Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Decision-Making Tasks

  • Angela J. YuEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_314-1


A diverse repertoire of behavioral tasks has been employed to examine the cognitive processes and neural basis underlying decision-making in humans and animals. Some of these have their origins in psychology, others in cognitive neuroscience, yet others in economics. There is also a continual invention of novel or hybrid paradigms, sometimes motivated by deep conceptual questions stimulated by computational modeling of decision-making and sometimes motivated by specific hypotheses related to functions of neuronal systems and brain regions that are suspected of playing an important role in decision-making.

Detailed Description

The area of decision-making is a dynamically evolving, multifaceted area of active research that sits at the interfaces of many areas, among them psychology, neuroscience, economics, finance, political science, engineering, and mathematics. The range of decision-making tasks seen in the literature is extremely diverse, reflecting the interdisciplinary...


Color Word Multisensory Integration Reward Rate Outcome Uncertainty Response Deadline 
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  1. Battaglia PW, Jacobs RA, Aslin RN (2003) Bayesian integration of visual and auditory signals for spatial localization. J Opt Soc Am A Opt Image Sci Vis 20(7):1391–1397PubMedCrossRefGoogle Scholar
  2. Behrens TEJ, Woolrich MW, Walton ME, Rushworth MFS (2007) Learning the value of information in an uncertain world. Nature Neurosci 10(9):1214–1221PubMedCrossRefGoogle Scholar
  3. Bogacz R, Brown E, Moehlis J, Hu P, Holmes P, Cohen JD (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced choice tasks. Psychol Rev 113(4):700–765PubMedCrossRefGoogle Scholar
  4. Busemeyer JR, Townsend JT (1993) Decision field theory. Psychol Rev 100:432–459PubMedCrossRefGoogle Scholar
  5. Cho RY, Nystrom LE, Brown ET, Jone AD, Braver TS, Holmes PJ, Cohen JD (2002) Mechanisms underlying dependencies of performance on stimulus history in a two-alternative forced choice task. Cogn Affect Behav Neurosci 2:283–299PubMedCrossRefGoogle Scholar
  6. Churchland AK, Kiani R, Shadlen MN (2008) Decision-making with multiple alternatives. Nat Neurosci 11(6):693–702PubMedCentralPubMedCrossRefGoogle Scholar
  7. Dayan P, Kakade S, Montague PR (2000) Learning and selective attention. Nat Rev Neurosci 3:1218–1223CrossRefGoogle Scholar
  8. Devauges V, Sara SJ (1990) Activation of the noradrenergic system facilitates an attentional shift in the rat. Behav Brain Res 39(1):19–28PubMedCrossRefGoogle Scholar
  9. Donders FC (1969) On the speed of mental processes. Acta Psychol (Amst) 30:412CrossRefGoogle Scholar
  10. Driver J, Noesselt T (2008) Multisensory interplay reveals crossmodal influences on “sensory-specific” brain regions, neural responses, and judgments. Neuron Rev 57:11–23CrossRefGoogle Scholar
  11. Eriksen BA, Eriksen CW (1974) Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept Psychophys 16:143–149CrossRefGoogle Scholar
  12. Ernst MO, Banks MS (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415(6870):429–433PubMedCrossRefGoogle Scholar
  13. Gold JI, Shadlen MN (2002) Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron 36:299–308PubMedCrossRefGoogle Scholar
  14. Gomez P, Ratcliff R, Perea M (2007) A model of the go-nogo task. J Exp Psychol 136(3):389–413. doi:10.1037/0096-3445.136.3.389.ACrossRefGoogle Scholar
  15. Heath T, Chatterjee S (1995) Asymmetric decoy effects on lower-quality versus higher-quality brands: meta-analytic and experimental evidence. J Consum Res 22:268–284CrossRefGoogle Scholar
  16. Herrnstein RJ (1961) Relative and absolute strength of responses as a function of frequency of reinforcement. J Exp Anal Behav 4:267–272PubMedCentralPubMedCrossRefGoogle Scholar
  17. Herrnstein RJ (1970) On the law of effect. J Exp Anal Behav 13:243–266PubMedCentralPubMedCrossRefGoogle Scholar
  18. Huber J, Payne J (1982) Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. J Consum Res 9(1):90–98Google Scholar
  19. Jacobs RA (1999) Optimal integration of texture and motion cues in depth. Vis Res 39:3621–3629PubMedCrossRefGoogle Scholar
  20. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decisions under risk. Econometrica 47:313–327CrossRefGoogle Scholar
  21. Kahneman D, Slovic P, Tversky A (eds) (1982) Judgement under uncertainty: heuristics and biases. Cambridge University Press, Cambridge, UKGoogle Scholar
  22. Kording KP, Beierholm U, Ma W, Quartz S, Tenenbaum J, Shams L (2007) Causal inference in cue combination. PLoS One 2(9):e943PubMedCentralPubMedCrossRefGoogle Scholar
  23. Laming DRJ (1968) Information theory of choice-reaction times. Academic, LondonGoogle Scholar
  24. Leotti LA, Wager TD (2009) Motivational influences on response inhibition measures. J Exp Psychol Hum Percept Perform 36(2):430–447CrossRefGoogle Scholar
  25. Logan G, Cowan W (1984) On the ability to inhibit thought and action: a theory of an act of control. Psych Rev 91:295–327CrossRefGoogle Scholar
  26. Nassar MR, Wilson RC, Heasly B, Gold JI (2010) An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. J Neurosci 30(37):12366–12378PubMedCentralPubMedCrossRefGoogle Scholar
  27. Nassar MR, Rumsey KM, Wilson RC, Parikh K, Heasly B, Gold JI (2012) Rational regulation of learning dynamics by pupil-linked arousal systems. Nature Neurosci 15(7):1040–1046Google Scholar
  28. Newsome WT, Paré EB (1988) A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J Neurosci 8(6):2201–2211PubMedGoogle Scholar
  29. Ratcliff R, Rouder JN (1998) Modeling response times for two-choice decisions. Psychol Sci 9:347–356CrossRefGoogle Scholar
  30. Ratcliff R, Smith PL (2004) A comparison of sequential sampling models for two-choice reaction time. Psychol Rev 111:333–346PubMedCentralPubMedCrossRefGoogle Scholar
  31. Roitman JD, Shadlen MN (2002) Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J Neurosci 22(21):9475–9489PubMedGoogle Scholar
  32. Salinas E, Stanford TR (2013) Waiting is the hardest part: comparison of two computational strategies for performing a compelled-response task. Front Comput Neurosci 33(13):5668–5685Google Scholar
  33. Salinas E, Shankar S, Gabriela Costello M, Zhu D, Stanford TR (2010) Waiting is the hardest part: comparison of two computational strategies for performing a compelled-response task. Front Comput Neurosci. doi:10.3389/fn-com.2010.00153PubMedCentralPubMedGoogle Scholar
  34. Shadlen MN, Newsome WT (2001) Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J Neurophysiol 86:1916–1935PubMedGoogle Scholar
  35. Shams L, Kamitani Y, Shimojo S (2000) What you see is what you hear. Nature 408:788PubMedCrossRefGoogle Scholar
  36. Shams L, Ma WJ, Beierholm U (2005) Sound-induced flash illusion as an optimal percept. Neuroreport 16(17):1923–1927PubMedCrossRefGoogle Scholar
  37. Shenoy P, Yu AJ (2011) Rational decision-making in inhibitory control. Front Hum Neurosci. doi:10.3389/fnhum.2011.00048PubMedCentralPubMedGoogle Scholar
  38. Shenoy P, Yu AJ (2012) Strategic impatience in Go/NoGo versus forced-choice decision-making. Adv Neural Inf Process Syst 25Google Scholar
  39. Shenoy P, Yu AJ (2013) A rational account of contextual effects in preference choice: what makes for a bargain? In: Proceedings of the thirty-fifth annual conference of the cognitive science society. Berlin, GermanyGoogle Scholar
  40. Simon JR (1967) Ear preference in a simple reaction-time task. J Exp Psychol 75(1):49–55PubMedCrossRefGoogle Scholar
  41. Simonson I (1989) Choice based on reasons: the case of attraction and compromise effects. J Consum Res 16:158–157CrossRefGoogle Scholar
  42. Stroop JR (1935) Studies of interference in serial verbal reactions. J Exp Psychol Gen 18:643–662CrossRefGoogle Scholar
  43. Trueblood JS (2012) Multialternative context effects obtained using an inference task. Psychon Bull Rev 19(5):962–968. doi:10.3758/s13423-012-0288-9PubMedCrossRefGoogle Scholar
  44. Tversky A (n.d.) Elimination by aspects: a theory of choice. Psychol Rev 79:288–299Google Scholar
  45. Usher M, McClelland JL (2001) The time course of perceptual choice: the leaky, competing accumulator model. Psychol Rev 108(3):550–592PubMedCrossRefGoogle Scholar
  46. Usher M, McClelland J (2004) Loss aversion and inhibition in dynamical models of multialternative choice. Psychol Rev 111(3):757–769Google Scholar
  47. Wald A (1947) Sequential analysis. Wiley, New YorkGoogle Scholar
  48. Wald A, Wolfowitz J (1948) Optimal character of the sequential probability ratio test. Ann Math Statist 19:326–339CrossRefGoogle Scholar
  49. Yu AJ, Cohen JD (2009) Sequential effects: superstition or rational behavior? Adv Neural Inf Process Syst 21:1873–1880Google Scholar
  50. Yu AJ, Dayan P (2005) Uncertainty, neuromodulation, and attention. Neuron 46:681–692PubMedCrossRefGoogle Scholar
  51. Yu AJ, Huang H (2014) Maximizing masquerading as matching in human visual search choice behavior. Decision (To appear)Google Scholar
  52. Yu AJ, Dayan P, Cohen JD (2009) Dynamics of attentional selection under conflict: toward a rational Bayesian account. J Exp Psychol Hum Percept Perform 35:700–717PubMedCentralPubMedCrossRefGoogle Scholar

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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Cognitive ScienceUniversity of California, San DiegoLa JollaUSA