Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Neural basis of deciding, choosing and acting

Abstract

The ability and opportunity to make decisions and carry out effective actions in pursuit of goals is central to intelligent life. Recent research has provided significant new insights into how the brain arrives at decisions, makes choices, and produces and evaluates the consequences of actions. In fact, by monitoring or manipulating specific neurons, certain choices can now be predicted or manipulated.

Key Points

  • Neurophysiological studies over the past decade have provided new insights into how the brain arrives at decisions, makes choices, produces actions and evaluates the consequences of actions.

  • Choice, decision and action are common words that require precise definitions to prevent confusion. Decision refers to covert deliberation about ambiguous or conflicting alternatives. Choice refers to the final commitment, an overt action performed in the context of alternatives for which explanations in terms of reasons and desires can be given.

  • Neural correlates of choosing, deciding and acting have been identified by monitoring neural activity in macaque monkeys performing tasks that require one response among alternatives that are more or less distinct with possibly different pay-offs. Hypotheses arising from neurophysiological data have been tested and extended by electrically stimulating the brain to probe and manipulate decision processes.

  • Neuroscience research shows that the choice of an agent hinges on activating a surprisingly small number of neurons in discrete parts of the brain. The fact that decisions can be predicted and manipulated challenges some accounts of the freedom of the will. However, the unpredictability inherent in nonlinear dynamic brain processes transpiring in an unpredictable world affords validity to our decisions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Visual choice behaviour.
Figure 2: Neural correlates of a choice in visual search.
Figure 3: Neural correlates of a perceptual decision.
Figure 4: Neural control of a purposeful eye movement.
Figure 5: Neural correlates of performance monitoring.

Similar content being viewed by others

References

  1. Mellers, B. A., Schwartz, A. & Crooke, A. D. J. Judgment and decision making. Annu. Rev. Psychol. 49, 447–477 (1998).A balanced survey of the current psychological perspective on deciding.

    CAS  PubMed  Google Scholar 

  2. Ryle, G. The Concept of Mind (Hutchinson's Univ. Library, London, 1949).

    Google Scholar 

  3. Nowell-Smith, P. H. Choosing, deciding and doing. Analysis 18, 63–69 (1958).

    Google Scholar 

  4. Evans, L. L. Choice. Phil. Quart. 5, 303– 315 (1955).

    Google Scholar 

  5. Kowler, E. Eye Movements and their Role in Visual and Cognitive Processes (Elsevier, Amsterdam, 1990).

    Google Scholar 

  6. Dennett, D. C. Elbow Room: The Varieties of Free Will Worth Wanting (MIT Press, Cambridge, Massachusetts, 1984).

    Google Scholar 

  7. Goldman, A. I. A Theory of Human Action (Prentice-Hall, Englewood Cliffs, New Jersey, 1970).

    Google Scholar 

  8. Parker, A. J. & Newsome, W. T. Sense and the single neuron: Probing the physiology of perception. Annu. Rev. Neurosci. 21, 227–277 (1998). A comprehensive and enlightening review of the neural processes in the cerebral cortex that underlie sensation and perception.

    Article  CAS  PubMed  Google Scholar 

  9. Schall, J. D. & Thompson, K. G. Neural selection and control of visually guided eye movements. Annu. Rev. Neurosci. 22, 241–259 (1999).

    CAS  PubMed  Google Scholar 

  10. Romo, R. & Salinas, E. Sensing and deciding in the somatosensory system. Curr. Opin. Neurobiol. 9, 487– 493 (1999).

    CAS  PubMed  Google Scholar 

  11. Leon, M. I. & Shadlen, M. N. Exploring the neurophysiology of decisions. Neuron 21, 669– 672 (1998).

    CAS  PubMed  Google Scholar 

  12. Wolfe, J. M. in Attention (ed. Pashler, H.) 13–74 (Psychological Press, Hove, East Sussex, UK, 1998).

    Google Scholar 

  13. Schall, J. D. & Hanes, D. P. Neural basis of saccade target selection in frontal eye field during visual search. Nature 366, 467–469 (1993).

    CAS  PubMed  Google Scholar 

  14. Gottlieb, J. P., Kusunoki, M. & Goldberg, M. E. The representation of visual salience in monkey parietal cortex. Nature 391, 481– 484 (1998).

    CAS  PubMed  Google Scholar 

  15. Basso, M. A. & Wurtz, R. H. Modulation of neuronal activity in superior colliculus by changes in target probability. J. Neurosci. 18, 7519–7534 ( 1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lamme, V. A., Super, H., Landman, R., Roelfsema, P. R. & Spekreijse, H. The role of primary visual cortex (V1) in visual awareness . Vision Res. 40, 1507– 1521 (2000).

    CAS  PubMed  Google Scholar 

  17. Ito, M. & Gilbert, C. D. Attention modulates contextual influences in the primary visual cortex of alert monkeys. Neuron 22, 593–604 ( 1999).

    CAS  PubMed  Google Scholar 

  18. Schall, J. D., Hanes, D. P., Thompson, K. & King, D. J. Saccade target selection in frontal eye field of macaque I: Visual and premovement activation. J. Neurosci. 15, 6905– 6918 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Thompson, K. G., Hanes, D. P., Bichot, N. P. & Schall, J. D. Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. J. Neurophysiol. 76, 4040–4055 ( 1996).This paper describes the time course of the neural process of discriminating the target in a visual search array. It is the first to relate the time of target selection to the time of saccade initiation.

    CAS  PubMed  Google Scholar 

  20. Burman, D. D. & Segraves, M. A. Primate frontal eye field activity during natural scanning eye movements. J. Neurophysiol. 71, 1266–1271 (1994).

    CAS  PubMed  Google Scholar 

  21. Thompson, K. G., Bichot, N. P. & Schall, J. D. Dissociation of target selection from saccade planning in macaque frontal eye field. J. Neurophysiol. 77, 1046–1050 (1997).

    CAS  PubMed  Google Scholar 

  22. Murthy, A., Thompson, K. G. & Schall, J. D. Neural control of saccade target selection during visual search. Soc. Neurosci. Abst. 25, 806 (1999).

    Google Scholar 

  23. Bichot, N. P. & Schall, J. D. Effects of similarity and history on neural mechanisms of visual selection. Nature Neurosci. 2, 549–554 (1999).

    CAS  PubMed  Google Scholar 

  24. Kim, M. S. & Cave, K. R. Spatial attention in search for features and feature conjunctions. Psychonomic Sci. 6, 376–380 (1995).

    Google Scholar 

  25. Findlay, J. M. Saccade target selection during visual search. Vision Res. 37, 617–631 (1997).

    CAS  PubMed  Google Scholar 

  26. Motter, B. C. & Belky, E. J. The guidance of eye movements during active visual search. Vision Res. 38, 1805 –1815 (1998).

    CAS  PubMed  Google Scholar 

  27. Bichot, N. P., Schall, J. D. & Thompson, K. G. Visual feature selectivity in frontal eye fields induced by experience in mature macaques. Nature 381 , 697–699 (1996).

    CAS  PubMed  Google Scholar 

  28. Asaad, W. F., Rainer, G. & Miller, E. K. Neural activity in the primate prefrontal cortex during associative learning. Neuron 21, 1399–1407 (1998).

    CAS  PubMed  Google Scholar 

  29. Wise, S. P. & Murray, E. A. Arbitrary associations between antecedents and actions. Trends Neurosci. 23, 271–276 (2000).

    CAS  PubMed  Google Scholar 

  30. Chen, L. L. & Wise, S. P. Neuronal activity in the supplementary eye field during acquisition of conditional oculomotor associations. J. Neurophysiol. 73, 1101–1121 (1995).

    CAS  PubMed  Google Scholar 

  31. Mitz, A. R., Godschalk, M. & Wise, S. P. Learning-dependent neuronal activity in the premotor cortex: Activity during the acquisition of conditional motor associations . J. Neurosci. 11, 1855– 1872 (1991).A demonstration of how the premotor cortex is involved in learning arbitrary associations of stimulus to response.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. White, I. M. & Wise, S. P. Rule-dependent neuronal activity in the prefrontal cortex. Exp. Brain Res. 126, 315–335 (1999).

    CAS  PubMed  Google Scholar 

  33. Newsome, W. T. The King Solomon Lectures in Neuroethology. Deciding about motion: linking perception to action. J. Comp. Physiol. A 181, 5–12 (1997).

    CAS  PubMed  Google Scholar 

  34. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S. & Movshon, J. A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 ( 1996).This paper describes in quantitative terms the relationship between neural activity and the perceptual report when the discriminative stimulus is weak or absent. Even when no sensory evidence was provided, a weak correlation was observed between the report of the monkeys and the discharge rate of the neurons in area MT of the extrastriate visual cortex.

    CAS  PubMed  Google Scholar 

  35. Shadlen, M. N., Britten, K. H., Newsome, W. T. & Movshon, J. A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Romo, R., Merchant, H., Zainos, A. & Hernandez, A. Categorization of somaesthetic stimuli: Sensorimotor performance and neuronal activity in primary somatic sensory cortex of awake monkeys. NeuroReport 7, 1273–1279 (1996).

    CAS  PubMed  Google Scholar 

  37. Hernandez, A., Zainos, A. & Romo, R. Neuronal correlates of sensory discrimination in the somatosensory cortex. Proc. Natl Acad. Sci. USA 97, 6191 –6196 (2000).This paper shows the quantitative relationship between the firing rate of neurons in somatosensory cortex and a monkey's report of the frequency of mechanical vibrations applied to the fingertips. The somatosensory cortex, like the visual system, produces decisions that are based on the activity of just a few neurons.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Shadlen, M. N. & Newsome, W. T. Motion perception: seeing and deciding. Proc. Natl Acad. Sci. 93, 628–633 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nature Neurosci. 2, 176–185 ( 1999).

    PubMed  Google Scholar 

  40. Horwitz, G. D. & Newsome, W. T. Separate signals for target selection and movement specification in the superior colliculus . Science 284, 1158–1161 (1999).

    CAS  PubMed  Google Scholar 

  41. Thompson, K. G. & Schall, J. D. The detection of visual signals by macaque frontal eye field during masking. Nature Neurosci. 2, 283–288 (1999).The first study of the neural correlates of visual masking that related perceptual reports to neural activity on a trial-by-trial basis. Differences in activity amounting to one or two spikes were amplified into positive reports of the presence of a masked stimulus.

    CAS  PubMed  Google Scholar 

  42. Thompson, K. G. & Schall, J. D. Antecedents and correlates of visual detection and awareness in macaque prefrontal cortex . Vision Res. 40, 1523– 1538 (2000).

    CAS  PubMed  Google Scholar 

  43. Salinas, E. & Romo, R. Conversion of sensory signals into motor commands in primary motor cortex. J. Neurosci. 18, 499–511 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Romo, R., Merchant, H., Zainos, A. & Hernandez, A. Categorical perception of somaesthetic stimuli: Psychophysical measurements correlated with neuronal events in primate medial premotor cortex. Cereb. Cortex 7, 317–326 ( 1997).

    CAS  PubMed  Google Scholar 

  45. Logothetis, N. K. & Schall, J. D. Neuronal correlates of subjective visual perception. Science 245, 761–763 (1989).Binocular rivalry with moving gratings was used to create an ambiguous stimulus that could support two distinct perceptual states. The activity of some neurons in area MT was associated with the perceptual report and not the retinal stimulation. This is the first paper to report an explicit association between the activity of neurons in the visual system and the perceptual state of monkeys.

    CAS  PubMed  Google Scholar 

  46. Romo, R., Hernàndez, A., Zainos, A. & Salinas, E. Sensing without touching: Psychophysical performance based on cortical microstimulation . Neuron 26, 1–20 (2000).

    Google Scholar 

  47. Salzman, C. D. & Newsome, W. T. Neural mechanisms for forming a perceptual decision. Science 264, 231–237 (1994).

    CAS  PubMed  Google Scholar 

  48. Salzman, C. D., Murasugi, C. M., Britten, K. H. & Newsome, W. T. Microstimulation in visual area MT: Effects on direction discrimination performance . J. Neurosci. 12, 2331– 2355 (1992).This paper shows that electrical stimulation of area MT influences monkeys' decisions about the direction of motion in a display.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Seidemann, E., Zohary, E. & Newsome, W. T. Temporal gating of neural signals during performance of a visual discrimination task. Nature 394, 72–75 (1998).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  51. Davidson, D. Actions, reasons and causes. J. Phil. 60, 685–700 (1963).

    Google Scholar 

  52. Sterelny, K. in Where Biology Meets Psychology: Philosophical Essays (ed. Hardcastle, V. G.) 203–219 (MIT Press, Cambridge, Massachusetts, 1999).

    Google Scholar 

  53. Schueler, G. F. Desire: Its Role in Practical Reason and the Explanation of Action (MIT Press, Cambridge, Massachusetts, 1995).

    Google Scholar 

  54. Kahneman, D., Wakker, P. P. & Sarin, R. Back to Bentham? Explorations of experienced utility . Quart. J. Econ. 112, 375– 405 (1997).

    Google Scholar 

  55. Rachlin, H. & Laibson, D. I. (eds) The Matching Law: Papers in Psychology and Economics. (Harvard Univ. Press, Cambridge, Massachusetts, 1997).

    Google Scholar 

  56. Olds, J. & Milner, P. M. Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. J. Comp. Physiol. Psychol. 47, 419– 427 (1954).A finding that spawned a new field of inquiry.

    CAS  PubMed  Google Scholar 

  57. Shizgal, P. Neural basis of utility estimation. Curr. Opin. Neurobiol. 7, 198–208 (1997).

    CAS  PubMed  Google Scholar 

  58. Berridge, K. C. & Robinson, T. E. What is the role of dopamine in reward: Hedonic impact, reward learning or incentive salience? Brain Res. Rev. 28, 309– 369 (1998).

    CAS  PubMed  Google Scholar 

  59. Robbins, T. W. & Everitt, B. J. Neurobehavioral mechanisms of reward and motivation. Curr. Opin. Neurobiol. 6, 228–236 (1996).

    CAS  PubMed  Google Scholar 

  60. Platt, M. L. & Glimcher, P. W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999).

    CAS  PubMed  Google Scholar 

  61. Leon, M. I. & Shadlen, M. N. Effect of expected reward magnitude on the response of neurons in the dorsolateral prefrontal cortex of the macaque . Neuron 24, 415–425 (1999).

    CAS  PubMed  Google Scholar 

  62. Kawagoe, R., Takikawa, Y. & Hikosaka, O. Expectation of reward modulates cognitive signals in the basal ganglia. Nature Neurosci. 1, 411 –416 (1998).References 60 62 show how the activity of neurons that had been thought to be involved in visual motor coordination is influenced by the likelihood and amount of reinforcement that monkeys receive.

    CAS  PubMed  Google Scholar 

  63. Watanabe, M. Reward expectancy in primate prefrontal neurons. Nature 382, 629–632 (1996).

    CAS  PubMed  Google Scholar 

  64. Hikosaka, K. & Watanabe, M. Delay activity of orbital and lateral prefrontal neurons of the monkey varying with different rewards. Cereb. Cortex 10, 263–271 (2000).

    CAS  PubMed  Google Scholar 

  65. Shima, K. & Tanji, J. Role for cingulate motor area cells in voluntary movement selection based on reward. Science 282, 1335–1338 (1998). Monkeys were required to change their behaviour to continue obtaining reinforcement. The cue to change behaviours was a change in the reinforcement. Neurons in anterior cingulate cortex signalled the transitions.

    CAS  PubMed  Google Scholar 

  66. Schultz, W. Multiple reward signals in the brain. Nature Rev. Neurosci. 1, 199–207 (2000).

    CAS  Google Scholar 

  67. Tremblay, L. & Schultz, W. Modifications of reward expectation-related neuronal activity during learning in primate orbitofrontal cortex. J. Neurophysiol. 83, 1877–1885 (1999).

    Google Scholar 

  68. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 ( 1997).This paper proposes a mechanistic model for how behaviour is shaped by reinforcement contingencies.

    CAS  PubMed  Google Scholar 

  69. Brown, J., Bullock, D. & Grossberg, S. How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. J. Neurosci. 19, 10502–10511 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Gallistel, C. R. Foraging for brain stimulation: toward a neurobiology of computation. Cognition 50, 151–170 ( 1994).

    CAS  PubMed  Google Scholar 

  71. Aston-Jones, G., Rajkowski, J. & Cohen, J. Role of locus coeruleus in attention and behavioral flexibility. Biol. Psychiat. 46, 1309– 1320 (1999).

    CAS  PubMed  Google Scholar 

  72. Egelman, D. M., Person, C. & Montague, P. R. A computational role for dopamine delivery in human decision making. J. Cogn. Neurosci. 10, 673–630 (1998).

    Google Scholar 

  73. Montague, P. R., Dayan, P. & Sejnowski, T. J. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J. Neurosci. 16, 1936–1947 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Bechara, A., Damasio, H., Tranel, D. & Damasio, A. R. Deciding advantageously before knowing the advantageous strategy. Science 275 , 1293–1295 (1997).

    CAS  PubMed  Google Scholar 

  75. Donders, F. C. translated in Attention and Performance II (ed. Koster, W. G.) 412–431 (North-Holland Publishing Co., Amsterdam, 1868/1969).

    Google Scholar 

  76. Luce, R. D. Response Times: Their Role in Inferring Elementary Mental Organization (Oxford Univ. Press, Oxford, 1986).

    Google Scholar 

  77. Posner, M. I. Chronometric Explorations of Mind (Lawrence Erlbaum, Hillsdale, New Jersey, USA, 1978).

    Google Scholar 

  78. Meyer, D. E., Osman, A. M., Irwin, D. E. & Yantis, S. Modern mental chronometry. Biol. Psychol. 26, 3–67 (1988).An informative review that is still timely.

    CAS  PubMed  Google Scholar 

  79. Munoz, D. P. & Wurtz, R. H. Saccade-related activity in monkey superior colliculus. I. Characteristics of burst and buildup cells. J. Neurophysiol. 73, 2313–2333 (1995).

    CAS  PubMed  Google Scholar 

  80. Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 ( 1985).

    CAS  PubMed  Google Scholar 

  81. Munoz, D. P. & Wurtz, R. H. Fixation cells in monkey superior colliculus. I. Characteristics of cell discharge. J. Neurophysiol. 70, 559–575 ( 1993).

    CAS  PubMed  Google Scholar 

  82. Munoz, D. P. & Wurtz, R. H. Fixation cells in monkey superior colliculus. II. Reversible activation and deactivation. J. Neurophysiol. 70, 576–589 ( 1993).

    CAS  PubMed  Google Scholar 

  83. Hanes, D. P. & Schall, J. D. Neural control of voluntary movement initiation. Science 274, 427– 430 (1996).This paper provides neurophysiological evidence contradicting one model and supporting an alternative model of response time. Movement-related neural activity in the frontal eye field corresponds to a race or diffusion with a variable rate to a fixed threshold.

    CAS  PubMed  Google Scholar 

  84. Lecas, J.-C., Requin, J., Anger, C. & Vitton, N. Changes in neuronal activity of the monkey precentral cortex during preparation for movement. J. Neurophysiol. 56, 1680–1702 (1986).

    CAS  PubMed  Google Scholar 

  85. Everling, S. & Munoz, D. P. Neuronal correlates for preparatory set associated with pro-saccades and anti-saccades in the primate frontal eye field. J. Neurosci. 20, 387– 400 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Dorris, M. C., Paré, M. & Munoz, D. P. Neuronal activity in monkey superior colliculus related to the initiation of saccadic eye movements. J. Neurosci. 17, 8566–8579 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. J. & Donchin, E. Pre- and poststimulus activation of response channels: A psychophysiological analysis. J. Exp. Psychol. Hum. Percept. Perform. 14, 331–344 (1988).

    CAS  PubMed  Google Scholar 

  88. Carpenter, R. H. S. & Williams, M. L. L. Neural computation of log likelihood in the control of saccadic eye movements. Nature 377, 59–62 ( 1995).

    CAS  PubMed  Google Scholar 

  89. Ratcliff, R., Van Zandt, T. & McKoon, G. Connectionist and diffusion models of reaction time . Psychol. Rev. 106, 261– 300.

  90. Usher, M. & McClelland, J. L. On the time course of perceptual choice: The leaky competing accumulator model. Psych. Rev. (submitted).

  91. Hanes, D. P., Patterson, W. F. & Schall, J. D. The role of frontal eye field in countermanding saccades: Visual, movement and fixation activity. J. Neurophysiol. 79, 817–834 (1998).

    CAS  PubMed  Google Scholar 

  92. Logan, G. D. in Inhibitory Processes in Attention, Memory and Language (eds. Dagenbach, D. & Carr, T. H.) 189–239 (Academic, San Diego, 1994).

    Google Scholar 

  93. Logan, G. D. & Cowan, W. B. On the ability to inhibit thought and action: A theory of an act of control. Psychol. Rev. 91, 295–327 (1984).

    Google Scholar 

  94. Hanes, D. P. & Schall, J. D. Countermanding saccades in macaque . Vis. Neurosci. 12, 929– 937 (1995).

    CAS  PubMed  Google Scholar 

  95. DeJong, R., Coles, M. G. H., Logan, G. D. & Gratton, G. In search of the point of no return: The control of response processes. J. Exp. Psychol. Human Percept. Perform. 16, 164– 182 (1990).

    Google Scholar 

  96. DeJong, R., Coles, M. G. H. & Logan, G. D. Strategies and mechanisms in nonselective and selective inhibitory motor control. J. Exp. Psychol. Human Percept. Perform. 21, 498–511 ( 1995).

    CAS  Google Scholar 

  97. Logan, G. D. Executive control of thought and action. Acta Psychologica 60, 193–210 (1985).

    Google Scholar 

  98. Norman, M. & Shallice, T. in Consciousness and Self-Regulation: Advances in Research and Theory Vol. 4 (eds Davidson, R. J., Schwartz, D. & Shapiro, D.) 1–18 (Plenum, New York, 1986).

    Google Scholar 

  99. Cohen, J. D., Braver, T. S. & O'Reilly, R. C. A computational approach to prefrontal cortex, cognitive control and schizophrenia: Recent developments and current challenges. Phil. Trans. R. Soc. Lond. B 351, 1515– 1527 (1996). PubMed

    CAS  Google Scholar 

  100. Meyer, D. E. & Kieras, D. E. A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms . Psychol. Rev. 104, 3– 65 (1997).

    CAS  PubMed  Google Scholar 

  101. Stuphorn, V., Taylor, T. L. & Schall, J. D. Performance monitoring by supplementary eye field . Nature (in the press).Surprising observations show that the supplementary eye field does not produce signals sufficient to control gaze but instead produces signals appropriate to monitor behaviour.

  102. Falkenstein, M., Hohnsbein, J., Hoormann, J. & Blanke, L. Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. Electroencephal. Clin. Neurophysiol. 78, 447–455 ( 1991).

    CAS  Google Scholar 

  103. Gehring, W. J., Goss, B., Coles, M. G. & Meyer, D. E. A neural system for error detection and compensation. Psych. Sci. 4 , 385–390 (1993). This paper describes the error-related negativity, a scalp potential that appears when human subjects make errors. The brain's ability to detect errors is a prerequisite to exerting executive control over behaviour.

    Google Scholar 

  104. Miltner, W. H. R., Braun, C. H. & Coles, M. G. H. Event-related brain potentials following incorrect feedback in a time-estimation task: Evidence for a 'generic' neural system for error detection. J. Cogn. Neurosci. 9, 788–798 (1997).

    CAS  PubMed  Google Scholar 

  105. Scheffers, M. K., Coles, M. G., Bernstein, P., Gehring, W. J. & Donchin, E. Event-related brain potentials and error-related processing: An analysis of incorrect responses to go and no-go stimuli. Psychophysiology 33, 42– 53 (1996).

    CAS  PubMed  Google Scholar 

  106. Falkenstein, M., Koshlykova, N. A., Kiroj, V. N., Hoormann, J. & Hohnsbein, J. Late ERP components in visual and auditory Go/Nogo tasks. Electroencephal. Clin. Neurophysiol. 96, 36–43 ( 1995).

    CAS  Google Scholar 

  107. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S. & Cohen, J. D. Evaluating the demand for control: Anterior cingulate cortex and cross–talk monitoring. Psychol. Rev. (in the press).A review covering the means by which, and the conditions under which, executive control is required. It is proposed that the brain detects the need for control by monitoring the extent of co-activation of mutually incompatible modules or processes.

  108. Carter, C. S. et al. Anterior cingulate cortex, error detection and the on-line monitoring of performance. Science 280, 747–749 (1998).

    CAS  PubMed  Google Scholar 

  109. MacDonald, A. W. III, Cohen, J. D., Stenger, V. A. & Carter, C. S. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835–1838 (2000).

    CAS  PubMed  Google Scholar 

  110. Carter, C. S. et al. Parsing executive processes: strategic vs. evaluative functions of the anterior cingulate cortex. Proc. Natl Acad. Sci. USA 97, 1944–1948 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S. & Cohen, J. D. Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402, 179–181 (1999).

    CAS  PubMed  Google Scholar 

  112. Amador, N., Schlag–Rey, M. & Schlag, J. Reward-predicting and reward-detecting neuronal activity in the primate supplementary eye field. J. Neurophysiol. 84, 2166–2170 (2000).

    CAS  PubMed  Google Scholar 

  113. Penfield, W. The Mystery of the Mind: a Critical Study of Consciousness and the Human Brain (Princeton Univ. Press, Princeton, New Jersey, 1975 ).

    Google Scholar 

  114. Editorial. Does neuroscience threaten human values? Nature Neurosci. 1, 535 (1998 ).

  115. Ball, P. Transitions still to be made. Nature 402, C37–C76 (1999).

    Google Scholar 

  116. van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 ( 1996).The well-known irregularity of neural activity can arise from the deterministic but unpredictable dynamics of neural networks.

    CAS  PubMed  Google Scholar 

  117. Wilson, H. Spikes, Decisions and Actions: Dynamical Foundations of Neuroscience. (Oxford Univ. Press, New York, 1999).

    Google Scholar 

  118. Gilden, D. L., Thornton, T. & Mallon, M. W. 1/f noise in human cognition. Science 267, 1837–1839 ( 1995).

    CAS  PubMed  Google Scholar 

  119. Pressing, J. & Jolley-Rogers, G. Spectral properties of human cognition and skill. Biol Cybern. 76, 339 –347 (1997).

    CAS  PubMed  Google Scholar 

  120. Globus, G. G. Toward a noncomputational cognitive neuroscience. J. Cogn. Neurosci. 4, 299–310 ( 1992).

    CAS  PubMed  Google Scholar 

  121. Busemeyer, J. R. & Townsend, J. T. Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment . Psychol. Rev. 100, 432– 459 (1993).

    CAS  PubMed  Google Scholar 

  122. van Gelder, T. The dynamical hypothesis in cognitive science. Behav. Brain Sci. 21, 615–628 ( 1998).

    CAS  PubMed  Google Scholar 

  123. Senders, J. W. & Moray, N. P. Human Error: Cause, Prediction, and Reduction Analysis (Lawrence Erlbaum, Hillsdale, New Jersey, 1991).

    Google Scholar 

  124. Baars, B. J. Experimental Slips and Human Error: Exploring the Architecture of Volition (Plenum, New York, 1992).

    Google Scholar 

  125. Klein, G. Sources of Power: How People Make Decisions (MIT Press, Cambridge, Massachusetts, 1998).

    Google Scholar 

  126. Britten, K. H., Shadlen, M. N., Newsome, W. T. & Movshon, J. A. Responses of neurons in macaque MT to stochastic motion signals. Vis. Neurosci. 10, 1157–1169 (1993).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

I am very grateful to R. Blake, M. Chun, F. Ebner, R. Marois, A. Murthy, M. Shadlen and V. Stuphorn for comments on the manuscript. Research in my laboratory is supported by the NEI, the NIMH and the McKnight Endowment Fund for Neuroscience.

Author information

Authors and Affiliations

Authors

Related links

Related links

FURTHER INFORMATION

Schall lab homepage

Glossary

FRONTAL EYE FIELD

An area in the frontal lobe that receives visual inputs and produces movements of the eye.

ODDBALL

The one stimulus that is different from all of the rest. Usually refers to the stimulus that has a unique feature (colour, form, direction of motion) in a visual search array.

BACKWARD MASKING

The reduced perception that occurs when a weak stimulus is followed immediately by a stronger stimulus.

BINOCULAR RIVALRY

The perceptual alternation that occurs when markedly different stimuli are presented to the two eyes – for example, horizontal bars in one eye and vertical bars in the other.

RESPONSE TIME

The time that elapses between presentation of a stimulus requiring a behavioural response and the time of initiation of the response.

RACE MODEL

A common model in cognitive psychology in which a behaviour is supposed to be the outcome of a race between two or more processes that have random finish times. Race models have been used to explain choice behaviour and the control of actions.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schall, J. Neural basis of deciding, choosing and acting. Nat Rev Neurosci 2, 33–42 (2001). https://doi.org/10.1038/35049054

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/35049054

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing