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Neural basis of deciding, choosing and acting


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.

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  1. 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.

  2. 2

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

  3. 3

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

  4. 4

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

  5. 5

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

  6. 6

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

  7. 7

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

  8. 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.

  9. 9

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

  10. 10

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

  11. 11

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

  12. 12

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

  13. 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).

  14. 14

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

  15. 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).

  16. 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).

  17. 17

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

  18. 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).

  19. 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.

  20. 20

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

  21. 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).

  22. 22

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

  23. 23

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

  24. 24

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

  25. 25

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

  26. 26

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

  27. 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).

  28. 28

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

  29. 29

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

  30. 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).

  31. 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.

  32. 32

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

  33. 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).

  34. 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.

  35. 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).

  36. 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).

  37. 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.

  38. 38

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

  39. 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).

  40. 40

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

  41. 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.

  42. 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).

  43. 43

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

  44. 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).

  45. 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.

  46. 46

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

  47. 47

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

  48. 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.

  49. 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).

  50. 50

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

  51. 51

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

  52. 52

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

  53. 53

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

  54. 54

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

  55. 55

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

  56. 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.

  57. 57

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

  58. 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).

  59. 59

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

  60. 60

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

  61. 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).

  62. 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.

  63. 63

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

  64. 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).

  65. 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.

  66. 66

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

  67. 67

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

  68. 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.

  69. 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).

  70. 70

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

  71. 71

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

  72. 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).

  73. 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).

  74. 74

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

  75. 75

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

  76. 76

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

  77. 77

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

  78. 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.

  79. 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).

  80. 80

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

  81. 81

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

  82. 82

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

  83. 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.

  84. 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).

  85. 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).

  86. 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).

  87. 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).

  88. 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).

  89. 89

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

  90. 90

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

  91. 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).

  92. 92

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

  93. 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).

  94. 94

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

  95. 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).

  96. 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).

  97. 97

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

  98. 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).

  99. 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

  100. 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).

  101. 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. 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).

  103. 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.

  104. 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).

  105. 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).

  106. 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).

  107. 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. 108

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

  109. 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).

  110. 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).

  111. 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).

  112. 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).

  113. 113

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

  114. 114

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

  115. 115

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

  116. 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.

  117. 117

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

  118. 118

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

  119. 119

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

  120. 120

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

  121. 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).

  122. 122

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

  123. 123

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

  124. 124

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

  125. 125

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

  126. 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).

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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.

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


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.


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


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.


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


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.

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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.