Neuroheuristics of Decision Making: From Neuronal Activity to EEG

  • Alessandro E. P. Villa
  • Pascal Missonnier
  • Alessandra Lintas
Part of the Intelligent Systems Reference Library book series (ISRL, volume 28)


Neuroheuristics, or Neuristics, is a term issued from the Greek terms neuron (nerve) and heuriskein (to find, to discover). It refers to that branch of Science aimed at exploring the Neurosciences through an ongoing process continuously renewed at each successive step of its advancement towards understanding the brain in its entirety. This chapter presents a neuroheuristic approach to the decision making process, firstly in an animal experiment, in an attempt to investigate the basic processes away from an anthropological perspective, and secondly in a classical neuroeconomic paradigm, the Ultimatum Game (UG). Multiple electrodes for multiple neuronal recordings were chronically implanted in cerebral cortical areas of freely-moving rats trained in a response choice task. Invariant preferred firing sequences appeared in association with the response predicted by the subject or in association with specific errors of decision. We recorded EEG and analyzed event-related potentials of subjects in a two conditions variant of UG where human players acted either as proposers with computer-controlled virtual partners or as responders to offers made by a virtual proposer. A proposer, in contrast to a responder, has to store the future proposed value in short-term memory and engage retrieval processes after getting the responder’s reaction. Our EEG results support the hypothesis that while playing the role of proposers human subjects engage in a specific retrieval process while performing UG.


Spike Train Ultimatum Game Contingent Negative Variation Executive Function Work Memory Training 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Abeles, M.: Quantification, smoothing, and confidence limits for single-units’ histograms. J. Neurosci. Methods 5(4), 317–325 (1982)CrossRefGoogle Scholar
  2. 2.
    Abeles, M.: Corticonics: Neural Circuits of the Cerebral Cortex, 1st edn. Cambridge University Press, Cambridge (1991)CrossRefGoogle Scholar
  3. 3.
    Abeles, M., Bergman, H., Margalit, E., Vaadia, E.: Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. Neurophysiol. 70(4), 1629–1638 (1993)Google Scholar
  4. 4.
    Abeles, M., Gerstein, G.L.: Detecting spatiotemporal firing patterns among simultaneously recorded single neurons. J. Neurophysiol. 60(3), 909–924 (1988)Google Scholar
  5. 5.
    Aksenova, T.I., Volkovych, V.V., Villa, A.E.: Detection of spectral instability in EEG recordings during the preictal period. J. Neural. Eng. 4(3), 173–178 (2007)CrossRefGoogle Scholar
  6. 6.
    Asai, Y., Guha, A., Villa, A.E.P.: Deterministic neural dynamics transmitted through neural networks. Neural Netw. 21(6), 799–809 (2008)CrossRefGoogle Scholar
  7. 7.
    Asai, Y., Villa, A.E.P.: Reconstruction of underlying nonlinear deterministic dynamics embedded in noisy spike trains. J. Biol. Phys. 34(3-4), 325–340 (2008)CrossRefGoogle Scholar
  8. 8.
    Baddeley, A.: The fractionation of working memory. Proc. Natl. Acad. Sci. U S A 93(24), 13,468–13,472 (1996)Google Scholar
  9. 9.
    Baker, T.E., Holroyd, C.B.: Dissociated roles of the anterior cingulate cortex in reward and conflict processing as revealed by the feedback error-related negativity and N200. Biological Psychology 87(1), 25–34 (2011)CrossRefGoogle Scholar
  10. 10.
    Bartholow, B.D., Pearson, M.A., Dickter, C.L., Sher, K.J., Fabiani, M., Gratton, G.: Strategic control and medial frontal negativity: beyond errors and response conflict. Psychophysiology 42(1), 33–42 (2005)CrossRefGoogle Scholar
  11. 11.
    Bastiaansen, M., Hagoort, P.: Event-induced theta responses as a window on the dynamics of memory. Cortex 39(4-5), 967–992 (2003)CrossRefGoogle Scholar
  12. 12.
    Bastiaansen, M.C., Brunia, C.H.: Anticipatory attention: an event-related desynchronization approach. Int. J. Psychophysiol. 43(1), 91–107 (2001)CrossRefGoogle Scholar
  13. 13.
    Bechara, A., Damasio, A.R., Damasio, H., Anderson, S.W.: Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50(1-3), 7–15 (1994)CrossRefGoogle Scholar
  14. 14.
    Bechara, A., Damasio, H., Tranel, D., Damasio, A.R.: The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers. Trends Cogn. Sci. 9(4), 159–162 (2005)CrossRefGoogle Scholar
  15. 15.
    Bornkessel-Schlesewsky, I., Schlesewsky, M.: An alternative perspective on “semantic P600” effects in language comprehension. Brain Res. Rev. 59(1), 55–73 (2008)CrossRefGoogle Scholar
  16. 16.
    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(6758), 179–181 (1999)CrossRefGoogle Scholar
  17. 17.
    Braitenberg, V., Schüz, A.: Cortex: Statistics and Geometry of Neuronal Connectivity. Springer, Berlin (1998) ISBN: 3-540-63816-4 Google Scholar
  18. 18.
    Breiter, H.C., Aharon, I., Kahneman, D., Dale, A., Shizgal, P.: Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 30(2), 619–639 (2001)CrossRefGoogle Scholar
  19. 19.
    Burks, S.V., Carpenter, J.P., Goette, L., Rustichini, A.: Cognitive skills affect economic preferences, strategic behavior, and job attachment. Proc. Natl. Acad. Sci. U S A 106(19), 7745–7750 (2009)CrossRefGoogle Scholar
  20. 20.
    Cabessa, J., Villa, A.E.P.: A hierarchical classification of first-order recurrent neural networks. Chin. J. Physiol. 53(6), 407–416 (2010)CrossRefGoogle Scholar
  21. 21.
    Cameron, L.A.: Raising the stakes in the Ultimatum Game: Experimental evidence from Indonesia. Econ. Inq. 37(1), 47–59 (1999)CrossRefGoogle Scholar
  22. 22.
    Carretié, L., Mercado, F., Tapia, M., Hinojosa, J.A.: Emotion, attention, and the ’negativity bias’, studied through event-related potentials. Int. J. Psychophysiol. 41(1), 75–85 (2001)CrossRefGoogle Scholar
  23. 23.
    Cavanagh, J.F., Frank, M.J., Klein, T.J., Allen, J.J.: Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. NeuroImage 49(4), 3198–3209 (2010)CrossRefGoogle Scholar
  24. 24.
    Chabris, C.F., Laibson, D., Morris, C.L., Schuldt, J.P., Taubinsky, D.: The allocation of time in decision-making. J. Eur. Econ. Assoc. 7(2), 628–637 (2009)CrossRefGoogle Scholar
  25. 25.
    Chang, Y.H., Levinboim, T., Maheswaran, R.: The Social Ultimatum Game. In: This Book. Springer, Heidelberg (2011)Google Scholar
  26. 26.
    Crowley, K.E., Colrain, I.M.: A review of the evidence for P2 being an independent component process: age, sleep and modality. Clin. Neurophysiol. 115(4), 732–744 (2004)CrossRefGoogle Scholar
  27. 27.
    Damasio, A.R., Tranel, D., Damasio, H.: Somatic markers and the guidance of behaviour: theory and preliminary testing. In: Levin, H.S., Eisenberg, H.M., Benton, A.L. (eds.) Frontal Lobe Function and Dysfunction, pp. 217–229. Oxford University Press, New York (1991)Google Scholar
  28. 28.
    Davies, P.L., Segalowitz, S.J., Dywan, J., Pailing, P.E.: Error-negativity and positivity as they relate to other ERP indices of attentional control and stimulus processing. Biol. Psychol. 56(3), 191–206 (2001)CrossRefGoogle Scholar
  29. 29.
    Davis, C.E., Hauf, J.D., Wu, D.Q., Everhart, D.E.: Brain function with complex decision making using electroencephalography. Int. J. Psychophysiol. 79(2), 175–183 (2011)CrossRefGoogle Scholar
  30. 30.
    Daw, N.D.: Dopamine: at the intersection of reward and action. Nat. Neurosci. 10(12), 1505–1507 (2007)CrossRefGoogle Scholar
  31. 31.
    Deecke, L., Kornhuber, H.H.: Human freedom, reasoned will, and the brain. the Bereitschaftspotential story. In: Jahanshahi, M., Hallett, M. (eds.) The Bereitschaftspotential, Movement-Related Cortical Potentials, pp. 283–320. Kluwer Academic/Plenum Publishers (2003)Google Scholar
  32. 32.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRefGoogle Scholar
  33. 33.
    Doesburg, S.M., Green, J.J., McDonald, J.J., Ward, L.M.: Rhythms of consciousness: binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception. PLoS One, 4(7) (2009)Google Scholar
  34. 34.
    Doya, K.: Modulators of decision making. Nat. Neurosci. 11(4), 410–416 (2008)CrossRefGoogle Scholar
  35. 35.
    Dudkin, K.N., Kruchinin, V.K., Chueva, I.V.: Neurophysiologic correlates of the decision-making processes in the cerebral cortex of monkeys during visual recognition. Neurosci. Behav. Physiol. 25(5), 348–356 (1995)CrossRefGoogle Scholar
  36. 36.
    Edin, F., Klingberg, T., Johansson, P., McNab, F., Tegnér, J., Compte, A.: Mechanism for top-down control of working memory capacity. Proc. Natl. Acad. Sci. U S A 106(16), 6802–6807 (2009) CrossRefGoogle Scholar
  37. 37.
    Engel, A.K., Fries, P., Singer, W.: Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2(10), 704–716 (2001)CrossRefGoogle Scholar
  38. 38.
    Eriksen, C.W., Schultz, D.W.: Information processing in visual search: a continuous flow conception and experimental results. Percept. Psychophys. 25(4), 249–263 (1979)CrossRefGoogle Scholar
  39. 39.
    Eriksson, J.L., Villa, A.E.P.: Learning of auditory equivalence classes for vowels by rats. Behav. Proc. 73, 348–359 (2006)CrossRefGoogle Scholar
  40. 40.
    Farné, R.: Pedagogy of Play. Topoi 24(2), 169–181 (2005)CrossRefGoogle Scholar
  41. 41.
    Federmeier, K.D., Kutas, M.: Meaning and modality: influences of context, semantic memory organization, and perceptual predictability on picture processing. J. Exp. Psychol. Learn. Mem. Cogn. 27(1), 202–224 (2001)CrossRefGoogle Scholar
  42. 42.
    Folstein, J.R., Van Petten, C.: Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 45(1), 152–170 (2008)Google Scholar
  43. 43.
    Frank, M.J., Claus, E.D.: Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal. Psychol. Rev. 113(2), 300–326 (2006)CrossRefGoogle Scholar
  44. 44.
    Frederick, S.: Cognitive reflection and decision making. J. Econ. Perspect. 19(4), 25–42 (2005)CrossRefGoogle Scholar
  45. 45.
    Frenzel, S., Schlesewsky, M., Bornkessel-Schlesewsky, I.: Conflicts in language processing: a new perspective on the N400-P600 distinction. Neuropsychologia 49(3), 574–579 (2011)CrossRefGoogle Scholar
  46. 46.
    Freunberger, R., Klimesch, W., Doppelmayr, M., Höller, Y.: Visual P2 component is related to theta phase-locking. Neurosci. Lett. 426(3), 181–186 (2007)CrossRefGoogle Scholar
  47. 47.
    Frith, U., Happé, F.: Autism: beyond “theory of mind”. Cognition 50(1-3), 115–132 (1994)CrossRefGoogle Scholar
  48. 48.
    Gaillard, A.W.: Effects of warning-signal modality on the contingent negative variation (CNV). Biol. Psychol. 4(2), 139–154 (1976)CrossRefGoogle Scholar
  49. 49.
    Gasser, T., Bächer, P., Möcks, J.: Transformations towards the normal distribution of broad band spectral parameters of the EEG. Electroencephalogr. Clin. Neurophysiol. 53(1), 119–124 (1982)CrossRefGoogle Scholar
  50. 50.
    Gatev, P., Wichmann, T.: Interactions between cortical rhythms and spiking activity of single basal ganglia neurons in the normal and parkinsonian state. Cereb. Cortex 19(6), 1330–1344 (2009)CrossRefGoogle Scholar
  51. 51.
    Gehring, W.J., Willoughby, A.R.: The medial frontal cortex and the rapid processing of monetary gains and losses. Science 295(5563), 2279–2282 (2002)CrossRefGoogle Scholar
  52. 52.
    Giard, M.H., Fort, A., Mouchetant-Rostaing, Y., Pernier, J.: Neurophysiological mechanisms of auditory selective attention in humans. Front. Biosci. 5, 84–94 (2000)CrossRefGoogle Scholar
  53. 53.
    Girden, E.R.: ANOVA: repeated measures. In: Quantitative Applications in the Social Sciences, vol. 84. Sage University Papers. Sage, Thousand Oaks (1992)Google Scholar
  54. 54.
    Gollo, L.L., Mirasso, C., Villa, A.E.: Dynamic control for synchronization of separated cortical areas through thalamic relay. Neuroimage 52(3), 947–955 (2010)CrossRefGoogle Scholar
  55. 55.
    Gratton, G., Coles, M.G., Donchin, E.: A new method for off-line removal of ocular artifact. Electroencephalogr. Clin. Neurophysiol. 55(4), 468–484 (1983)CrossRefGoogle Scholar
  56. 56.
    Gray, H.: Anatomy of the human body. Lea and Febiger, Philadelphia (1918)Google Scholar
  57. 57.
    Grice, G.R., Nullmeyer, R., Spiker, V.A.: Human reaction times: Toward a general theory. J. Exp. Psychol. 11, 135–153 (1982)Google Scholar
  58. 58.
    Guderian, S., Düzel, E.: Induced theta oscillations mediate large-scale synchrony with mediotemporal areas during recollection in humans. Hippocampus 15(7), 901–912 (2005)CrossRefGoogle Scholar
  59. 59.
    Güth, W., Schmittberger, R., Schwarze, B.: An experimental analysis of ultimatum bargaining. J. Econ. Behav. Organ. 3(4), 367–388 (1982)CrossRefGoogle Scholar
  60. 60.
    Hackley, S.A., Woldorff, M., Hillyard, S.A.: Cross-modal selective attention effects on retinal, myogenic, brainstem, and cerebral evoked potentials. Psychophysiology 27(2), 195–208 (1990)CrossRefGoogle Scholar
  61. 61.
    Hampton, A.N., Bossaerts, P., O’Doherty, J.P.: Neural correlates of mentalizing-related computations during strategic interactions in humans. Proc. Natl. Acad. Sci. U S A 105(18), 6741–6746 (2008)CrossRefGoogle Scholar
  62. 62.
    Hayon, G., Abeles, M., Lehmann, D.: A model for representing the dynamics of a system of synfire chains. J. Comput. Neurosci. 18, 41–53 (2005)MathSciNetCrossRefGoogle Scholar
  63. 63.
    Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., McElreath, R.: In search of Homo economicus: Behavioral experiments in 15 small-scale societies. Am. Econ. Rev. 91(2), 73–78 (2001)CrossRefGoogle Scholar
  64. 64.
    Hillyard, S.A., Picton, T.W., Regan, D.: Sensation, perception and attention: Analysis using ERPs. In: Callaway, E., Tueting, P., Koslow, S.H. (eds.) Event-related Brain Potentials in Man, pp. 223–321. Academic Press, New York (1978)Google Scholar
  65. 65.
    Holroyd, C.B., Krigolson, O.E., Baker, R., Lee, S., Gibson, J.: When is an error not a prediction error? An electrophysiological investigation. Cogn. Aff. Behav. Neurosci. 9, 59–70 (2009)CrossRefGoogle Scholar
  66. 66.
    Jaeggi, S.M., Buschkuehl, M., Jonides, J., Perrig, W.J.: Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. U S A 105(19), 6829–6833 (2008)CrossRefGoogle Scholar
  67. 67.
    Hewig, J., Trippe, R., Hecht, H., Coles, M.G.H., Holroyd, C.B., Miltner, W.H.R.: Decision-Making in Blackjack: An Electrophysiological Analysis. Cereb. Cortex 17, 865–877 (2007)CrossRefGoogle Scholar
  68. 68.
    Kahneman, D., Tversky, A.: Prospect Theory: An Analysis of Decision under Risk. Econometrica 47(2) (1979)Google Scholar
  69. 69.
    Kalenscher, T., Pennartz, C.M.: Is a bird in the hand worth two in the future? The neuroeconomics of intertemporal decision-making. Prog. Neurobiol. 84(3), 284–315 (2008)CrossRefGoogle Scholar
  70. 70.
    Kamarajan, C., Porjesz, B., Rangaswamy, M., Tang, Y., Chorlian, D.B., Padmanabhapillai, A., Saunders, R., Pandey, A.K., Roopesh, B.N., Manz, N., Stimus, A.T., Begleiter, H.: Brain signatures of monetary loss and gain: outcome-related potentials in a single outcome gambling task. Behav. Brain Res. 197(1), 62–76 (2009)CrossRefGoogle Scholar
  71. 71.
    Kenning, P., Plassmann, H.: Neuroeconomics: an overview from an economic perspective. Brain Res. Bull. 67(5), 343–354 (2005)CrossRefGoogle Scholar
  72. 72.
    Kirchner, W.K.: Age differences in short-term retention of rapidly changing information. J. Exp. Psychol. 55(4), 352–358 (1958)CrossRefGoogle Scholar
  73. 73.
    Klimesch, W., Sauseng, P., Hanslmayr, S.: EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res. Rev. 53(1), 63–88 (2007)CrossRefGoogle Scholar
  74. 74.
    Klingberg, T., Fernell, E., Olesen, P.J., Johnson, M., Gustafsson, P., Dahlström, K., Gillberg, C.G., Forssberg, H., Westerberg, H.: Computerized training of working memory in children with ADHD–a randomized, controlled trial. J. Am. Acad. Child Adolesc. Psychiatry 44(2), 177–186 (2005)CrossRefGoogle Scholar
  75. 75.
    Klostermann, F., Wahl, M., Marzinzik, F., Schneider, G.H., Kupsch, A., Curio, G.: Mental chronometry of target detection: human thalamus leads cortex. Brain 129(Pt 4), 923–931 (2006)Google Scholar
  76. 76.
    Knoch, D., Nitsche, M.A., Fischbacher, U., Eisenegger, C., Pascual-Leone, A., Fehr, E.: Studying the neurobiology of social interaction with transcranial direct current stimulation–the example of punishing unfairness. Cereb. Cortex 18(9), 1987–1990 (2008)CrossRefGoogle Scholar
  77. 77.
    Knutson, B., Adams, C.M., Fong, G.W., Hommer, D.: Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21(16) (2001)Google Scholar
  78. 78.
    Knutson, B., Bossaerts, P.: Neural antecedents of financial decisions. J. Neurosci. 27(31), 8174–8177 (2007)CrossRefGoogle Scholar
  79. 79.
    Knyazev, G.G.: Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci. Biobehav. Rev. 31(3), 377–395 (2007)MathSciNetCrossRefGoogle Scholar
  80. 80.
    Koban, L., Pourtois, G., Vocat, R., Vuilleumier, P.: When your errors make me lose or win: event-related potentials to observed errors of cooperators and competitors. Soc. Neurosci. 5(4), 360–374 (2010)CrossRefGoogle Scholar
  81. 81.
    Koenigs, M., Tranel, D.: Irrational economic decision-making after ventromedial prefrontal damage: evidence from the Ultimatum Game. J. Neurosci. 27(4), 951–956 (2007)CrossRefGoogle Scholar
  82. 82.
    Kopp, B., Rist, F., Mattler, U.: N200 in the flanker task as a neurobehavioral tool for investigating executive control. Psychophysiology 33(3), 282–294 (1996)CrossRefGoogle Scholar
  83. 83.
    Kornblum, S.: Dimensional overlap and dimensional relevance in stimulus-response and stimulus-stimulus compatibility. In: Stelmach, G.E., Requin, J. (eds.) Tutorials in Motor Behavior II, vol. 2, pp. 743–777. Elsevier, Amsterdam (1992)Google Scholar
  84. 84.
    Kornblum, S., Hasbroucq, T., Osman, A.: Dimensional overlap: Cognitive basis for stimulus-response compatibility: A model and taxonomy. Psychol. Rev. 97, 253–270 (1990)CrossRefGoogle Scholar
  85. 85.
    Kornblum, S., Lee, J.W.: Stimulus-response compatibility with relevant and irrelevant stimulus dimensions that do and do not overlap with the response. J. Exp. Psychol.: Human Perception Perform. 21, 855–875 (1995)CrossRefGoogle Scholar
  86. 86.
    Kostandov, E.A., Cheremushkin, E.A., Kozlov, M.K.: Evoked synchronization/desynchronization of cortical electrical activity in response to facial stimuli during formation of a set to an emotionally negative expression. Neurosci. Behav. Physiol. 40(4), 421–428 (2010)CrossRefGoogle Scholar
  87. 87.
    Krause, C.M., Pesonen, M., Hämäläinen, H.: Brain oscillatory 4-30 Hz electroencephalogram responses in adolescents during a visual memory task. Neuroreport 21(11), 767–771 (2010)CrossRefGoogle Scholar
  88. 88.
    Krause, C.M., Sillanmäki, L., Koivisto, M., Saarela, C., Häggqvist, A., Laine, M., Hämäläinen, H.: The effects of memory load on event-related EEG desynchronization and synchronization. Clin. Neurophysiol. 111(11), 2071–2078 (2000)CrossRefGoogle Scholar
  89. 89.
    Kujala, T., Näätänen, R.: The adaptive brain: a neurophysiological perspective. Prog. Neurobiol. 91(1), 55–67 (2010)CrossRefGoogle Scholar
  90. 90.
    Kull, K.: Biosemiotics in the twentieth century: A view from biology. Semiotica 127, 385–414 (1999)CrossRefGoogle Scholar
  91. 91.
    Kutas, M., Federmeier, K.D.: Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annu. Rev. Psychol. 62, 621–647 (2011)CrossRefGoogle Scholar
  92. 92.
    Li, L., Gratton, C., Yao, D., Knight, R.T.: Role of frontal and parietal cortices in the control of bottom-up and top-down attention in humans. Brain Res. 1344, 173–184 (2010)CrossRefGoogle Scholar
  93. 93.
    Linden, D.E.: The P300: where in the brain is it produced and what does it tell us? Neuroscientist 11(6), 563–576 (2005)MathSciNetCrossRefGoogle Scholar
  94. 94.
    MacDonald, A.W., Carter, C.S., Kerns, J.G., Ursu, S., Barch, D.M., Holmes, A.J., Stenger, V.A., Cohen, J.D.: Specificity of prefrontal dysfunction and context processing deficits to schizophrenia in never-medicated patients with first-episode psychosis. Am. J. Psychiatry 162(3), 475–484 (2005)CrossRefGoogle Scholar
  95. 95.
    Makeig, S.: Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr. Clin. Neurophysiol. 86(4), 283–293 (1993)CrossRefGoogle Scholar
  96. 96.
    Marco-Pallarés, J., Krämer, U.M., Strehl, S., Schröder, A., Münte, T.F.: When decisions of others matter to me: an electrophysiological analysis. BMC Neurosci. 11, 86–86 (2010)CrossRefGoogle Scholar
  97. 97.
    McClelland, J.L.: On the time relations of mental processes: A framework for analyzing processes in cascade. Psychol. Rev. 86, 287–330 (1979)CrossRefGoogle Scholar
  98. 98.
    McClure, S.M., Laibson, D.I., Loewenstein, G., Cohen, J.D.: Separate neural systems value immediate and delayed monetary rewards. Science 306(5695), 503–507 (2004)CrossRefGoogle Scholar
  99. 99.
    McNab, F., Varrone, A., Farde, L., Jucaite, A., Bystritsky, P., Forssberg, H., Klingberg, T.: Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science 323(5915), 800–802 (2009), doi:10.1126/science.1166102CrossRefGoogle Scholar
  100. 100.
    Meier, S., Sprenger, C.: Impatience and credit behavior: evidence from a field experiment. Working Papers 07-3, Federal Reserve Bank of Boston (2007)Google Scholar
  101. 101.
    Mennes, M., Wouters, H., van den Bergh, B., Lagae, L., Stiers, P.: ERP correlates of complex human decision making in a gambling paradigm: detection and resolution of conflict. Psychophysiology 45(5), 714–720 (2008)CrossRefGoogle Scholar
  102. 102.
    Miller, J.: Discrete versus continuous stage models of human information processing: In search of partial output. J. Exp. Psychol.: Human Perception Perform. 8, 273–296 (1982)CrossRefGoogle Scholar
  103. 103.
    Miller, J.: Discrete and continuous models of human information processing: Theoretical distinctions and empirical results. Acta Psychol. 67, 191–257 (1988)CrossRefGoogle Scholar
  104. 104.
    Milliken, G., Johnson, D.: Analysis of Messy Data, Volume I: Designed Experiments, 2nd edn. CRC Press, Boca Raton (2009)zbMATHCrossRefGoogle Scholar
  105. 105.
    Missonnier, P., Deiber, M.P., Gold, G., Millet, P., Gex-Fabry Pun, M., Fazio-Costa, L., Giannakopoulos, P., Ibáñez, V.: Frontal theta event-related synchronization: comparison of directed attention and working memory load effects. J. Neural. Transm. 113(10), 1477–1486 (2006)CrossRefGoogle Scholar
  106. 106.
    Missonnier, P., Leonards, U., Gold, G., Palix, J., Ibáñez, V., Giannakopoulos, P.: A new electrophysiological index for working memory load in humans. Neuroreport 14(11), 1451–1455 (2003)CrossRefGoogle Scholar
  107. 107.
    Moody, D.: Can intelligence be increased by training on a task of working memory? Intelligence 37(4), 327–328 (2009)MathSciNetCrossRefGoogle Scholar
  108. 108.
    Morris, G., Nevet, A., Arkadir, D., Vaadia, E., Bergman, H.: Midbrain dopamine neurons encode decisions for future action. Nat. Neurosci. 9(8), 1057–1063 (2006)CrossRefGoogle Scholar
  109. 109.
    Morup, M., Hansen, L.K., Arnfred, S.M.: ERPWAVELAB a toolbox for multi-channel analysis of time-frequency transformed event related potentials. J. Neurosci. Methods 161(2), 361–368 (2007)CrossRefGoogle Scholar
  110. 110.
    Näätänen, R.: The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. Behav. Brain Sci. 13, 201–288 (1990)CrossRefGoogle Scholar
  111. 111.
    Nunez, P., Srinivasan, R.: Electric Fields of the Brain: The Neurophysics of EEG, 2nd edn. Oxford University Press, New York (2005)Google Scholar
  112. 112.
    Oberauer, K., Schulze, R., Wilhelm, O., Süss, H.M.: Working memory and intelligence–their correlation and their relation: comment on Ackerman, Beier, and Boyle. Psychol. Bull. 131(1), 61–65 (2005)CrossRefGoogle Scholar
  113. 113.
    Olesen, P.J., Macoveanu, J., Tegnér, J., Klingberg, T.: Brain activity related to working memory and distraction in children and adults. Cereb. Cortex 17(5), 1047–1054 (2007)CrossRefGoogle Scholar
  114. 114.
    Olesen, P.J., Westerberg, H., Klingberg, T.: Increased prefrontal and parietal activity after training of working memory. Nat. Neurosci. 7(1), 75–79 (2004)CrossRefGoogle Scholar
  115. 115.
    Oya, H., Adolphs, R., Kawasaki, H., Bechara, A., Damasio, A., Howard, M.A.: Electrophysiological correlates of reward prediction error recorded in the human prefrontal cortex. Proc. Natl. Acad. Sci. U S A 102(23), 8351–8356 (2005)CrossRefGoogle Scholar
  116. 116.
    Palva, J.M., Monto, S., Kulashekhar, S., Palva, S.: Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc. Natl. Acad. Sci. U S A 107(16), 7580–7585 (2010)CrossRefGoogle Scholar
  117. 117.
    Pan, X., Sawa, K., Tsuda, I., Tsukada, M., Sakagami, M.: Reward prediction based on stimulus categorization in primate lateral prefrontal cortex. Nat. Neurosci. 11(6), 703–712 (2008)CrossRefGoogle Scholar
  118. 118.
    Paxinos, G.: The Rat Nervous System, 3rd edn. Academic Press, London (2004)Google Scholar
  119. 119.
    Perrig, S., Dutoit, P., Espa-Cervena, K., Shaposhnyk, V., Pelletier, L., Berger, F., Villa, A.E.P.: Changes in quadratic phase coupling of EEG signals during wake and sleep in two chronic insomnia patients, before and after cognitive behavioral therapy. In: Apolloni, B., Bassis, S., Morabito, C.F. (eds.) Neural Nets WIRN 2009. Frontiers in Artificial Intelligence and Applications, vol. 204, pp. 217–228. IOS Press, Amsterdam (2009)Google Scholar
  120. 120.
    Pessoa, L., Gutierrez, E., Bandettini, P., Ungerleider, L.: Neural correlates of visual working memory: fMRI amplitude predicts task performance. Neuron 35(5), 975–987 (2002)CrossRefGoogle Scholar
  121. 121.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)CrossRefGoogle Scholar
  122. 122.
    Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007)CrossRefGoogle Scholar
  123. 123.
    Polich, J., Criado, J.R.: Neuropsychology and neuropharmacology of P3a and P3b. Int. J. Psychophysiol. 60(2), 172–185 (2006)CrossRefGoogle Scholar
  124. 124.
    Potts, G.F., Martin, L.E., Kamp, S.M., Donchin, E.: Neural response to action and reward prediction errors: Comparing the error-related negativity to behavioral errors and the feedback-related negativity to reward prediction violations. Psychophysiology 48(2), 218–228 (2011)CrossRefGoogle Scholar
  125. 125.
    Prut, Y., Vaadia, E., Bergman, H., Slovin, H., Abeles, M.: Spatiotemporal structure of cortical activity: Properties and behavioral relevance. J. Neurophysiol. 79, 2857–2874 (1998)Google Scholar
  126. 126.
    Raghavachari, S., Lisman, J.E., Tully, M., Madsen, J.R., Bromfield, E.B., Kahana, M.J.: Theta oscillations in human cortex during a working-memory task: evidence for local generators. J. Neurophysiol. 95(3), 1630–1638 (2006)CrossRefGoogle Scholar
  127. 127.
    Roberts, P.D., Bell, C.C.: Spike timing dependent synaptic plasticity in biological systems. Biological Cybernetics 87, 392–403 (2002)zbMATHCrossRefGoogle Scholar
  128. 128.
    Roth, A., Prasnikar, V., Okuno-Fujiwara, M., Zamir, S.: Bargaining and market behavior in Jerusalem, Ljubljana, Pittsburgh, and Tokyo: An experimental study. Am. Econ. Rev. 81(5), 1068–1095 (1991)Google Scholar
  129. 129.
    Rowe, J.B., Toni, I., Josephs, O., Frackowiak, R.S., Passingham, R.E.: The prefrontal cortex: response selection or maintenance within working memory? Science 288(5471), 1656–1660 (2000)CrossRefGoogle Scholar
  130. 130.
    Rushworth, M.F.S., Behrens, T.E.J.: Choice, uncertainty and value in prefrontal and cingulate cortex. Nature Neuroscience 11, 389–397 (2008)CrossRefGoogle Scholar
  131. 131.
    Sakai, K., Rowe, J.B., Passingham, R.E.: Active maintenance in prefrontal area 46 creates distractor-resistant memory. Nat. Neurosci. 5(5), 479–484 (2002)Google Scholar
  132. 132.
    Samejima, K., Ueda, Y., Doya, K., Kimura, M.: Representation of action-specific reward values in the striatum. Science 310, 1337–1340 (2005)CrossRefGoogle Scholar
  133. 133.
    Sanders, A.F.: Issues and trends in the debate on discrete vs. continuous processing of information. Acta Psychol. 74, 123–167 (1990)CrossRefGoogle Scholar
  134. 134.
    Sanfey, A.G., Rilling, J.K., Aronson, J.A., Nystrom, L.E., Cohen, J.D.: The neural basis of economic decision-making in the Ultimatum Game. Science 300(5626), 1755–1758 (2003)CrossRefGoogle Scholar
  135. 135.
    Schultz, W.: Behavioral dopamine signals. Trends Neurosci. 30, 203–210 (2007)CrossRefGoogle Scholar
  136. 136.
    Shmiel, T., Drori, R., Shmiel, O., Ben-Shaul, Y., Nadasdy, Z., Shemesh, M., Teicher, M., Abeles, M.: Neurons of the cerebral cortex exhibit precise inter-spike timing in correspondence to behavior. Proc. Natl. Acad. Sci. USA  102, 18,655–18,657 (2005)Google Scholar
  137. 137.
    Slonim, R., Roth, A.E.: Learning in high stakes Ultimatum Games: An experiment in the Slovak Republic. Econometrica 66(3), 569–596 (1998)zbMATHCrossRefGoogle Scholar
  138. 138.
    Sternberg, S.: The discovery of processing stages: Extensions of Donders’ method. In: Koster, W.G. (ed.) Attention and Performance II. Acta Psychologica, vol. 30, pp. 276–315. North-Holland, Amsterdam (1969)Google Scholar
  139. 139.
    Sunde, U., Dohmen, T., Falk, A., Huffman, D.: Are risk aversion and impatience related to cognitive ability? Am. Econ. Rev. 100, 1238–1260 (2010)CrossRefGoogle Scholar
  140. 140.
    Tallon-Baudry, C.: The roles of gamma-band oscillatory synchrony in human visual cognition. Front. Biosci. 14, 321–332 (2009)CrossRefGoogle Scholar
  141. 141.
    Taylor, J.G., Villa, A.E.P.: The “Conscious I”: A Neuroheuristic Approach to the Mind. In: Baltimore, D., Dulbecco, R., Jacob, F., Levi Montalcini, R. (eds.) Frontiers of Life, vol. III, pp. 349–368. Academic Press, London (2001) ISBN: 0-12-077340-6Google Scholar
  142. 142.
    Tecce, J.J.: Contingent negative variation (CNV) and psychological processes in man. Psychol. Bull. 77(2), 73–108 (1972)CrossRefGoogle Scholar
  143. 143.
    Tetko, I.V., Villa, A.E.P.: Fast combinatorial methods to estimate the probability of complex temporal patterns of spikes. Biological Cybernetics 76(5), 397–408 (1997)zbMATHCrossRefGoogle Scholar
  144. 144.
    Tetko, I.V., Villa, A.E.P.: A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Meth. 105, 1–14 (2001)CrossRefGoogle Scholar
  145. 145.
    Thaler, R.H.: From Homo economicus to Homo sapiens. Journal of Economic Perspectives 14, 133–141 (2000)CrossRefGoogle Scholar
  146. 146.
    Tlauka, M.: Stimulus-response sets with multiple dimensional overlap: Asymmetric effects are influenced by the degree of overlap. Austral. J. Psychol. 57, 28–37 (2005)CrossRefGoogle Scholar
  147. 147.
    Todd, J.J., Marois, R.: Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428(6984), 751–754 (2004)CrossRefGoogle Scholar
  148. 148.
    Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A.: The neural basis of loss aversion in decision-making under risk. Science 315(5811), 515–518 (2007)CrossRefGoogle Scholar
  149. 149.
    Travis, F., Tecce, J.J.: Effects of distracting stimuli on CNV amplitude and reaction time. Int. J. Psychophysiol. 31(1), 45–50 (1998)CrossRefGoogle Scholar
  150. 150.
    Verleger, R., Jaśkowski, P., Wascher, E.: Evidence for an integrative role of P3b in linking reaction to perception. J. Psychophysiol. 19(3), 165–181 (2005)CrossRefGoogle Scholar
  151. 151.
    Villa, A.E.P.: Empirical Evidence about Temporal Structure in Multi-unit Recordings. In: Miller, R. (ed.) Time and the Brain. Conceptual Advances in Brain Research, ch. 1, vol. 3, pp. 1–51. Harwood Academic, Amsterdam (2000)CrossRefGoogle Scholar
  152. 152.
    Villa, A.E.P.: Neuroheuristics, a new paradigm in neuroscience. Rev. Med. Suisse Romande 120(9), 743–748 (2000)Google Scholar
  153. 153.
    Villa, A.E.P.: Neural Coding in the Neuroheuristic Perspective. In: Barbieri, M. (ed.) The Codes of Life: The Rules of Macroevolution, Biosemiotics, ch. 16, vol. 1, pp. 357–377. Springer, Berlin (2008)Google Scholar
  154. 154.
    Villa, A.E.P., Eriksson, J., Eriksson, C., Haeberli, C., Hyland, B., Najem, A.: A novel Go/Nogo conflict paradigm in rats suggests an interaction between stimulus evaluation and response systems. Behav. Proc. 48, 69–88 (1999)CrossRefGoogle Scholar
  155. 155.
    Villa, A.E.P., Hyland, B., Tetko, I.V., Najem, A.: Dynamical cell assemblies in the rat auditory cortex in a reaction-time task. BioSystems 48, 269–278 (1998)CrossRefGoogle Scholar
  156. 156.
    Villa, A.E.P., Tetko, I.V.: Spatiotemporal activity patterns detected from single cell measurements from behaving animals. In: Lindblad, T., Padgett, M.L., Kinser, J.M. (eds.) Virtual Intelligence/Dynamic Neural Networks: Signals from the Brain. Proceedings of SPIE, vol. 3728, pp. 20–34. SPIE, San Jose (1999)Google Scholar
  157. 157.
    Villa, A.E.P., Tetko, I.V., Hyland, B., Najem, A.: Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task. Proc. Natl. Acad. Sci. U S A 96(3), 1106–1111 (1999)CrossRefGoogle Scholar
  158. 158.
    Voyteka, B., Knighta, R.T.: Prefrontal cortex and basal ganglia contributions to visual working memory. PNAS 107, 18,167–18,172 (2010)Google Scholar
  159. 159.
    Wickens, C., Kramer, A., Vanasse, L., Donchin, E.: Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science 221(4615), 1080–1082 (1983)CrossRefGoogle Scholar
  160. 160.
    Windmann, S., Kutas, M.: Electrophysiological correlates of emotion-induced recognition bias. J. Cogn. Neurosci. 13 (2001)Google Scholar
  161. 161.
    Yang, J., Zhang, Q.: Electrophysiological correlates of decision-making in high-risk versus low-risk conditions of a gambling game. Psychophysiology (2011) (in press), doi:10.1111/j.1469-8986.2011.1202.xGoogle Scholar
  162. 162.
    Yu, R., Zhou, X.: To bet or not to bet? The error negativity or error-related negativity associated with risk-taking choices. J. Cogn. Neurosci. 21(4), 684–696 (2009)CrossRefGoogle Scholar
  163. 163.
    Yu, S., Huang, D., Singer, W., Nikolić, D.: A small world of neuronal synchrony. Cereb. Cortex 18, 2891–2901 (2008)CrossRefGoogle Scholar
  164. 164.
    Zappoli, R.: Permanent or transitory effects on neurocognitive components of the CNV complex induced by brain dysfunctions, lesions and ablations in humans. Int. J. Psychophysiol. 48(2), 189–220 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro E. P. Villa
    • 1
    • 2
  • Pascal Missonnier
    • 1
    • 3
  • Alessandra Lintas
    • 1
    • 4
  1. 1.Neuroheuristic Research Group, Department of Information Science, and LABEX, Faculty of Business and EconomicsUniversity of LausanneSwitzerland
  2. 2.INSERM U836; Grenoble Institute of NeuroscienceUniversité Joseph FourierGrenobleFrance
  3. 3.Division of General Psychiatry, Department of PsychiatryUniversity Hospitals of GenevaSwitzerland
  4. 4.Department of Medicine, Unit of AnatomyUniversity of FribourgSwitzerland

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