol. 107, 890–901 (2012).

    Article  PubMed  Google Scholar 

  27. Mutha, P. K. & Sainburg, R. L. Shared bimanual tasks elicit bimanual reflexes during movement. J. Neurophysiol. 102, 3142–3155 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Burkitt, J. J., Grierson, L. E. M., Staite, V., Elliott, D. & Lyons, J. The impact of prior knowledge about visual feedback on motor performance and learning. Adv. Phys. Educ. 3, 1–9 (2013).

    Article  Google Scholar 

  29. Hansen, S., Glazebrook, C. M., Anson, J. G., Weeks, D. J. & Elliott, D. The influence of advance information about target location and visual feedback on movement planning and execution. Can. J. Exp. Psychol. 60, 200–208 (2006).

    Article  PubMed  Google Scholar 

  30. Khan, M. A., Elliot, D., Coull, J., Chua, R. & Lyons, J. Optimal control strategies under different feedback schedules: kinematic evidence. J. Mot. Behav. 34, 45–57 (2002).

    Article  PubMed  Google Scholar 

  31. Elliott, D. et al. Goal-directed aiming: two components but multiple processes. Psychol. Bull. 136, 1023–1044 (2010).

    Article  PubMed  Google Scholar 

  32. Elliott, D. et al. The multiple process model of goal-directed reaching revisited. Neurosci. Biobehav. Rev. 72, 95–110 (2017).

    Article  PubMed  Google Scholar 

  33. Brenner, E. & Smeets, J. B. J. Fast corrections of movements with a computer mouse. Spat. Vis. 16, 365–376 (2003).

    Article  PubMed  Google Scholar 

  34. Saunders, J. A. & Knill, D. C. Humans use continuous visual feedback from the hand to control fast reaching movements. Exp. Brain Res. 152, 341–352 (2003).

    Article  PubMed  Google Scholar 

  35. Dimitriou, M., Wolpert, D. M. & Franklin, D. W. The temporal evolution of feedback gains rapidly update to task demands. J. Neurosci. 33, 10898–10909 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Saunders, J. A. & Knill, D. C. Visual feedback control of hand movements. J. Neurosci. 24, 3223–3234 (2004). This study uses visual perturbations to examine the relative contributions of motion and position information on feedback control and shows that both forms of visual feedback control operate continuously during target-directed reaching.

    Article  PubMed  CAS  Google Scholar 

  37. Franklin, D. W., Reichenbach, A., Franklin, S. & Diedrichsen, J. Temporal evolution of spatial computations for visuomotor control. J. Neurosci. 36, 2329–2341 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. McClelland, J. L. On the time relations of mental processes: an examination of systems of processes in cascade. Psychol. Rev. 86, 287–330 (1979).

    Article  Google Scholar 

  39. Miller, G. A., Galanter, E. & Pribram, K. H. Plans and the structure of behavior. (Holt, Rinehart and Winston, Inc, New York, 1960).

    Book  Google Scholar 

  40. Cisek, P. Cortical mechanisms of action selection: the affordance competition hypothesis. Phil. Trans. R. Soc. B 362, 1585–1599 (2007). This influential paper provides a theoretical framework for interpreting the results of several neurophysiological and behavioural studies. In contrast to serial models of planning that argue that target selection precedes action specification, this theory proposes that both target selection and action specification operate continuously and in parallel.

    Article  PubMed  Google Scholar 

  41. Howard, L. A. & Tipper, S. P. Hand deviations away from visual cues: indirect evidence for inhibition. Exp. Brain Res. 113, 144–152 (1997).

    Article  PubMed  CAS  Google Scholar 

  42. Tipper, S. P., Howard, L. A. & Jackson, S. R. Selective reaching to grasp: evidence for distractor interference effects. Vis. Cogn. 4, 1–38 (1997).

    Article  Google Scholar 

  43. Welsh, T. N., Elliott, D. & Weeks, D. J. Hand deviations toward distractors. Evidence for response competition. Exp. Brain Res. 127, 207–212 (1999).

    Article  PubMed  CAS  Google Scholar 

  44. Welsh, T. N. & Elliott, D. Movement trajectories in the presence of a distracting stimulus: evidence for a response activation model of selective reaching. Q. J. Exp. Psychol. A 57, 1031–1057 (2004).

    Article  PubMed  Google Scholar 

  45. Cisek, P. & Kalaska, J. F. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45, 801–814 (2005). This paper shows, using neural recordings, that the primate motor system, when presented with two reaching options, simultaneously represents both reach options before later reflecting the selection between them.

    Article  PubMed  CAS  Google Scholar 

  46. Coallier, É., Michelet, T. & Kalaska, J. F. Dorsal premotor cortex: neural correlates of reach target decisions based on a color-location matching rule and conflicting sensory evidence. J. Neurophysiol. 113, 3543–3573 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cui, H. & Andersen, R. A. Different representations of potential and selected motor plans by distinct parietal areas. J. Neurosci. 31, 18130–18136 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Dekleva, B. M., Ramkumar, P., Wanda, P. A., Kording, K. P. & Miller, L. E. Uncertainty leads to persistent effects on reach representations in dorsal premotor cortex. eLife 5, e14316 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Grent-’t-Jong, T., Oostenveld, R., Medendorp, W. P. & Praamstra, P. Separating visual and motor components of motor cortex activation for multiple reach targets: a visuomotor adaptation study. J. Neurosci. 35, 15135–15144 (2015).

    Article  PubMed  CAS  Google Scholar 

  50. Klaes, C., Westendorff, S., Chakrabarti, S. & Gail, A. Choosing goals, not rules: deciding among rule-based action plans. Neuron 70, 536–548 (2011).

    Article  PubMed  CAS  Google Scholar 

  51. Gibson, J. J. The Ecological Approach to Visual Perception: Classic Edition. (Psychology Press, London, 2014).

    Book  Google Scholar 

  52. Cisek, P. & Kalaska, J. F. Neural mechanisms for interacting with a world full of action choices. Annu. Rev. Neurosci. 33, 269–298 (2010).

    Article  PubMed  CAS  Google Scholar 

  53. Pesaran, B., Nelson, M. J. & Andersen, R. A. Dorsal premotor neurons encode the relative position of the hand, eye, and goal during reach planning. Neuron 51, 125–134 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Pearce, T. M. & Moran, D. W. Strategy-dependent encoding of planned arm movements in the dorsal premotor cortex. Science 337, 984–988 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Munoz, D. P. & Wurtz, R. H. Saccade-related activity in monkey superior colliculus. II. Spread of activity during saccades. J. Neurophysiol. 73, 2334–2348 (1995).

    Article  PubMed  CAS  Google Scholar 

  56. Basso, M. A. & Wurtz, R. H. Modulation of neuronal activity by target uncertainty. Nature 389, 66–69 (1997).

    Article  PubMed  CAS  Google Scholar 

  57. Platt, M. L. & Glimcher, P. W. Responses of intraparietal neurons to saccadic targets and visual distractors. J. Neurophysiol. 78, 1574–1589 (1997).

    Article  PubMed  CAS  Google Scholar 

  58. McPeek, R. M. Competition between saccade goals in the superior colliculus produces saccade curvature. J. Neurophysiol. 89, 2577–2590 (2003).

    Article  PubMed  Google Scholar 

  59. Bhutani, N. et al. Queuing of concurrent movement plans by basal ganglia. J. Neurosci. 33, 9985–9997 (2013).

    Article  PubMed  CAS  Google Scholar 

  60. Costello, M. G., Zhu, D., Salinas, E. & Stanford, T. R. Perceptual modulation of motor — but not visual — responses in the frontal eye field during an urgent-decision task. J. Neurosci. 33, 16394–16408 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Franklin, D. W. & Wolpert, D. M. Computational mechanisms of sensorimotor control. Neuron 72, 425–442 (2011).

    Article  PubMed  CAS  Google Scholar 

  62. Gottlieb, J. Attention, learning, and the value of information. Neuron 76, 281–295 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Tipper, S. P., Howard, L. A. & Houghton, G. in Control of Cognitive Processes: Attention and Performance XVIII (eds. Monsell, S. & Driver J.) 223–245 (MIT, Cambridge, 1999).

    Google Scholar 

  64. Gallivan, J. P. et al. One to four, and nothing more: nonconscious parallel individuation of objects during action planning. Psychol. Sci. 22, 803–811 (2011).

    Article  PubMed  Google Scholar 

  65. Gallivan, J. P., Logan, L., Wolpert, D. M. & Flanagan, J. R. Parallel specification of competing sensorimotor control policies for alternative action options. Nat. Neurosci. 19, 320–326 (2016).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  66. Stewart, B. M., Baugh, L. A., Gallivan, J. P. & Flanagan, J. R. Simultaneous encoding of the direction and orientation of potential targets during reach planning: evidence of multiple competing reach plans. J. Neurophysiol. 110, 807–816 (2013).

    Article  PubMed  Google Scholar 

  67. Stewart, B. M., Gallivan, J. P., Baugh, L. A. & Flanagan, J. R. Motor, not visual, encoding of potential reach targets. Curr. Biol. 24, R953–954 (2014).

    Article  PubMed  CAS  Google Scholar 

  68. Haith, A. M., Pakpoor, J. & Krakauer, J. W. Independence of movement preparation and movement initiation. J. Neurosci. 36, 3007–3015 (2016).

    Article  PubMed  CAS  Google Scholar 

  69. Wong, A. L. & Haith, A. M. Motor planning flexibly optimizes performance under uncertainty about task goals. Nat. Commun. 8, 14624 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Chapman, C. S. et al. Reaching for the unknown: multiple target encoding and real-time decision-making in a rapid reach task. Cognition 116, 168–176 (2010).

    Article  PubMed  Google Scholar 

  71. Haith, A. M., Huberdeau, D. M. & Krakauer, J. W. Hedging your bets: intermediate movements as optimal behavior in the context of an incomplete decision. PLoS Comput. Biol. 11, e1004171 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Hudson, T. E., Maloney, L. T. & Landy, M. S. Movement planning with probabilistic target information. J. Neurophysiol. 98, 3034–3046 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Christopoulos, V. & Schrater, P. R. Dynamic integration of value information into a common probability currency as a theory for flexible decision making. PLoS Comput. Biol. 11, e1004402 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Christopoulos, V., Bonaiuto, J. & Andersen, R. A. A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives. PLoS Comput. Biol. 11, e1004104 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Ghez, C. et al. Discrete and continuous planning of hand movements and isometric force trajectories. Exp. Brain Res. 115, 217–233 (1997).

    Article  PubMed  CAS  Google Scholar 

  76. Gallivan, J. P., Stewart, B. M., Baugh, L. A., Wolpert, D. M. & Flanagan, J. R. Rapid automatic motor encoding of competing reach options. Cell Rep. 18, 1619–1626 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  77. Nashed, J. Y., Diamond, J. S., Gallivan, J. P., Wolpert, D. M. & Flanagan, J. R. Grip force when reaching with target uncertainty provides evidence for motor optimization over averaging. Sci. Rep. 7, 11703 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Chapman, C. S., Gallivan, J. P., Wong, J. D., Wispinski, N. J. & Enns, J. T. The snooze of lose: rapid reaching reveals that losses are processed more slowly than gains. J. Exp. Psychol. Gen. 144, 844–863 (2015).

    Article  PubMed  Google Scholar 

  79. Welsh, T. N. The relationship between attentional capture and deviations in movement trajectories in a selective reaching task. Acta Psychol. 137, 300–308 (2011).

    Article  Google Scholar 

  80. Pastor-Bernier, A. & Cisek, P. Neural correlates of biased competition in premotor cortex. J. Neurosci. 31, 7083–7088 (2011).

    Article  PubMed  CAS  Google Scholar 

  81. Cisek, P. & Pastor-Bernier, A. On the challenges and mechanisms of embodied decisions. Phil. Trans. R. Soc. B 369, 20130479–20130479 (2014).

    Article  PubMed  Google Scholar 

  82. Cos, I., Bélanger, N. & Cisek, P. The influence of predicted arm biomechanics on decision making. J. Neurophysiol. 105, 3022–3033 (2011).

    Article  PubMed  Google Scholar 

  83. Cos, I., Medleg, F. & Cisek, P. The modulatory influence of end-point controllability on decisions between actions. J. Neurophysiol. 108, 1764–1780 (2012).

    Article  PubMed  Google Scholar 

  84. Cos, I., Duque, J. & Cisek, P. Rapid prediction of biomechanical costs during action decisions. J. Neurophysiol. 112, 1256–1266 (2014). This study shows, using transcranial magnetic stimulation over motor cortex, that the prediction of the effort associated with competing reach options is computed rapidly and influences reach decisions within 200 ms of the presentation of those options.

    Article  PubMed  Google Scholar 

  85. Shadmehr, R., Huang, H. J. & Ahmed, A. A. A. Representation of effort in decision-making and motor control. Curr. Biol. 26, 1929–1934 (2016).

    Article  PubMed  CAS  Google Scholar 

  86. Morel, P., Ulbrich, P. & Gail, A. What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control. PLOS Biol. 15, e2001323 (2017). This study systematically determined the internal cost function for choosing between effortful movements and showed, using force-loaded reaches, that the effort cost associated with decision-making was similar to the cost functions in motor control. This supports the notion that motor control and decision-making are governed by similar optimization principles.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Suriya-Arunroj, L. & Gail, A. I. Plan therefore i choose: free-choice bias due to prior action-probability but not action-value. Front. Behav. Neurosci. 9, 315 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Gallivan, J. P., Barton, K. S., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. Action plan co-optimization reveals the parallel encoding of competing reach movements. Nat. Commun. 6, 7428 (2015). This paper shows, using a reaching task in which target-directed movements were executed only after one of two potential targets was cued, that the reach trajectory selected for the cued target often shares components of the movement that would have been required for the other (uncued) target, suggesting that movement specification often precedes target selection.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Gallivan, J. P., Bowman, N. A. R., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory. J. Neurophysiol. 115, 3113–3122 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Nashed, J. Y., Crevecoeur, F. & Scott, S. H. Rapid online selection between multiple motor plans. J. Neurosci. 34, 1769–1780 (2014). This study uses mechanical perturbations to reveal the remarkable speed of sensorimotor decision-making during target-directed reaching and shows that corrective responses associated with changes in the path towards a spatial target are expressed earlier (at ~60 ms) than corrective responses associated with changes in what spatial target to attain (at ~75 ms).

    Article  PubMed  CAS  Google Scholar 

  91. Cisek, P. Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26, 9761–9770 (2006).

    Article  PubMed  CAS  Google Scholar 

  92. Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Wang, X.-J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    Article  PubMed  CAS  Google Scholar 

  95. Schall, J. D. Neural basis of deciding, choosing and acting. Nat. Rev. Neurosci. 2, 33–42 (2001).

    Article  PubMed  CAS  Google Scholar 

  96. Sugrue, L. P., Corrado, G. S. & Newsome, W. T. Choosing the greater of two goods: neural currencies for valuation and decision making. Nat. Rev. Neurosci. 6, 363–375 (2005).

    Article  PubMed  CAS  Google Scholar 

  97. Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002). This influential paper examines the neural correlates of gradual decision formation during a random dot motion task and shows that the lateral intraparietal area accumulates sensory evidence for or against a behavioural response (in this case, an eye movement).

    Article  PubMed  CAS  Google Scholar 

  98. Song, J. H. & Nakayama, K. Hidden cognitive states revealed in choice reaching tasks. Trends Cogn. Sci. 13, 360–366 (2009).

    Article  PubMed  Google Scholar 

  99. Selen, L. P., Shadlen, M. N. & Wolpert, D. M. Deliberation in the motor system: reflex gains track evolving evidence leading to a decision. J. Neurosci. 32, 2276–2286 (2012). This study shows, by eliciting reflex responses via mechanical perturbations at different times during a decision task, that the state of the motor system dynamically tracks the accumulated evidence in favour of one movement versus another. This result suggests the continuous flow of an evolving decision to the motor system.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Gold, J. I. & Shadlen, M. N. Representation of a perceptual decision in developing oculomotor commands. Nature 404, 390–394 (2000).

    Article  PubMed  CAS  Google Scholar 

  101. Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008).

    Article  PubMed  CAS  Google Scholar 

  102. Resulaj, A., Kiani, R., Wolpert, D. M. & Shadlen, M. N. Changes of mind in decision-making. Nature 461, 263–266 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex. eLife 4, e04677 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Kiani, R., Cueva, C. J., Reppas, J. B. & Newsome, W. T. Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials. Curr. Biol. 24, 1542–1547 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Hagura, N., Haggard, P. & Diedrichsen, J. Perceptual decisions are biased by the cost to act. eLife 6, e18422 (2017). This study shows that perceptual decisions can be biased by imperceptibly changing the physical resistances of reaching movements used to report those decisions. This finding suggests that the motor system, rather than simply conveying an upstream decision, can recursively influence how sensory information is processed and transformed into a decision variable.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Burk, D., Ingram, J. N., Franklin, D. W., Shadlen, M. N. & Wolpert, D. M. Motor effort alters changes of mind in sensorimotor decision making. PLoS ONE 9, e92681 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Moher, J. & Song, J. H. Perceptual decision processes flexibly adapt to avoid change-of-mind motor costs. J. Vis. 14, 1 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Proffitt, D. R., Stefanucci, J., Banton, T. & Epstein, W. The role of effort in perceiving distance. Psychol. Sci. 14, 106–112 (2003).

    Article  PubMed  Google Scholar 

  109. Ramenzoni, V. C., Riley, M. A., Shockley, K. & Davis, T. Short article: Carrying the height of the world on your ankles: encumbering observers reduces estimates of how high an actor can jump. Q. J. Exp. Psychol. 61, 1487–1495 (2008).

    Article  Google Scholar 

  110. Tipper, S. P., Meegan, D. & Howard, L. A. Action-centred negative priming: Evidence for reactive inhibition. Vis. Cogn. 9, 591–614 (2002).

    Article  Google Scholar 

  111. Bhalla, M. & Proffitt, D. R. Visual–motor recalibration in geographical slant perception. J. Exp. Psychol. Hum. Percept. Perform. 25, 1076–1096 (1999).

    Article  PubMed  CAS  Google Scholar 

  112. Kiani, R. & Shadlen, M. N. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009). This study shows that the same population of neurons that encode formation of a decision also represent the degree of certainty associated with the decision.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Fetsch, C. R., Kiani, R. & Shadlen, M. N. Predicting the accuracy of a decision: a neural mechanism of confidence. Cold Spring Harb. Symp. Quant. Biol. 79, 185–197 (2014).

    Article  PubMed  Google Scholar 

  114. Fetsch, C. R., Kiani, R., Newsome, W. T. & Shadlen, M. N. Effects of cortical microstimulation on confidence in a perceptual decision. Neuron 83, 797–804 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Kiani, R., Corthell, L. & Shadlen, M. N. Choice certainty is informed by both evidence and decision time. Neuron 84, 1329–1342 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. van den Berg, R. et al. A common mechanism underlies changes of mind about decisions and confidence. eLife 5, e12192 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  117. van den Berg, R., Zylberberg, A., Kiani, R., Shadlen, M. N. & Wolpert, D. M. Confidence is the bridge between multi-stage decisions. Curr. Biol. 26, 3157–3168 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Johansson, R. S. & Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).

    Article  PubMed  CAS  Google Scholar 

  119. Diedrichsen, J. & Kornysheva, K. Motor skill learning between selection and execution. Trends Cogn. Sci. 19, 227–233 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Wiener, J. M., Ehbauer, N. N. & Mallot, H. A. Planning paths to multiple targets: memory involvement and planning heuristics in spatial problem solving. Psychol. Res. 73, 644–658 (2009).

    Article  PubMed  CAS  Google Scholar 

  121. MacGregor, J. N. & Ormerod, T. Human performance on the traveling salesman problem. Percept. Psychophys. 58, 527–539 (1996).

    Article  PubMed  CAS  Google Scholar 

  122. Vickers, D., Butavicius, M., Lee, M. & Medvedev, A. Human performance on visually presented Traveling Salesman problems. Psychol. Res. 65, 34–45 (2001).

    Article  PubMed  CAS  Google Scholar 

  123. Diamond, J. S., Wolpert, D. M. & Flanagan, J. R. Rapid target foraging with reach or gaze: The hand looks further ahead than the eye. PLoS Comput. Biol. 13, e1005504 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Charnov, E. L. Optimal foraging, the marginal value theorem. Theor. Popul. Biol. 9, 129–136 (1976).

    Article  PubMed  CAS  Google Scholar 

  125. Stephens, D. W. & Krebs, J. R. Foraging Theory. (Princeton Univ. Press, Princeton, 1986).

    Google Scholar 

  126. Baldauf, D. & Deubel, H. Attentional landscapes in reaching and grasping. Vision Res. 50, 999–1013 (2010).

    Article  PubMed  Google Scholar 

  127. Pfeiffer, B. E. & Foster, D. J. Hippocampal place-cell sequences depict future paths to remembered goals. Nature 497, 74–79 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Moser, E. I., Kropff, E. & Moser, M.-B. Place cells, grid cells, and the brain’s spatial representation system. Annu. Rev. Neurosci. 31, 69–89 (2008).

    Article  PubMed  CAS  Google Scholar 

  129. Najemnik, J. & Geisler, W. S. Optimal eye movement strategies in visual search. Nature 434, 387–391 (2005).

    Article  PubMed  CAS  Google Scholar 

  130. Yang, S. C., Lengyel, M. & Wolpert, D. M. Active sensing in the categorization of visual patterns. eLife 5, e12215 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Mazzoni, P. & Krakauer, J. W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26, 3642–3645 (2006).

    Article  PubMed  CAS  Google Scholar 

  132. Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34, 3023–3032 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Fernandez-Ruiz, J., Wong, W., Armstrong, I. T. & Flanagan, J. R. Relation between reaction time and reach errors during visuomotor adaptation. Behav. Brain Res. 219, 8–14 (2011).

    Article  PubMed  Google Scholar 

  134. Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  137. Shadlen, M. N. & Kiani, R. Decision making as a window on cognition. Neuron 80, 791–806 (2013).

    Article  PubMed  CAS  Google Scholar 

  138. Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015). This paper records neuronal activity from six cortical regions while monkeys make decisions on the motion or colour of stimuli and shows that sensorimotor choices emerge in a frontoparietal network.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Gallivan, J. P., Cant, J. S., Goodale, M. A. & Flanagan, J. R. Representation of object weight in human ventral visual cortex. Curr. Biol. 24, 1866–1873 (2014).

    Article  PubMed  CAS  Google Scholar 

  140. Leone, F. T. M., Heed, T., Toni, I. & Medendorp, W. P. Understanding effector selectivity in human posterior parietal cortex by combining information patterns and activation measures. J. Neurosci. 34, 7102–7112 (2014).

    Article  PubMed  CAS  Google Scholar 

  141. Kornysheva, K. & Diedrichsen, J. Human premotor areas parse sequences into their spatial and temporal features. eLife 3, e03043 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Fabbri, S., Stubbs, K. M., Cusack, R. & Culham, J. C. Disentangling representations of object and grasp properties in the human brain. J. Neurosci. 36, 7648–7662 (2016).

    Article  PubMed  CAS  Google Scholar 

  143. Zheng, Z. Z. et al. Multivoxel patterns reveal functionally differentiated networks underlying auditory feedback processing of speech. J. Neurosci. 33, 4339–4348 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Formisano, E., De Martino, F., Bonte, M. & Goebel, R. ‘Who’ is saying ‘what’? Brain-based decoding of human voice and speech. Science 322, 970–973 (2008).

    Article  PubMed  CAS  Google Scholar 

  145. Polyn, S. M. Category-specific cortical activity precedes retrieval during memory search. Science 310, 1963–1966 (2005).

    Article  PubMed  CAS  Google Scholar 

  146. Tong, F. & Pratte, M. S. Decoding patterns of human brain activity. Annu. Rev. Psychol. 63, 483–509 (2012).

    Article  PubMed  Google Scholar 

  147. Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014).

    Article  PubMed  CAS  Google Scholar 

  148. Rissman, J. & Wagner, A. D. Distributed representations in memory: insights from functional brain imaging. Annu. Rev. Psychol. 63, 101–128 (2012).

    Article  PubMed  Google Scholar 

  149. Gallivan, J. P. & Chapman, C. S. Three-dimensional reach trajectories as a probe of real-time decision-making between multiple competing targets. Front. Neurosci. 8, 215 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Freeman, J. B., Dale, R. & Farmer, T. A. Hand in motion reveals mind in motion. Front. Psychol. 2, 59 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Koop, G. J. & Johnson, J. G. The response dynamics of preferential choice. Cogn. Psychol. 67, 151–185 (2013).

    Article  PubMed  Google Scholar 

  152. Scherbaum, S., Dshemuchadse, M., Fischer, R. & Goschke, T. How decisions evolve: the temporal dynamics of action selection. Cognition 115, 407–416 (2010).

    Article  PubMed  Google Scholar 

  153. Freeman, J. B., Nakayama, K. & Ambady, N. Finger in flight reveals parallel categorization across multiple social dimensions. Soc. Cogn. 31, 792–805 (2013).

    Article  Google Scholar 

  154. Spivey, M. J. & Dale, R. Continuous dynamics in real-time cognition. Curr. Dir. Psychol. Sci. 15, 207–211 (2006).

    Article  Google Scholar 

  155. Welsh, T. N. & Elliott, D. The effects of response priming on the planning and execution of goal-directed movements in the presence of a distracting stimulus. Acta Psychol. 119, 123–142 (2005).

    Article  Google Scholar 

  156. Wood, D. K. et al. Visual salience dominates early visuomotor competition in reaching behavior. J. Vis. 11, 16 (2011).

    Article  PubMed  Google Scholar 

  157. Chapman, C. S. et al. Counting on the motor system: rapid action planning reveals the format- and magnitude-dependent extraction of numerical quantity. J. Vis. 14, 30 (2014).

    Article  PubMed  Google Scholar 

  158. Dotan, D. & Dehaene, S. How do we convert a number into a finger trajectory? Cognition 129, 512–529 (2013).

    Article  PubMed  Google Scholar 

  159. Santens, S., Goossens, S. & Verguts, T. Distance in motion: response trajectories reveal the dynamics of number comparison. PLoS ONE 6, e25429 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  160. Song, J.-H. & Nakayama, K. Numeric comparison in a visually-guided manual reaching task. Cognition 106, 994–1003 (2008). This study provides a particularly elegant example of the ways in which top-down cognitive information can influence reach movement trajectories.

    Article  PubMed  Google Scholar 

  161. Pesquita, A., Chapman, C. S. & Enns, J. T. Humans are sensitive to attention control when predicting others’ actions. Proc. Natl Acad. Sci. USA 113, 8669–8674 (2016).

    Article  PubMed  CAS  Google Scholar 

  162. Rotman, G., Troje, N. F., Johansson, R. S. & Flanagan, J. R. Eye movements when observing predictable and unpredictable actions. J. Neurophysiol. 96, 1358–1369 (2006).

    Article  PubMed  Google Scholar 

  163. Vaziri-Pashkam, M., Cormiea, S. & Nakayama, K. Predicting actions from subtle preparatory movements. Cognition 168, 65–75 (2017).

    Article  PubMed  Google Scholar 

  164. Scott, S. H. The computational and neural basis of voluntary motor control and planning. Trends Cogn. Sci. 16, 541–549 (2012).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Institute of Health Research, the Canadian Foundation for Innovation, the Wellcome Trust and the Royal Society Noreen Murray Professorship in Neurobiology (to D.M.W.).

Reviewer information

Nature Reviews Neuroscience thanks A. Gail, T. Welsh and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Centre for Neuroscience Studies and Department of Psychology, Queen’s University, Kingston, Ontario, Canada

    Jason P. Gallivan & J. Randall Flanagan

  2. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, Ontario, Canada

    Jason P. Gallivan

  3. Faculty of Kinesiology, Sport, and Recreation and Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada

    Craig S. Chapman

  4. Department of Engineering, University of Cambridge, Cambridge, UK

    Daniel M. Wolpert

  5. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA

    Daniel M. Wolpert

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Contributions

J.P.G., C.S.C., D.M.W. and J.R.F. researched data for the article, made a substantial contribution to the discussion of content, wrote the article and reviewed and/or edited the manuscript before submission.

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Glossary

Movement trajectories

A series of arm configurations over time.

Feedforward planning

The specification of specific parameters characterizing an action (such as direction, speed or smoothness) in advance of movement.

Feedback control

The use of error signals, such as the difference between actual and desired movement, in generating a corrective action that minimizes these errors.

Noise

Random or unpredictable fluctuations and disturbances of neural, neuromuscular or environmental origin.

Subprocesses

Processes that are components of a larger process.

Action affordances

Representations of the actions that objects and stimuli in the environment afford at the level of the sensorimotor system.

Saccades

Rapid movements of the eyes that change fixation from one point to another.

Intersegmental dynamics

The interaction torques that arise when the motion of one individual arm segment propagates to the adjacent segments.

Motor representations

The coding of a stimulus and/or associated action in the motor system.

Random dot display

A visual display of moving dots frequently used in perceptual decision-making experiments. Determining the net direction of the dots can be made difficult for the observer by varying the number of dots that are moving in the same direction (coherence) compared with the number of dots that move in random (non-coherent) directions.

Accumulate to bound (drift-diffusion) model

A well-defined model in which evidence is accumulated for one or other choice options at each time step and integrated until a predetermined decision threshold is reached.

Embodied cognition

The theory that many features of cognition are shaped and constrained by the body of the individual.

Wagering tasks

A set of gambling tasks used in psychology to assess an observer’s belief about the outcome of an event or fact.

Race model

A well-defined model in which evidence for each choice option is accumulated separately. A decision is made either when one of the accumulators reaches a predetermined threshold or, when the decision is forced, the decision associated with the accumulator with the highest evidence is selected.

Temporal discounting

The discounting of the value of items or rewards as a function of time, with their value being deemed greater the closer they approach the present time.

Active sensing

An active strategy through which the body’s sensors are directed so as to maximally extract goal-relevant information.

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Gallivan, J.P., Chapman, C.S., Wolpert, D.M. et al. Decision-making in sensorimotor control. Nat Rev Neurosci 19, 519–534 (2018). https://doi.org/10.1038/s41583-018-0045-9

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