Impulsivity pp 163-199 | Cite as

Engaging and Exploring: Cortical Circuits for Adaptive Foraging Decisions

  • David L. Barack
  • Michael L. PlattEmail author
Part of the Nebraska Symposium on Motivation book series (NSM, volume 64)


Impulsivity is a profound source of poor decision making, often bringing suffering to both person and polity. Although impulsivity attends psychiatric disorders such as addiction, pathological gambling, attention deficit/hyperactivity disorder, and obsessive–compulsive disorder, almost everyone makes impulsive decisions that disregard the long-term consequences of our actions in favor of the near-term allure of immediate temptations. Deliberating between long-term benefits and short-term rewards is also a hallmark of foraging decisions, probably the most fundamental of all challenges confronted by mobile organisms. Behavioral studies confirm theoretical predictions that foragers compute the value of current offers, track background reward rates over different temporal and spatial scales, and update strategies in response to changes in the environment. These observations suggest that the execution of foraging computations is fundamental for understanding the organization of the nervous system. Here, we describe a process model for making foraging choices that integrates the value of short-term options and compares that value to a decision threshold determined by long-term reward rates. In addition, the role of interrupts and optimization routines are here incorporated for the first time into a foraging framework, by adapting decision thresholds to changes in the environment. A core network of brain areas, including the ventromedial prefrontal cortex, the anterior cingulate cortex, and the posterior cingulate cortex, under the modulatory influence of dopamine and norepinephrine, executes these computations and implements these processes. Our model provocatively implies that maladaptive impulsive choices can result from dysregulated foraging neurocircuitry.


Pathological Gambling Posterior Cingulate Cortex Reward Rate Ventromedial Prefrontal Cortex Current Offer 
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.


  1. Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Neural Information Processing Systems Conference; A preliminary version of this work was presented at the aforementined conference., American Psychological Association.Google Scholar
  2. Addicott, M. A., Pearson, J. M., Kaiser, N., Platt, M. L., & McClernon, F. J. (2015). Suboptimal foraging behavior: A new perspective on gambling. Behavioral Neuroscience, 129(5), 656.PubMedPubMedCentralCrossRefGoogle Scholar
  3. Altshuler, D. L., & Clark, C. J. (2003). Darwin’s hummingbirds. Science, 300(5619), 588–589.PubMedCrossRefGoogle Scholar
  4. American Psychiatric Association. (2013). The diagnostic and statistical manual of mental disorders: DSM 5. Washington, DC: American Psychiatric Association.CrossRefGoogle Scholar
  5. Anderson, D. E., Vogel, E. K., & Awh, E. (2013). A common discrete resource for visual working memory and visual search. Psychological Science, 24(6).Google Scholar
  6. Aston-Jones, G. (2004). Locus coeruleus, A5 and A7 noradrenergic cell groups. In G. Paxinos (Ed.), The rat nervous system (pp. 259–294). San Diego: Elsevier Academic Press.CrossRefGoogle Scholar
  7. Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience, 28, 403–450.PubMedCrossRefGoogle Scholar
  8. Aston-Jones, G., & Waterhouse, B. (2016). Locus coeruleus: From global projection system to adaptive regulation of behavior. Brain Research, 1645, 75–78.PubMedCrossRefGoogle Scholar
  9. Barack, D. L., Gariépy, J.-F., & Platt, M. L. (2014). Expected value and expected information encoding in the posterior cingulate. Poster presented at Computational and Systems Neuroscience Conference 11, Salt Lake City, UT.Google Scholar
  10. Barack, D. L., & Gold, J. I. (2016). Temporal trade-offs in psychophysics. Current Opinion in Neurobiology, 37, 121–125.PubMedCrossRefGoogle Scholar
  11. Barack, D. L., Hayden, B. Y., Pearson, J. M., & Platt, M. L. (2012). Neural threshold for foraging decisions in posterior cingulate cortex. Poster presented at Computational and Systems Neuroscience Conference 9, Salt Lake City, UT.Google Scholar
  12. Barack, D. L., & Platt, M. L. (2013). Components of strategic decision mechanisms in posterior cingulate cortex. Poster presented at Computational and Systems Neuroscience Conference 10, Salt Lake City, UT.Google Scholar
  13. Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412–427.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Bartumeus, F., Catalan, J., Fulco, U., Lyra, M., & Viswanathan, G. (2002). Optimizing the encounter rate in biological interactions: Lévy versus Brownian strategies. Physical Review Letters, 88(9), 097901.PubMedCrossRefGoogle Scholar
  15. Bateson, M., & Kacelnik, A. (1997). Starlings’ preferences for predictable and unpredictable delays to food. Animal Behaviour, 53(6), 1129–1142.PubMedCrossRefGoogle Scholar
  16. Beierholm, U., Guitart-Masip, M., Economides, M., Chowdhury, R., Düzel, E., Dolan, R., et al. (2013). Dopamine modulates reward-related vigor. Neuropsychopharmacology, 38(8), 1495–1503.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Bendesky, A., Tsunozaki, M., Rockman, M. V., Kruglyak, L., & Bargmann, C. I. (2011). Catecholamine receptor polymorphisms affect decision-making in C. elegans. Nature, 472(7343), 313–318.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Berger-Tal, O., & Bar-David, S. (2015). Recursive movement patterns: Review and synthesis across species. Ecosphere, 6(9), 149.CrossRefGoogle Scholar
  19. Bernacchia, A., Seo, H., Lee, D., & Wang, X.-J. (2011). A reservoir of time constants for memory traces in cortical neurons. Nature Neuroscience, 14(3), 366–372.PubMedPubMedCentralCrossRefGoogle Scholar
  20. Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis (Exposition of a new theory on the measurement of risk). Comentarii Acad. Scient. Petropolis (translated in Econometrica), 5(22), 23–36.Google Scholar
  21. Bickel, W., & Marsch, L. A. (2001). Toward a behavioral economic understanding of drug dependence: Delay discounting processes. Addiction, 96(1), 73–86.PubMedCrossRefGoogle Scholar
  22. Bickel, W., Miller, M. L., Yi, R., Kowal, B. P., Lindquist, D. M., & Pitcock, J. A. (2007). Behavioral and neuroeconomics of drug addiction: Competing neural systems and temporal discounting processes. Drug and Alcohol Dependence, 90, S85–S91.PubMedCrossRefGoogle Scholar
  23. Björklund, A., & Dunnett, S. B. (2007). Dopamine neuron systems in the brain: An update. Trends in Neurosciences, 30(5), 194–202.PubMedCrossRefGoogle Scholar
  24. Blanchard, T. C., & Hayden, B. Y. (2014). Neurons in dorsal anterior cingulate cortex signal postdecisional variables in a foraging task. The Journal of Neuroscience, 34(2), 646–655.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Boorman, E. D., Behrens, T. E. J., Woolrich, M. W., & Rushworth, M. F. S. (2009). How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action. Neuron, 62(5), 733–743.PubMedCrossRefGoogle Scholar
  26. Boorman, E. D., Rushworth, M. F., & Behrens, T. E. (2013). Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multi-alternative choice. The Journal of Neuroscience, 33(6), 2242–2253.PubMedPubMedCentralCrossRefGoogle Scholar
  27. Bouret, S., & Richmond, B. J. (2010). Ventromedial and orbital prefrontal neurons differentially encode internally and externally driven motivational values in monkeys. The Journal of Neuroscience, 30(25), 8591–8601.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Boyd, L., Edwards, J., Siengsukon, C., Vidoni, E., Wessel, B., & Linsdell, M. (2009). Motor sequence chunking is impaired by basal ganglia stroke. Neurobiology of Learning and Memory, 92(1), 35–44.PubMedCrossRefGoogle Scholar
  29. Cain, M. S., Vul, E., Clark, K., & Mitroff, S. R. (2012). A Bayesian optimal foraging model of human visual search. Psychological Science, 23(9), 1047–1054.PubMedCrossRefGoogle Scholar
  30. Calhoun, A. J., & Hayden, B. Y. (2015). The foraging brain. Current Opinion in Behavioral Sciences, 5, 24–31.CrossRefGoogle Scholar
  31. Caraco, T. (1981). Energy budgets, risk and foraging preferences in dark-eyed juncos (Junco hyemalis). Behavioral Ecology and Sociobiology, 8(3), 213–217.CrossRefGoogle Scholar
  32. Carpenter, R. H. (1988). Movements of the eyes. London: Pion Limited.Google Scholar
  33. Chandler, D. J., Gao, W.-J., & Waterhouse, B. D. (2014). Heterogeneous organization of the locus coeruleus projections to prefrontal and motor cortices. Proceedings of the National Academy of Sciences (USA), 111(18), 6816–6821.CrossRefGoogle Scholar
  34. Chandler, D. J., Lamperski, C. S., & Waterhouse, B. D. (2013). Identification and distribution of projections from monoaminergic and cholinergic nuclei to functionally differentiated subregions of prefrontal cortex. Brain Research, 1522, 38–58.PubMedCrossRefGoogle Scholar
  35. Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 19(2), 129–136.CrossRefGoogle Scholar
  36. Clithero, J. A., & Rangel, A. (2014). Informatic parcellation of the network involved in the computation of subjective value. Social Cognitive and Affective Neuroscience, 9(9), 1289–1302.PubMedCrossRefGoogle Scholar
  37. Clutton-Brock, T., & Harvey, P. H. (1980). Primates, brains and ecology. Journal of Zoology, 190(3), 309–323.CrossRefGoogle Scholar
  38. Constantino, S. M., & Daw, N. D. (2015). Learning the opportunity cost of time in a patch-foraging task. Cognitive, Affective, & Behavioral Neuroscience, 15(4), 837–853.CrossRefGoogle Scholar
  39. Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58(3), 306–324.PubMedPubMedCentralCrossRefGoogle Scholar
  40. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215.PubMedCrossRefGoogle Scholar
  41. Cosgrove, G. R., & Rauch, S. L. (2003). Stereotactic cingulotomy. Neurosurgery Clinics of North America, 14(2), 225–235.PubMedCrossRefGoogle Scholar
  42. Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441(7095), 876–879.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Deserno, L., Huys, Q. J., Boehme, R., Buchert, R., Heinze, H.-J., Grace, A. A., et al. (2015). Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making. Proceedings of the National Academy of Sciences (USA), 112(5), 1595–1600.CrossRefGoogle Scholar
  44. Desrochers, T. M., Jin, D. Z., Goodman, N. D., & Graybiel, A. M. (2010). Optimal habits can develop spontaneously through sensitivity to local cost. Proceedings of the National Academy of Sciences (USA), 107(47), 20512–20517.CrossRefGoogle Scholar
  45. DiLeone, R. J. (2009). The influence of leptin on the dopamine system and implications for ingestive behavior. International Journal of Obesity, 33, S25–S29.PubMedPubMedCentralCrossRefGoogle Scholar
  46. Doll, B. B., Bath, K. G., Daw, N. D., & Frank, M. J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. The Journal of Neuroscience, 36(4), 1211–1222.PubMedPubMedCentralCrossRefGoogle Scholar
  47. Dukas, R. (2002). Behavioural and ecological consequences of limited attention. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 357(1427), 1539–1547.PubMedPubMedCentralCrossRefGoogle Scholar
  48. Dukas, R. R., & Jim, (2009). Cognitive ecology II. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
  49. Farmer, G. D., Janssen, C. P., & Brumby, D. P. (2011). How long have I got? Making optimal visit durations in a dual-task setting. Proceedings of the 33rd annual meeting of the Cognitive Science Society.Google Scholar
  50. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40, 351–401.CrossRefGoogle Scholar
  51. Freidin, E., Aw, J., & Kacelnik, A. (2009). Sequential and simultaneous choices: Testing the diet selection and sequential choice models. Behavioural Processes, 80(3), 218–223.PubMedCrossRefGoogle Scholar
  52. Fretwell, S. D., & Calver, J. S. (1969). On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheoretica, 19(1), 37–44.CrossRefGoogle Scholar
  53. Fu, W.-T., & Pirolli, P. (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web. Human-Computer Interaction, 22(4), 355–412.Google Scholar
  54. Fujii, N., & Graybiel, A. M. (2003). Representation of action sequence boundaries by macaque prefrontal cortical neurons. Science, 301(5637), 1246–1249.PubMedCrossRefGoogle Scholar
  55. Fujii, N., & Graybiel, A. M. (2005). Time-varying covariance of neural activities recorded in striatum and frontal cortex as monkeys perform sequential-saccade tasks. Proceedings of the National Academy of Sciences (USA), 102(25), 9032–9037.CrossRefGoogle Scholar
  56. Gallistel, C. R., & Gibbon, J. (2000). Time, rate, and conditioning. Psychological Review, 107(2), 289.PubMedCrossRefGoogle Scholar
  57. Genovesio, A., Wise, S. P., & Passingham, R. E. (2014). Prefrontal–parietal function: From foraging to foresight. Trends in Cognitive Sciences, 18(2), 72–81.PubMedCrossRefGoogle Scholar
  58. Gershman, S. J., Pesaran, B., & Daw, N. D. (2009). Human reinforcement learning subdivides structured action spaces by learning effector-specific values. The Journal of Neuroscience, 29(43), 13524–13531.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Gilzenrat, M. S., Nieuwenhuis, S., Jepma, M., & Cohen, J. D. (2010). Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function. Cognitive, Affective, & Behavioral Neuroscience, 10(2), 252–269.CrossRefGoogle Scholar
  60. Gittins, J. C. (1979). Bandit processes and dynamic allocation indices. Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 148–177.Google Scholar
  61. Gittins, J., & Jones, D. (1974). A dynamic allocation index for the sequential allocation of experiments. In J. M. Gani, K. Sarkadi, & I. Vincze (Eds.), Progress in statistics. Amsterdam: North Holland.Google Scholar
  62. Glimcher, P., & Fehr, E. (2013). Neuroeconomics: Decision making and the brain. San Diego, CA: Academic Press.Google Scholar
  63. Glimcher, P., Kable, J., & Louie, K. (2007). Neuroeconomic studies of impulsivity: Now or just as soon as possible? The American Economic Review, 97(2), 142–147.CrossRefGoogle Scholar
  64. Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574.PubMedCrossRefGoogle Scholar
  65. Graybiel, A. M. (2008). Habits, rituals, and the evaluative brain. Annual Review of Neuroscience, 31, 359–387.PubMedCrossRefGoogle Scholar
  66. Graybiel, A. M., & Rauch, S. L. (2000). Toward a neurobiology of obsessive-compulsive disorder. Neuron, 28(2), 343–347.PubMedCrossRefGoogle Scholar
  67. Greenberg, B. D., Rauch, S. L., & Haber, S. N. (2010). Invasive circuitry-based neurotherapeutics: Stereotactic ablation and deep brain stimulation for OCD. Neuropsychopharmacology, 35(1), 317–336.PubMedCrossRefGoogle Scholar
  68. Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217–229.PubMedCrossRefGoogle Scholar
  69. Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4), 263–268.CrossRefGoogle Scholar
  70. Guitart-Masip, M., Fuentemilla, L., Bach, D. R., Huys, Q. J., Dayan, P., Dolan, R. J., et al. (2011). Action dominates valence in anticipatory representations in the human striatum and dopaminergic midbrain. The Journal of Neuroscience, 31(21), 7867–7875.PubMedPubMedCentralCrossRefGoogle Scholar
  71. Hamilton, W. D. (1964). The genetical evolution of social behaviour. I. Journal of Theoretical Biology, 7(1), 1–16.PubMedCrossRefGoogle Scholar
  72. Harding, R. S., & Teleki, G. (1981). Omnivorous primates: Gathering and hunting in human evolution. New York: Columbia University Press.Google Scholar
  73. Hayden, B. Y., Nair, A. C., McCoy, A. N., & Platt, M. L. (2008). Posterior cingulate cortex mediates outcome-contingent allocation of behavior. Neuron, 60(1), 19–25.PubMedPubMedCentralCrossRefGoogle Scholar
  74. Hayden, B. Y., Pearson, J. M., & Platt, M. L. (2009). Fictive reward signals in the anterior cingulate cortex. Science, 324(5929), 948–950.PubMedPubMedCentralCrossRefGoogle Scholar
  75. Hayden, B. Y., Pearson, J. M., & Platt, M. L. (2011). Neuronal basis of sequential foraging decisions in a patchy environment. Nature Neuroscience, 14(7), 933–939.PubMedPubMedCentralCrossRefGoogle Scholar
  76. Heilbronner, S. R., & Haber, S. N. (2014). Frontal cortical and subcortical projections provide a basis for segmenting the cingulum bundle: Implications for neuroimaging and psychiatric disorders. The Journal of Neuroscience, 34(30), 10041–10054.PubMedPubMedCentralCrossRefGoogle Scholar
  77. Heilbronner, S. R., & Hayden, B. Y. (2016). Dorsal anterior cingulate cortex: A bottom-up view. Annual Review of Neuroscience, 39, 149–170.PubMedCrossRefGoogle Scholar
  78. Heilbronner, S. R., & Platt, M. L. (2013). Causal evidence of performance monitoring by neurons in posterior cingulate cortex during learning. Neuron, 80(6), 1384–1391.PubMedPubMedCentralCrossRefGoogle Scholar
  79. Hemmi, J. M., & Menzel, C. R. (1995). Foraging strategies of long-tailed macaques, Macaca fascicularis: Directional extrapolation. Animal Behaviour, 49(2), 457–464.CrossRefGoogle Scholar
  80. Hills, T. (2010). Investigating mathematical search behavior using network analysis. In R. Lesh, P. L. Galbraith, C. R. Hains, & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 571–581). New York: Springer.CrossRefGoogle Scholar
  81. Hills, T., Brockie, P. J., & Maricq, A. V. (2004). Dopamine and glutamate control area-restricted search behavior in Caenorhabditis elegans. The Journal of neuroscience, 24(5), 1217–1225.PubMedCrossRefGoogle Scholar
  82. Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119(2), 431.PubMedCrossRefGoogle Scholar
  83. Hills, T. T., Mata, R., Wilke, A., & Samanez-Larkin, G. R. (2013). Mechanisms of age-related decline in memory search across the adult life span. Developmental Psychology, 49(12), 2396.PubMedCrossRefGoogle Scholar
  84. Hills, T. T., & Pachur, T. (2012). Dynamic search and working memory in social recall. Journal of Experimental Psychology. Learning, Memory, and Cognition, 38(1), 218.PubMedCrossRefGoogle Scholar
  85. Hills, T. T., Todd, P. M., & Goldstone, R. L. (2008). Search in external and internal spaces evidence for generalized cognitive search processes. Psychological Science, 19(8), 802–808.PubMedCrossRefGoogle Scholar
  86. Hills, T. T., Todd, P. M., & Goldstone, R. L. (2010). The central executive as a search process: Priming exploration and exploitation across domains. Journal of Experimental Psychology: General, 139(4), 590.CrossRefGoogle Scholar
  87. Hills, T. T., Todd, P. M., & Jones, M. N. (2015). Foraging in semantic fields: How we search through memory. Topics in Cognitive Science, 7(3), 513–534.PubMedCrossRefGoogle Scholar
  88. Hunt, L. T., Woolrich, M. W., Rushworth, M. F., & Behrens, T. E. (2013). Trial-type dependent frames of reference for value comparison. PLoS Computational Biology, 9(9), e1003225.PubMedPubMedCentralCrossRefGoogle Scholar
  89. Janmaat, K. R. L., Byrne, R. W., & Zuberbühler, K. (2006). Evidence for a spatial memory of fruiting states of rainforest trees in wild mangabeys. Animal Behaviour, 72(4), 797–807.CrossRefGoogle Scholar
  90. Janssen, C. P., Brumby, D. P., Dowell, J., Chater, N., & Howes, A. (2011). Identifying optimum performance trade-offs using a cognitively bounded rational analysis model of discretionary task interleaving. Topics in Cognitive Science, 3(1), 123–139.PubMedCrossRefGoogle Scholar
  91. Jepma, M., & Nieuwenhuis, S. (2011). Pupil diameter predicts changes in the exploration–exploitation trade-off: Evidence for the adaptive gain theory. Journal of Cognitive Neuroscience, 23(7), 1587–1596.PubMedCrossRefGoogle Scholar
  92. Jin, X., Tecuapetla, F., & Costa, R. M. (2014). Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nature Neuroscience, 17(3), 423–430.PubMedPubMedCentralCrossRefGoogle Scholar
  93. Joshi, S., Li, Y., Kalwani, R. M., & Gold, J. I. (2016). Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron, 89(1), 221–234.PubMedCrossRefGoogle Scholar
  94. Kacelnik, A. (1997). Normative and descriptive models of decision making: Time discounting and risk sensitivity. In G. R. Bock & G. Cardew (Eds.), Characterizing human psychological adaptations (pp. 51–67). Chichester, UK: Wiley.Google Scholar
  95. Kacelnik, A., & Bateson, M. (1996). Risky theories: The effects of variance on foraging decisions. American Zoologist, 36, 402–434.CrossRefGoogle Scholar
  96. Kacelnik, A., Vasconcelos, M., Monteiro, T., & Aw, J. (2011). Darwin’s tug-of-war vs. starlings’ horse-racing: How adaptations for sequential encounters drive simultaneous choice. Behavioral Ecology and Sociobiology, 65(3), 547–558.CrossRefGoogle Scholar
  97. Kolling, N., Behrens, T. E. J., Mars, R. B., & Rushworth, M. F. S. (2012). Neural mechanisms of foraging. Science, 336(6077), 95–98.PubMedPubMedCentralCrossRefGoogle Scholar
  98. Kolling, N., Behrens, T., Wittmann, M., & Rushworth, M. (2016). Multiple signals in anterior cingulate cortex. Current Opinion in Neurobiology, 37, 36–43.PubMedPubMedCentralCrossRefGoogle Scholar
  99. Kolling, N., Wittmann, M., & Rushworth, M. F. (2014). Multiple neural mechanisms of decision making and their competition under changing risk pressure. Neuron, 81(5), 1190–1202.PubMedPubMedCentralCrossRefGoogle Scholar
  100. Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298.PubMedCrossRefGoogle Scholar
  101. Krebs, J. R., Kacelnik, A., & Taylor, P. (1978). Test of optimal sampling by foraging great tits. Nature, 275(5675), 27–31.CrossRefGoogle Scholar
  102. Lebreton, M., Jorge, S., Michel, V., Thirion, B., & Pessiglione, M. (2009). An automatic valuation system in the human brain: Evidence from functional neuroimaging. Neuron, 64(3), 431–439.PubMedCrossRefGoogle Scholar
  103. Leech, R., Kamourieh, S., Beckmann, C. F., & Sharp, D. J. (2011). Fractionating the default mode network: Distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. The Journal of Neuroscience, 31(9), 3217–3224.PubMedCrossRefGoogle Scholar
  104. Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(1), 12–32.PubMedCrossRefGoogle Scholar
  105. Levitt, P., & Moore, R. Y. (1978). Noradrenaline neuron innervation of the neocortex in the rat. Brain Research, 139(2), 219–231.PubMedCrossRefGoogle Scholar
  106. Lim, S.-L., O’Doherty, J. P., & Rangel, A. (2011). The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. The Journal of Neuroscience, 31(37), 13214–13223.PubMedCrossRefGoogle Scholar
  107. Litt, A., Plassmann, H., Shiv, B., & Rangel, A. (2011). Dissociating valuation and saliency signals during decision-making. Cerebral Cortex, 21(1), 95–102.PubMedCrossRefGoogle Scholar
  108. Loewenstein, G. (1996). Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65(3), 272–292.CrossRefGoogle Scholar
  109. MacGregor, J. N., & Chu, Y. (2011). Human performance on the traveling salesman and related problems: A review. The Journal of Problem Solving, 3(2), 2.CrossRefGoogle Scholar
  110. Maia, T. V., Cooney, R. E., & Peterson, B. S. (2008). The neural bases of obsessive–compulsive disorder in children and adults. Development and Psychopathology, 20(4), 1251–1283.PubMedPubMedCentralCrossRefGoogle Scholar
  111. Marije Boonstra, A., Oosterlaan, J., Sergeant, J. A., & Buitelaar, J. K. (2005). Executive functioning in adult ADHD: A meta-analytic review. Psychological Medicine, 35(08), 1097–1108.PubMedCrossRefGoogle Scholar
  112. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information New York: Henry Holt and Co Inc.Google Scholar
  113. McGuire, J. T., & Kable, J. W. (2015). Medial prefrontal cortical activity reflects dynamic re-evaluation during voluntary persistence. Nature Neuroscience, 18(5), 760–766.PubMedPubMedCentralCrossRefGoogle Scholar
  114. McGuire, J. T., Nassar, M. R., Gold, J. I., & Kable, J. W. (2014). Functionally dissociable influences on learning rate in a dynamic environment. Neuron, 84(4), 870–881.PubMedPubMedCentralCrossRefGoogle Scholar
  115. Meder, D., Haagensen, B. N., Hulme, O., Morville, T., Gelskov, S., Herz, D. M., et al. (2016). Tuning the brake while raising the stake: Network dynamics during sequential decision-making. The Journal of Neuroscience, 36(19), 5417–5426.PubMedPubMedCentralCrossRefGoogle Scholar
  116. Menzel, E. W. (1973). Chimpanzee spatial memory organization. Science, 182(4115), 943–945.PubMedCrossRefGoogle Scholar
  117. Menzel, C. R. (1991). Cognitive aspects of foraging in Japanese monkeys. Animal Behaviour, 41(3), 397–402.CrossRefGoogle Scholar
  118. Menzel, C. (1996). Structure-guided foraging in long-tailed macaques. American Journal of Primatology, 38(2), 117–132.CrossRefGoogle Scholar
  119. Menzies, L., Chamberlain, S. R., Laird, A. R., Thelen, S. M., Sahakian, B. J., & Bullmore, E. T. (2008). Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: The orbitofronto-striatal model revisited. Neuroscience and Biobehavioral Reviews, 32(3), 525–549.PubMedCrossRefGoogle Scholar
  120. Metcalfe, J., & Jacobs, W. J. (2010). People’s study time allocation and its relation to animal foraging. Behavioural Processes, 83(2), 213–221.PubMedCrossRefGoogle Scholar
  121. Milton, K. (1981). Distribution patterns of tropical plant foods as an evolutionary stimulus to primate mental development. American Anthropologist, 83(3), 534–548.CrossRefGoogle Scholar
  122. Milton, K. (1988). Foraging behaviour and the evolution of primate intelligence. In A. W. R. W. Byrne (Ed.), Machiavellian intelligence: Social expertise and the evolution of intellect in monkeys, apes, and humans (pp. 285–305). New York: Clarendon Press.Google Scholar
  123. Nassar, M. R., Rumsey, K. M., Wilson, R. C., Parikh, K., Heasly, B., & Gold, J. I. (2012). Rational regulation of learning dynamics by pupil-linked arousal systems. Nature Neuroscience, 15(7), 1040–1046.PubMedPubMedCentralCrossRefGoogle Scholar
  124. Nassar, M. R., Wilson, R. C., Heasly, B., & Gold, J. I. (2010). An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. The Journal of Neuroscience, 30(37), 12366–12378.PubMedPubMedCentralCrossRefGoogle Scholar
  125. Nenadic, I. (2008). Targeting brain regions and symptoms: Neuronal single-unit recordings and deep brain stimulation in obsessive-compulsive disorder. Biological Psychiatry, 63(6), 542–543.PubMedCrossRefGoogle Scholar
  126. Newell, A. (1994). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  127. Niv, Y., Daw, N. D., & Dayan, P. (2005). How fast to work: Response vigor, motivation and tonic dopamine. Proceedings of the 18th International Conference on Neural Information Processing Systems, NIPS’05 (pp. 1019–1026). Cambridge, MA: MIT Press.Google Scholar
  128. Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007). Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology (Berl), 191(3), 507–520.CrossRefGoogle Scholar
  129. Nonacs, P. (2001). State dependent behavior and the marginal value theorem. Behavioral Ecology, 12(1), 71–83.CrossRefGoogle Scholar
  130. Noonan, M. P., Walton, M. E., Behrens, T. E. J., Sallet, J., Buckley, M. J., & Rushworth, M. F. S. (2010). Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex. Proceedings of the National Academy of Sciences (USA), 107(47), 20547–20552.CrossRefGoogle Scholar
  131. Noser, R., & Byrne, R. W. (2010). How do wild baboons (Papio ursinus) plan their routes? Travel among multiple high-quality food sources with inter-group competition. Animal Cognition, 13(1), 145–155.PubMedCrossRefGoogle Scholar
  132. Ohashi, K., & Thomson, J. D. (2005). Efficient harvesting of renewing resources. Behavioral Ecology, 16(3), 592–605.CrossRefGoogle Scholar
  133. Ohashi, K., & Thomson, J. D. (2009). Trapline foraging by pollinators: Its ontogeny, economics and possible consequences for plants. Annals of Botany, 103(9), 1365–1378.PubMedPubMedCentralCrossRefGoogle Scholar
  134. Pammi, V. C., Miyapuram, K. P., Samejima, K., Bapi, R. S., & Doya, K. (2012). Changing the structure of complex visuo-motor sequences selectively activates the fronto-parietal network. Neuroimage, 59(2), 1180–1189.PubMedCrossRefGoogle Scholar
  135. Passingham, R. E., & Wise, S. P. (2012). The neurobiology of the prefrontal cortex: Anatomy, evolution, and the origin of insight. Oxford: Oxford University Press.CrossRefGoogle Scholar
  136. Paton, J. J., Belova, M. A., Morrison, S. E., & Salzman, C. D. (2006). The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature, 439(7078), 865–870.PubMedPubMedCentralCrossRefGoogle Scholar
  137. Payne, S., & Duggan, G. (2011). Giving up problem solving. Memory & Cognition, 39(5), 902–913.CrossRefGoogle Scholar
  138. Payne, S. J., Duggan, G. B., & Neth, H. (2007). Discretionary task interleaving: Heuristics for time allocation in cognitive foraging. Journal of Experimental Psychology: General, 136(3), 370.CrossRefGoogle Scholar
  139. Payzan-LeNestour, E., Dunne, S., Bossaerts, P., & O’Doherty, J. P. (2013). The neural representation of unexpected uncertainty during value-based decision making. Neuron, 79(1), 191–201.PubMedPubMedCentralCrossRefGoogle Scholar
  140. Pearson, J. M., Hayden, B. Y., Raghavachari, S., & Platt, M. L. (2009). Neurons in posterior cingulate cortex signal exploratory decisions in a dynamic multioption choice task. Currrent Biology, 19(18), 1532–1537.CrossRefGoogle Scholar
  141. Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011). Posterior cingulate cortex: Adapting behavior to a changing world. Trends in Cognitive Sciences, 15(4), 143–151.PubMedPubMedCentralCrossRefGoogle Scholar
  142. Pirolli, P. L. T. (2007). Information foraging theory: Adaptive interaction with information. Oxford: Oxford University Press.CrossRefGoogle Scholar
  143. Plassmann, H., O’Doherty, J. P., & Rangel, A. (2010). Appetitive and aversive goal values are encoded in the medial orbitofrontal cortex at the time of decision making. The Journal of Neuroscience, 30(32), 10799–10808.PubMedCrossRefGoogle Scholar
  144. Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400(6741), 233–238.PubMedCrossRefGoogle Scholar
  145. Platt, M., & Plassmann, H. (2014). Multistage valuation signals and common neural currencies. In P. W. Glimcher & E. Fehr (Eds.), Neuroeconomics (2nd ed., pp. 237–258). San Diego, CA: Academic Press.CrossRefGoogle Scholar
  146. Potenza, M. N. (2008). The neurobiology of pathological gambling and drug addiction: An overview and new findings. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 363(1507), 3181–3189.PubMedPubMedCentralCrossRefGoogle Scholar
  147. Procyk, E., Tanaka, Y. L., & Joseph, J. P. (2000). Anterior cingulate activity during routine and non-routine sequential behaviors in macaques. Nature Neuroscience, 3(5), 502–508.PubMedCrossRefGoogle Scholar
  148. Pylyshyn, Z. W. (2007). Things and places: How the mind connects with the world. Cambridge, MA: MIT press.Google Scholar
  149. Ranade, S., Hangya, B., & Kepecs, A. (2013). Multiple modes of phase locking between sniffing and whisking during active exploration. The Journal of Neuroscience, 33(19), 8250–8256.PubMedPubMedCentralCrossRefGoogle Scholar
  150. Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed choice. Current Opinion in Neurobiology, 20(2), 262–270.PubMedCrossRefGoogle Scholar
  151. Real, L. A. (1991). Animal choice behavior and the evolution of cognitive architecture. Science, 253(5023), 980–986.PubMedCrossRefGoogle Scholar
  152. Reboreda, J., & Kacelnik, A. (1991). Risk sensitivity in starlines: Variability in food amount and food delay. Behavioral Ecology, 2, 301–308.CrossRefGoogle Scholar
  153. Redish, A. (2012). Search processes and hippocampus. In P. M. Todd, T. T. Hills, & T. W. Robbins (Eds.), Cognitive search: Evolution, algorithms, and the brain (pp. 81–95). Cambridge, MA: MIT Press.Google Scholar
  154. Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. The Journal of Neuroscience, 22(21), 9475–9489.PubMedGoogle Scholar
  155. Rushworth, M. F., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011). Frontal cortex and reward-guided learning and decision-making. Neuron, 70(6), 1054–1069.PubMedCrossRefGoogle Scholar
  156. Rutledge, R. B., Lazzaro, S. C., Lau, B., Myers, C. E., Gluck, M. A., & Glimcher, P. W. (2009). Dopaminergic drugs modulate learning rates and perseveration in Parkinson’s patients in a dynamic foraging task. The Journal of Neuroscience, 29(48), 15104–15114.PubMedPubMedCentralCrossRefGoogle Scholar
  157. Sadacca, B. F., Jones, J. L., & Schoenbaum, G. (2016). Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework. eLife, 5, e13665.Google Scholar
  158. Saez, A., Rigotti, M., Ostojic, S., Fusi, S., & Salzman, C. (2015). Abstract context representations in primate amygdala and prefrontal cortex. Neuron, 87(4), 869–881.PubMedPubMedCentralCrossRefGoogle Scholar
  159. Sakai, K., Kitaguchi, K., & Hikosaka, O. (2003). Chunking during human visuomotor sequence learning. Experimental Brain Research, 152(2), 229–242.PubMedCrossRefGoogle Scholar
  160. Sara, S. J., & Bouret, S. (2012). Orienting and reorienting: The locus coeruleus mediates cognition through arousal. Neuron, 76(1), 130–141.PubMedCrossRefGoogle Scholar
  161. Saxena, S., Brody, A. L., Schwartz, J. M., & Baxter, L. R. (1998). Neuroimaging and frontal-subcortical circuitry in obsessive-compulsive disorder. British Journal of Psychiatry, Suppl(35), 26–37.Google Scholar
  162. Schultz, W. (1998). The phasic reward signal of primate dopamine neurons. Advances in Pharmacology, 42, 686–690.PubMedCrossRefGoogle Scholar
  163. Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annual Review of Psychology, 57, 87–115.PubMedCrossRefGoogle Scholar
  164. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.PubMedCrossRefGoogle Scholar
  165. Schultz, W., Tremblay, L., & Hollerman, J. R. (1998). Reward prediction in primate basal ganglia and frontal cortex. Neuropharmacology, 37(4–5), 421–429.PubMedCrossRefGoogle Scholar
  166. Seo, H., & Lee, D. (2007). Temporal filtering of reward signals in the dorsal anterior cingulate cortex during a mixed-strategy game. The Journal of Neuroscience, 27(31), 8366–8377.PubMedPubMedCentralCrossRefGoogle Scholar
  167. Sergeant, J. A., Geurts, H., & Oosterlaan, J. (2002). How specific is a deficit of executive functioning for attention-deficit/hyperactivity disorder? Behavioural Brain Research, 130(1), 3–28.PubMedCrossRefGoogle Scholar
  168. Shagrir, O. (2010). Marr on computational-level theories. Philosophy of Science, 77(4), 477–500.CrossRefGoogle Scholar
  169. Sharp, M. E., Foerde, K., Daw, N. D., & Shohamy, D. (2015). Dopamine selectively remediates ‘model-based’ reward learning: A computational approach. Brain, 139, 355–364.PubMedCrossRefGoogle Scholar
  170. Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 1286–1291.PubMedCrossRefGoogle Scholar
  171. Shenhav, A., Straccia, M. A., Cohen, J. D., & Botvinick, M. M. (2014). Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nature Neuroscience, 17(9), 1249–1254.PubMedPubMedCentralCrossRefGoogle Scholar
  172. Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4), 443–464.CrossRefGoogle Scholar
  173. Shidara, M., & Richmond, B. J. (2002). Anterior cingulate: Single neuronal signals related to degree of reward expectancy. Science, 296(5573), 1709–1711.PubMedCrossRefGoogle Scholar
  174. Smith, D. V., Hayden, B. Y., Truong, T. K., Song, A. W., Platt, M. L., & Huettel, S. A. (2010). Distinct value signals in anterior and posterior ventromedial prefrontal cortex. The Journal of Neuroscience, 30(7), 2490–2495.PubMedPubMedCentralCrossRefGoogle Scholar
  175. Stephens, D. (2008). Decision ecology: Foraging and the ecology of animal decision making. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 475–484.CrossRefGoogle Scholar
  176. Stephens, D. W., & Anderson, D. (2001). The adaptive value of preference for immediacy: When shortsighted rules have farsighted consequences. Behavioral Ecology, 12(3), 330–339.CrossRefGoogle Scholar
  177. Stephens, D., Couzin, I. D., & Giraldeau, L. (2012). Ecological and behavioral approaches to search behavior. In P. M. Todd, T. T. Hills, & T. W. Robbins (Eds.), Cognitive search: Evolution, algorithms, and the brain (pp. 25–45). Cambridge, MA: MIT Press.Google Scholar
  178. Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton, NJ: Princeton University Press.Google Scholar
  179. Steyvers, M., Lee, M. D., & Wagenmakers, E.-J. (2009). A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53(3), 168–179.CrossRefGoogle Scholar
  180. Strait, C. E., Blanchard, T. C., & Hayden, B. Y. (2014). Reward value comparison via mutual inhibition in ventromedial prefrontal cortex. Neuron, 82(6), 1357–1366.PubMedPubMedCentralCrossRefGoogle Scholar
  181. Summerfield, C., Behrens, T. E., & Koechlin, E. (2011). Perceptual classification in a rapidly changing environment. Neuron, 71(4), 725–736.PubMedPubMedCentralCrossRefGoogle Scholar
  182. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.Google Scholar
  183. Tan, C. M. (2008). Simulated annealing. Croatia: InTech.Google Scholar
  184. Tian, J., Huang, R., Cohen, J. Y., Osakada, F., Kobak, D., Machens, C. K., et al. (2016). Distributed and mixed information in monosynaptic inputs to dopamine neurons. Neuron, 91(6), 1374–1389.PubMedCrossRefGoogle Scholar
  185. Tinklepaugh, O. L. (1932). The multiple delayed reaction with chimpanzees and monkeys. Journal of Comparative Psychology, 13(2), 207.CrossRefGoogle Scholar
  186. Tremblay, P.-L., Bedard, M.-A., Langlois, D., Blanchet, P. J., Lemay, M., & Parent, M. (2010). Movement chunking during sequence learning is a dopamine-dependant process: A study conducted in Parkinson’s disease. Experimental Brain Research, 205(3), 375–385.PubMedCrossRefGoogle Scholar
  187. Tremblay, L., Hollerman, J. R., & Schultz, W. (1998). Modifications of reward expectation-related neuronal activity during learning in primate striatum. Journal of Neurophysiology, 80(2), 964–977.PubMedGoogle Scholar
  188. Trivers, R. L. (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, 46(1), 35–57.CrossRefGoogle Scholar
  189. Trivers, R. L. (1972). Parental investment and sexual selection. In B. Campbell (Ed.), Sexual selection and the descent of man (pp. 136–179). Chicago: Aldine de Gruyter.Google Scholar
  190. Vallone, D., Picetti, R., & Borrelli, E. (2000). Structure and function of dopamine receptors. Neuroscience and Biobehavioral Reviews, 24(1), 125–132.PubMedCrossRefGoogle Scholar
  191. van Laarhoven, P., & Aarts, E. (1987). Simulated annealing: Theory and applications. New York: Springer Science & Business Media.Google Scholar
  192. van Meel, C. S., Heslenfeld, D. J., Oosterlaan, J., & Sergeant, J. A. (2007). Adaptive control deficits in attention-deficit/hyperactivity disorder (ADHD): The role of error processing. Psychiatry Research, 151(3), 211–220.PubMedCrossRefGoogle Scholar
  193. Verwey, W. B. (1996). Buffer loading and chunking in sequential keypressing. Journal of Experimental Psychology: Human Perception and Performance, 22(3), 544.Google Scholar
  194. Verwey, W. B. (2001). Concatenating familiar movement sequences: The versatile cognitive processor. Acta Psychologica, 106(1), 69–95.PubMedCrossRefGoogle Scholar
  195. Vogt, B. A., Rosene, D. L., & Pandya, D. N. (1979). Thalamic and cortical afferents differentiate anterior from posterior cingulate cortex in the monkey. Science, 204(4389), 205–207.PubMedCrossRefGoogle Scholar
  196. Westendorff, S., D., Everling, K. S., & Womelsdorf, T. (2016). Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior. Journal of Neurophysiology, (in press).Google Scholar
  197. Wilke, A., Hutchinson, J., Todd, P. M., & Czienskowski, U. (2009). Fishing for the right words: Decision rules for human foraging behavior in internal search tasks. Cognitive Science, 33(3), 497–529.PubMedCrossRefGoogle Scholar
  198. Williams, G. C. (1966). Natural selection, the costs of reproduction, and a refinement of Lack’s principle. The American Naturalist, 100(916), 687–690.CrossRefGoogle Scholar
  199. Williams, S. M., & Goldman-Rakic, P. S. (1993). Characterization of the dopaminergic innervation of the primate frontal cortex using a dopamine-specific antibody. Cerebral Cortex, 3(3), 199–222.PubMedCrossRefGoogle Scholar
  200. Willingham, D. B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105(3), 558.PubMedCrossRefGoogle Scholar
  201. Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014). Humans use directed and random exploration to solve the explore–exploit dilemma. Journal of Experimental Psychology: General, 143(6), 2074.CrossRefGoogle Scholar
  202. Wittmann, B. C., Daw, N. D., Seymour, B., & Dolan, R. J. (2008). Striatal activity underlies novelty-based choice in humans. Neuron, 58(6), 967–973.PubMedPubMedCentralCrossRefGoogle Scholar
  203. Wittmann, M. K., Kolling, N., Akaishi, R., Chau, B. K., Brown, J. W., Nelissen, N., et al. (2016). Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex. Nature Communications, 7, 12327.PubMedPubMedCentralCrossRefGoogle Scholar
  204. Wolfe, J. M. (2013). When is it time to move to the next raspberry bush? Foraging rules in human visual search. Journal of Vision, 13(3), 10–10.PubMedPubMedCentralCrossRefGoogle Scholar
  205. Woods, S. P., Lovejoy, D. W., & Ball, J. D. (2002). Neuropsychological characteristics of adults with ADHD: A comprehensive review of initial studies. The Clinical Neuropsychologist, 16(1), 12–34.PubMedCrossRefGoogle Scholar
  206. Wunderlich, K., Dayan, P., & Dolan, R. J. (2012). Mapping value based planning and extensively trained choice in the human brain. Nature Neuroscience, 15(5), 786–791.PubMedPubMedCentralCrossRefGoogle Scholar
  207. Wunderlich, K., Rangel, A., & O’Doherty, J. P. (2010). Economic choices can be made using only stimulus values. Proceedings of the National Academy of Sciences (USA), 107(34), 15005–15010.CrossRefGoogle Scholar
  208. Wymbs, N. F., Bassett, D. S., Mucha, P. J., Porter, M. A., & Grafton, S. T. (2012). Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans. Neuron, 74(5), 936–946.PubMedPubMedCentralCrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Philosophy, Department of Neuroscience, Department of Economics, and Center for Science and SocietyColumbia UniversityNew YorkUSA
  2. 2.Department of NeuroscienceUniversity of PennsylvaniaPhiladelphiaUSA

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