1932

Abstract

The right decision today may be the wrong decision tomorrow. We live in a world in which expectations, contingencies, and goals continually evolve and change. Thus, decisions do not occur in isolation but rather are tightly embedded in these streams of temporal dependencies. Accordingly, even relatively straightforward visual decisions must take into account not just the immediate sensory input but also past experiences and future goals and expectations. Here, we evaluate recent progress in understanding how the brain implements these dependencies. We show that visual decision-making relies on mechanisms of evidence accumulation and commitment that have been studied extensively under relatively static, isolated conditions but in general can operate much more flexibly. A deeper understanding of these mechanisms will require identifying the principles that govern this flexibility, which must operate across different timescales to produce effective decisions in uncertain and dynamic environments.

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2017-09-15
2024-03-28
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Literature Cited

  1. Adams RP, MacKay DJC. 2007. Bayesian online changepoint detection. arXiv: 0710.3742 [stat.ML]
  2. Adams WJ, Graf EW, Ernst MO. 2004. Experience can change the ‘light-from-above’ prior. Nat. Neurosci. 7:1057–58 [Google Scholar]
  3. Ahissar M, Hochstein S. 2004. The reverse hierarchy theory of visual perceptual learning. Trends Cogn. Sci. 8:457–64 [Google Scholar]
  4. Aston-Jones G, Cohen JD. 2005. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28:403–50 [Google Scholar]
  5. Atick JJ, Redlich AN. 1990. Towards a theory of early visual processing. Neural Comput 2:308–20 [Google Scholar]
  6. Attneave F. 1954. Some informational aspects of visual perception. Psychol. Rev. 61:183–93 [Google Scholar]
  7. Averbeck BB, Latham PE, Pouget A. 2006. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7:358–66 [Google Scholar]
  8. Barlow HB. 1961. Possible principles underlying the transformations of sensory messages. Sensory Communication217–34 Cambridge, MA: MIT Press [Google Scholar]
  9. Barnard GA. 1946. Sequential tests in industrial statistics. J. R. Stat. Soc. 8:Suppl. 11–26 [Google Scholar]
  10. Basso MA, Wurtz RH. 1997. Modulation of neuronal activity by target uncertainty. Nature 389:66–69 [Google Scholar]
  11. Basten U, Biele G, Heekeren HR, Fiebach CJ. 2010. How the brain integrates costs and benefits during decision making. PNAS 107:21767–72 [Google Scholar]
  12. Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. 2012. Canonical microcircuits for predictive coding. Neuron 76:695–711 [Google Scholar]
  13. Behrens TE, Woolrich MW, Walton ME, Rushworth MF. 2007. Learning the value of information in an uncertain world. Nat. Neurosci. 10:1214–21 [Google Scholar]
  14. Bejjanki VR, Beck JM, Lu Z-L, Pouget A. 2011. Perceptual learning as improved probabilistic inference in early sensory areas. Nat. Neurosci. 14:642–48 [Google Scholar]
  15. Bennur S, Gold JI. 2011. Distinct representations of a perceptual decision and the associated oculomotor plan in the monkey lateral intraparietal area. J. Neurosci. 31:913–21 [Google Scholar]
  16. Berniker M, Voss M, Kording K. 2010. Learning priors for Bayesian computations in the nervous system. PLOS ONE 5:e12686 [Google Scholar]
  17. Bitzer S, Park H, Blankenburg F, Kiebel SJ. 2014. Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model. Front. Hum. Neurosci. 8:102 [Google Scholar]
  18. Boehm U, Hawkins GE, Brown S, van Rijn H, Wagenmakers EJ. 2016. Of monkeys and men: impatience in perceptual decision-making. Psychon. Bull. Rev. 23:738–49 [Google Scholar]
  19. Boettiger CA, Mitchell JM, Tavares VC, Robertson M, Joslyn G. et al. 2007. Immediate reward bias in humans: fronto-parietal networks and a role for the catechol-O-methyltransferase 158Val/Val genotype. J. Neurosci. 27:14383–91 [Google Scholar]
  20. Bogacz R, Brown E, Moehlis J, 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. 113:700–65 [Google Scholar]
  21. Bogacz R, Larsen T. 2011. Integration of reinforcement learning and optimal decision-making theories of the basal ganglia. Neural Comput 23:817–51 [Google Scholar]
  22. Boot WR, Blakely DP, Simons DJ. 2011. Do action video games improve perception and cognition?. Front. Psychol. 2:226 [Google Scholar]
  23. Botvinick MM, Niv Y, Barto AC. 2008. Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113:262–80 [Google Scholar]
  24. Bronfman ZZ, Brezis N, Moran R, Tsetsos K, Donner T, Usher M. 2015. Decisions reduce sensitivity to subsequent information. Proc. R. Soc. B 282:20150228 [Google Scholar]
  25. Brown S, Heathcote A. 2005. Practice increases the efficiency of evidence accumulation in perceptual choice. J. Exp. Psychol. Hum. Percept. Perform. 31:289–98 [Google Scholar]
  26. Brown SD, Heathcote A. 2008. The simplest complete model of choice response time: linear ballistic accumulation. Cogn. Psychol. 57:153–78 [Google Scholar]
  27. Carlson T, Grol MJ, Verstraten FAJ. 2006. Dynamics of visual recognition revealed by fMRI. NeuroImage 32:892–905 [Google Scholar]
  28. Carrasco M. 2011. Visual attention: the past 25 years. Vis. Res. 51:1484–525 [Google Scholar]
  29. Chen M-Y, Jimura K, White CN, Maddox WT, Poldrack RA. 2015. Multiple brain networks contribute to the acquisition of bias in perceptual decision-making. Front. Neurosci. 9:63 [Google Scholar]
  30. Cicmil N, Cumming BG, Parker AJ, Krug K. 2015. Reward modulates the effect of visual cortical microstimulation on perceptual decisions. eLife 4:e07832 [Google Scholar]
  31. Cisek P, Puskas GA, El-Murr S. 2009. Decisions in changing conditions: the urgency-gating model. J. Neurosci. 29:11560–71 [Google Scholar]
  32. Cohen MX, Frank MJ. 2009. Neurocomputational models of basal ganglia function in learning, memory and choice. Behav. Brain Res. 199:141–56 [Google Scholar]
  33. Coppola DM, Purves HR, McCoy AN, Purves D. 1998. The distribution of oriented contours in the real world. PNAS 95:4002–6 [Google Scholar]
  34. Dayan P, Zemel RS. 1999. Statistical models and sensory attention. Proc. Ninth Int. Conf. Artif. Neural Netw., Edinburgh, Scotl., Sept. 7–101017–22
  35. de Gee JW, Knapen T, Donner TH. 2014. Decision-related pupil dilation reflects upcoming choice and individual bias. PNAS 111:E618–25 [Google Scholar]
  36. de Lafuente V, Jazayeri M, Shadlen MN. 2015. Representation of accumulating evidence for a decision in two parietal areas. J. Neurosci. 35:4306–18 [Google Scholar]
  37. de Lange FP, Rahnev DA, Donner TH, Lau H. 2013. Prestimulus oscillatory activity over motor cortex reflects perceptual expectations. J. Neurosci. 33:1400–10 [Google Scholar]
  38. Ding L, Gold JI. 2010. Caudate encodes multiple computations for perceptual decisions. J. Neurosci. 30:15747–59 [Google Scholar]
  39. Ding L, Gold JI. 2012. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22:1052–67 [Google Scholar]
  40. Ditterich J. 2006. Evidence for time-variant decision making. Eur. J. Neurosci. 24:3628–41 [Google Scholar]
  41. Doi E, Gauthier JL, Field GD, Shlens J, Sher A. et al. 2012. Efficient coding of spatial information in the primate retina. J. Neurosci. 32:16256–64 [Google Scholar]
  42. Dorris MC, Munoz DP. 1998. Saccadic probability influences motor preparation signals and time to saccadic initiation. J. Neurosci. 18:7015–26 [Google Scholar]
  43. Dosher BA, Jeter P, Liu J, Lu Z-L. 2013. An integrated reweighting theory of perceptual learning. PNAS 110:13678–83 [Google Scholar]
  44. Dosher BA, Lu Z-L. 1998. Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. PNAS 95:13988–93 [Google Scholar]
  45. Drugowitsch J, Moreno-Bote R, Churchland AK, Shadlen MN, Pouget A. 2012. The cost of accumulating evidence in perceptual decision making. J. Neurosci. 32:3612–28 [Google Scholar]
  46. Duysens J, Gulyás B, Maes H. 1991. Temporal integration in cat visual cortex: a test of Bloch's law. Vis. Res. 31:1517–28 [Google Scholar]
  47. Fearnhead P, Liu Z. 2007. On-line inference for multiple changepoint problems. J. R. Stat. Soc. B 69:589–605 [Google Scholar]
  48. Fechner GT. 1966 (1860). Elements of Psychophysics transl. HE Alder New York: Holt, Rinehart, and Wilson
  49. Feng S, Holmes P, Rorie A, Newsome WT. 2009. Can monkeys choose optimally when faced with noisy stimuli and unequal rewards?. PLOS Comput. Biol. 5:e1000284 [Google Scholar]
  50. Fischer J, Whitney D. 2014. Serial dependence in visual perception. Nat. Neurosci. 17:738–43 [Google Scholar]
  51. Fiser J, Berkes P, Orbán G, Lengyel M. 2010. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14:119–30 [Google Scholar]
  52. Fleming SM, Daw ND. 2017. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation. Psychol. Rev. 124:91–114 [Google Scholar]
  53. Fleming SM, Whiteley L, Hulme OJ, Sahani M, Dolan RJ. 2010. Effects of category-specific costs on neural systems for perceptual decision-making. J. Neurophysiol. 103:3238–47 [Google Scholar]
  54. Forstmann BU, Dutilh G, Brown S, Neumann J, von Cramon DY. et al. 2008. Striatum and pre-SMA facilitate decision-making under time pressure. PNAS 105:17538–42 [Google Scholar]
  55. Frank MJ, Badre D. 2012. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cereb. Cortex 22:509–26 [Google Scholar]
  56. Friston K. 2010. The free-energy principle: a unified brain theory. ? Nat. Rev. Neurosci. 11:127–38 [Google Scholar]
  57. Ganguli D, Simoncelli EP. 2014. Efficient sensory encoding and Bayesian inference with heterogeneous neural populations. Neural Comput 26:2103–34 [Google Scholar]
  58. Ghose GM, Yang T, Maunsell JH. 2002. Physiological correlates of perceptual learning in monkey V1 and V2. J. Neurophysiol. 87:1867–88 [Google Scholar]
  59. Girshick AR, Landy MS, Simoncelli EP. 2011. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. 14:926–32 [Google Scholar]
  60. Glaze CM, Kable JW, Gold JI. 2015. Normative evidence accumulation in unpredictable environments. eLife 4:e08825 [Google Scholar]
  61. Gold JI, Shadlen MN. 2002. Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron 36:299–308 [Google Scholar]
  62. Gold JI, Shadlen MN. 2003. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23:632–51 [Google Scholar]
  63. Gold JI, Shadlen MN. 2007. The neural basis of decision making. Annu. Rev. Neurosci. 30:535–74 [Google Scholar]
  64. Green CS, Li R, Bavelier D. 2010. Perceptual learning during action video game playing. Top. Cogn. Sci. 2:202–16 [Google Scholar]
  65. Green DM, Swets JA. 1966. Signal Detection Theory and Psychophysics New York: John Wiley & Sons, Inc.
  66. Grimaldi P, Lau H, Basso MA. 2015. There are things that we know that we know, and there are things that we do not know we do not know: confidence in decision-making. Neurosci. Biobehav. Rev. 55:88–97 [Google Scholar]
  67. Grzywacz NM, de Juan J. 2003. Sensory adaptation as Kalman filtering: theory and illustration with contrast adaptation. Network 14:465–82 [Google Scholar]
  68. Hanes DP, Schall JD. 1996. Neural control of voluntary movement initiation. Science 274:427–30 [Google Scholar]
  69. Hanks TD, Mazurek ME, Kiani R, Hopp E, Shadlen MN. 2011. Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J. Neurosci. 31:6339–52 [Google Scholar]
  70. Hedges JH, Stocker AA, Simoncelli EP. 2011. Optimal inference explains the perceptual coherence of visual motion stimuli. J. Vis. 11:614 [Google Scholar]
  71. Heekeren HR, Marrett S, Ungerleider LG. 2008. The neural systems that mediate human perceptual decision making. Nat. Rev. Neurosci. 9:467–79 [Google Scholar]
  72. Heitz RP, Schall JD. 2012. Neural mechanisms of speed-accuracy tradeoff. Neuron 76:616–28 [Google Scholar]
  73. Helmholtz H. 1924 (1867). Handbook of Physiological Optics transl. Optical Society of America's Southall Menasha, Wisc.: Opt. Soc. Am.
  74. Hikosaka O. 2007. Basal ganglia mechanisms of reward-oriented eye movement. Ann. N. Y. Acad. Sci. 1104:229–49 [Google Scholar]
  75. Horstmann G. 2015. The surprise-attention link: a review. Ann. N. Y. Acad. Sci. 1339:106–15 [Google Scholar]
  76. Horwitz GD, Newsome WT. 1999. Separate signals for target selection and movement specification in the superior colliculus. Science 284:1158–61 [Google Scholar]
  77. Jacobs RA. 2009. Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning. J. Vis. 9:422 [Google Scholar]
  78. James TW, Gauthier I. 2006. Repetition-induced changes in BOLD response reflect accumulation of neural activity. Hum. Brain Mapp. 27:37–46 [Google Scholar]
  79. Jazayeri M, Movshon JA. 2006. Optimal representation of sensory information by neural populations. Nat. Neurosci. 9:690–96 [Google Scholar]
  80. Jazayeri M, Movshon JA. 2007. A new perceptual illusion reveals mechanisms of sensory decoding. Nature 446:912–15 [Google Scholar]
  81. Joshi S, Li Y, Kalwani RM, Gold JI. 2016. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89:221–34 [Google Scholar]
  82. Karklin Y, Lewicki MS. 2009. Emergence of complex cell properties by learning to generalize in natural scenes. Nature 457:83–86 [Google Scholar]
  83. Katz LN, Yates JL, Pillow JW, Huk AC. 2016. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535:285–88 [Google Scholar]
  84. Kelly SP, O'Connell RG. 2015. The neural processes underlying perceptual decision making in humans: recent progress and future directions. J. Physiol. Paris 109:27–37 [Google Scholar]
  85. Kepecs A, Uchida N, Zariwala HA, Mainen ZF. 2008. Neural correlates, computation and behavioural impact of decision confidence. Nature 455:227–31 [Google Scholar]
  86. Kiani R, Shadlen MN. 2009. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324:759–64 [Google Scholar]
  87. Kim J-N, Shadlen MN. 1999. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2:176–85 [Google Scholar]
  88. Kloosterman NA, Meindertsma T, van Loon AM, Lamme VA, Bonneh YS, Donner TH. 2015. Pupil size tracks perceptual content and surprise. Eur. J. Neurosci. 41:1068–78 [Google Scholar]
  89. Knill DC, Richards W. 1996. Perception as Bayesian Inference New York: Cambridge Univ. Press
  90. Kohn A. 2007. Visual adaptation: physiology, mechanisms, and functional benefits. J. Neurophysiol. 97:3155–64 [Google Scholar]
  91. Kok P, Rahnev D, Jehee JFM, Lau HC, de Lange FP. 2012. Attention reverses the effect of prediction in silencing sensory signals. Cereb. Cortex 22:2197–206 [Google Scholar]
  92. Kording KP, Tenenbaum JB, Shadmehr R. 2007. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat. Neurosci. 10:779–86 [Google Scholar]
  93. Krugel LK, Biele G, Mohr PNC, Li S-C, Heekeren HR. 2009. Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions. PNAS 106:17951–56 [Google Scholar]
  94. Laming DRJ. 1968. Information Theory of Choice Reaction Time New York: Wiley
  95. Latham PE, Nirenberg S. 2005. Synergy, redundancy, and independence in population codes, revisited. J. Neurosci. 25:5195–206 [Google Scholar]
  96. Latimer KW, Yates JL, Meister ML, Huk AC, Pillow JW. 2015. Neuronal modeling. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349:184–87 [Google Scholar]
  97. Laughlin S. 1981. A simple coding procedure enhances a neuron's information capacity. Z. Naturforschung C 36:910–12 [Google Scholar]
  98. Law CT, Gold JI. 2008. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11:505–13 [Google Scholar]
  99. Law CT, Gold JI. 2009. Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat. Neurosci. 12:655–63 [Google Scholar]
  100. Lee TS, Mumford D. 2003. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20:1434–48 [Google Scholar]
  101. Link SW, Heath RA. 1975. A sequential theory of psychological discrimination. Psychometrika 40:77–105 [Google Scholar]
  102. Li W, Piëch V, Gilbert CD. 2004. Perceptual learning and top-down influences in primary visual cortex. Nat. Neurosci. 7:651–57 [Google Scholar]
  103. Lo C-C, Wang X-J. 2006. Cortico-basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nat. Neurosci. 9:956–63 [Google Scholar]
  104. Lorteije JAM, Zylberberg A, Ouellette BG, De Zeeuw CI, Sigman M, Roelfsema PR. 2015. The formation of hierarchical decisions in the visual cortex. Neuron 87:1344–56 [Google Scholar]
  105. Luce RD. 1986. Response Times: Their Role in Inferring Elementary Mental Organization New York: Oxford Univ. Press
  106. Luu L, Stocker AA. 2016. Choice-induced biases in perception. bioRxiv 043224 https://doi.org/10.1101/043224 [Crossref]
  107. Ma WJ, Beck JM, Latham PE, Pouget A. 2006. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9:1432–38 [Google Scholar]
  108. Macmillan NA, Creelman CD. 2004. Detection Theory: A User's Guide Mahwah, NJ: Lawrence Erlbaum
  109. Maddox WT. 2002. Toward a unified theory of decision criterion learning in perceptual categorization. J. Exp. Anal. Behav. 78:567–95 [Google Scholar]
  110. Mante V, Frazor RA, Bonin V, Geisler WS, Carandini M. 2005. Independence of luminance and contrast in natural scenes and in the early visual system. Nat. Neurosci. 8:1690–97 [Google Scholar]
  111. McGuire JT, Nassar MR, Gold JI, Kable JW. 2014. Functionally dissociable influences on learning rate in a dynamic environment. Neuron 84:870–81 [Google Scholar]
  112. Mulder MJ, Wagenmakers EJ, Ratcliff R, Boekel W, Forstmann BU. 2012. Bias in the brain: a diffusion model analysis of prior probability and potential payoff. J. Neurosci. 32:2335–43 [Google Scholar]
  113. Murphy PR, Boonstra E, Nieuwenhuis S. 2016. Global gain modulation generates time-dependent urgency during perceptual choice in humans. Nat. Commun. 7:13526 [Google Scholar]
  114. Nassar MR, Rumsey KM, Wilson RC, Parikh K, Heasly B, Gold JI. 2012. Rational regulation of learning dynamics by pupil-linked arousal systems. Nat. Neurosci. 15:1040–46 [Google Scholar]
  115. 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. 30:12366–78 [Google Scholar]
  116. Nienborg H, Cumming BG. 2009. Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature 459:89–92 [Google Scholar]
  117. Olshausen BA, Field DJ. 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–9 [Google Scholar]
  118. O'Reilly JX. 2013. Making predictions in a changing world—inference, uncertainty, and learning. Front. Neurosci. 7:105 [Google Scholar]
  119. Ortega PA, Stocker AA. 2016. Human decision-making under limited time. Advances in Neural Information Processing Systems 29 DD Lee, M Sugiyama, UV Luxburg, I Guyon, R Garnett 100–8 New York: Curran Associates Inc. [Google Scholar]
  120. Ossmy O, Moran R, Pfeffer T, Tsetsos K, Usher M, Donner TH. 2013. The timescale of perceptual evidence integration can be adapted to the environment. Curr. Biol. 23:981–86 [Google Scholar]
  121. Palmer J, Huk AC, Shadlen MN. 2005. The effect of stimulus strength on the speed and accuracy of a perceptual decision. J. Vis. 5:51 [Google Scholar]
  122. Parker AJ, Newsome WT. 1998. Sense and the single neuron: probing the physiology of perception. Annu. Rev. Neurosci. 21:227–77 [Google Scholar]
  123. Peelen MV, Kastner S. 2014. Attention in the real world: toward understanding its neural basis. Trends Cogn. Sci. 18:242–50 [Google Scholar]
  124. Petrov AA, Dosher BA, Lu Z-L. 2005. The dynamics of perceptual learning: an incremental reweighting model. Psychol. Rev. 112:715–43 [Google Scholar]
  125. Piëch V, Li W, Reeke GN, Gilbert CD. 2013. Network model of top-down influences on local gain and contextual interactions in visual cortex. PNAS 110:E4108–17 [Google Scholar]
  126. Platt ML, Glimcher PW. 1999. Neural correlates of decision variables in parietal cortex. Nature 400:233–38 [Google Scholar]
  127. Pleskac TJ, Busemeyer JR. 2010. Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. Psychol. Rev. 117:864–901 [Google Scholar]
  128. Ploran EJ, Nelson SM, Velanova K, Donaldson DI, Petersen SE, Wheeler ME. 2007. Evidence accumulation and the moment of recognition: dissociating perceptual recognition processes using fMRI. J. Neurosci. 27:11912–24 [Google Scholar]
  129. Posner MI, Snyder CR, Davidson BJ. 1980. Attention and the detection of signals. J. Exp. Psychol. 109:160–74 [Google Scholar]
  130. Preuschoff K, 't Hart BM, Einhäuser W. 2011. Pupil dilation signals surprise: evidence for noradrenaline's role in decision making. Front. Neurosci. 5:115 [Google Scholar]
  131. Purcell BA, Kiani R. 2016. Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy. PNAS 113:E4531–40 [Google Scholar]
  132. Rao RP, Ballard DH. 1999. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2:79–87 [Google Scholar]
  133. Rao V, DeAngelis GC, Snyder LH. 2012. Neural correlates of prior expectations of motion in the lateral intraparietal and middle temporal areas. J. Neurosci. 32:10063–74 [Google Scholar]
  134. Ratcliff R, Cherian A, Segraves M. 2003. A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J. Neurophysiol. 90:1392–407 [Google Scholar]
  135. Ratcliff R, Hasegawa YT, Hasegawa RP, Smith PL, Segraves MA. 2007. Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. J. Neurophysiol. 97:1756–74 [Google Scholar]
  136. Ratcliff R, McKoon G. 2008. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput 20:873–922 [Google Scholar]
  137. Ratcliff R, Smith PL. 2004. A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111:333–67 [Google Scholar]
  138. Reddi BA, Carpenter RH. 2000. The influence of urgency on decision time. Nat. Neurosci. 3:827–30 [Google Scholar]
  139. Ribas-Fernandes JJF, Solway A, Diuk C, McGuire JT, Barto AG. et al. 2011. A neural signature of hierarchical reinforcement learning. Neuron 71:370–79 [Google Scholar]
  140. Roach NW, McGraw PV. 2009. Dynamics of spatial distortions reveal multiple time scales of motion adaptation. J. Neurophysiol. 102:3619–26 [Google Scholar]
  141. Roitman JD, Shadlen MN. 2002. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22:9475–89 [Google Scholar]
  142. Rorie AE, Gao J, McClelland JL, Newsome WT. 2010. Integration of sensory and reward information during perceptual decision-making in lateral intraparietal cortex (LIP) of the macaque monkey. PLOS ONE 5:e9308 [Google Scholar]
  143. Ruderman DL. 1994. The statistics of natural images. Netw. Comput. Neural Syst. 5:517–48 [Google Scholar]
  144. Rushworth MF, Noonan MP, Boorman ED, Walton ME, Behrens TE. 2011. Frontal cortex and reward-guided learning and decision-making. Neuron 70:1054–69 [Google Scholar]
  145. Sagi D. 2011. Perceptual learning in vision research. Vis. Res. 51:1552–66 [Google Scholar]
  146. Salinas E. 2006. How behavioral constraints may determine optimal sensory representations. PLOS Biol 4:e387 [Google Scholar]
  147. Sara SJ, Bouret S. 2012. Orienting and reorienting: the locus coeruleus mediates cognition through arousal. Neuron 76:130–41 [Google Scholar]
  148. Scheibe C, Ullsperger M, Sommer W, Heekeren HR. 2010. Effects of parametrical and trial-to-trial variation in prior probability processing revealed by simultaneous electroencephalogram/functional magnetic resonance imaging. J. Neurosci. 30:16709–17 [Google Scholar]
  149. Schouten JF, Bekker JA. 1967. Reaction time and accuracy. Acta Psychol 27:143–53 [Google Scholar]
  150. Seitz AR, Watanabe T. 2009. The phenomenon of task-irrelevant perceptual learning. Vis. Res. 49:2604–10 [Google Scholar]
  151. Serences JT, Saproo S. 2010. Population response profiles in early visual cortex are biased in favor of more valuable stimuli. J. Neurophysiol. 104:76–87 [Google Scholar]
  152. Seriès P, Seitz AR. 2013. Learning what to expect (in visual perception). Front. Hum. Neurosci. 7:668 [Google Scholar]
  153. Seung HS, Sompolinsky H. 1993. Simple models for reading neuronal population codes. PNAS 90:10749–53 [Google Scholar]
  154. Shadlen MN, Kiani R, Newsome WT, Gold JI, Wolpert DM. et al. 2016. Comment on “Single-trial spike trains in parietal cortex reveal discrete steps during decision-making.”. Science 351:1406 [Google Scholar]
  155. Sherman MT, Seth AK, Barrett AB, Kanai R. 2015. Prior expectations facilitate metacognition for perceptual decision. Conscious. Cogn. 35:53–65 [Google Scholar]
  156. Siegel M, Buschman TJ, Miller EK. 2015. Cortical information flow during flexible sensorimotor decisions. Science 348:1352–55 [Google Scholar]
  157. Simen P, Cohen JD, Holmes P. 2006. Rapid decision threshold modulation by reward rate in a neural network. Neural Netw 19:1013–26 [Google Scholar]
  158. Simen P, Contreras D, Buck C, Hu P, Holmes P, Cohen JD. 2009. Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions. J. Exp. Psychol. Hum. Percept. Perform. 35:1865–97 [Google Scholar]
  159. Simon HA. 1972. Theories of bounded rationality. Decis. Organ. 1:161–76 [Google Scholar]
  160. Simoncelli EP, Olshausen BA. 2001. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24:1193–216 [Google Scholar]
  161. Sims CR. 2016. Rate-distortion theory and human perception. Cognition 152:181–98 [Google Scholar]
  162. Smith PL, Ratcliff R. 2004. Psychology and neurobiology of simple decisions. Trends Neurosci 27:161–68 [Google Scholar]
  163. Solomon SG, Kohn A. 2014. Moving sensory adaptation beyond suppressive effects in single neurons. Curr. Biol. 24:R1012–22 [Google Scholar]
  164. Sompolinsky H, Yoon H, Kang K, Shamir M. 2001. Population coding in neuronal systems with correlated noise. Phys. Rev. E 64:051904 [Google Scholar]
  165. Sternberg S. 2001. Separate modifiability, mental modules, and the use of pure and composite measures to reveal them. Acta Psychol 106:147–246 [Google Scholar]
  166. Stocker AA, Simoncelli EP. 2005. Sensory adaptation within a Bayesian framework for perception. Advances in Neural Information Processing Systems 18 Y Weiss, B Schölkopf, J Platt 1291–98 Cambridge, MA: MIT Press [Google Scholar]
  167. Stocker AA, Simoncelli EP. 2008. A Bayesian model of conditioned perception. Advances in Neural Information Processing Systems 20 JC Platt, D Koller, Y Singer, S Roweis 1409–416 Cambridge, MA: MIT Press [Google Scholar]
  168. Stocker AA, Simoncelli EP. 2006. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 9:578–85 [Google Scholar]
  169. Stone M. 1960. Models for choice reaction time. Psychometrika 25:251–60 [Google Scholar]
  170. Summerfield C, de Lange FP. 2014. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15:745–56 [Google Scholar]
  171. Summerfield C, Egner T. 2016. Feature-based attention and feature-based expectation. Trends Cogn. Sci. 20:401–4 [Google Scholar]
  172. Summerfield C, Koechlin E. 2010. Economic value biases uncertain perceptual choices in the parietal and prefrontal cortices. Front. Hum. Neurosci. 4:208 [Google Scholar]
  173. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  174. Swets J, Tanner WP, Birdsall TG. 1961. Decision processes in perception. Psychol. Rev. 68:301–40 [Google Scholar]
  175. Terman M, Terman JS. 1972. Concurrent variation of response bias and sensitivity in an operant-psychophysical test. Percept. Psychophys. 11:428–32 [Google Scholar]
  176. Trommershauser J, Maloney LT, Landy MS. 2003. Statistical decision theory and the selection of rapid, goal-directed movements. J. Opt. Soc. Am. A 20:1419–33 [Google Scholar]
  177. Tsetsos K, Gao J, McClelland JL, Usher M. 2012. Using time-varying evidence to test models of decision dynamics: bounded diffusion vs. the leaky competing accumulator model. Front. Neurosci. 6:79 [Google Scholar]
  178. Usher M, McClelland JL. 2001. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108:550–92 [Google Scholar]
  179. van den Berg R, Zylberberg A, Kiani R, Shadlen MN, Wolpert DM. 2016. Confidence is the bridge between multi-stage decisions. Curr. Biol. 26:3157–68 [Google Scholar]
  180. van Veen V, Krug MK, Carter CS. 2008. The neural and computational basis of controlled speed-accuracy tradeoff during task performance. J. Cogn. Neurosci. 20:1952–65 [Google Scholar]
  181. Veliz-Cuba A, Kilpatrick ZP, Josić K. 2016. Stochastic models of evidence accumulation in changing environments. SIAM Rev 58:264–89 [Google Scholar]
  182. Voss A, Rothermund K, Voss J. 2004. Interpreting the parameters of the diffusion model: an empirical validation. Mem. Cognit. 32:1206–20 [Google Scholar]
  183. Wald A. 1947. Sequential Analysis New York: Wiley
  184. Wang R, Wang J, Zhang J-Y, Xie X-Y, Yang Y-X. et al. 2016. Perceptual learning at a conceptual level. J. Neurosci. 36:2238–46 [Google Scholar]
  185. Wang Z, Stocker AA, Lee DD. 2016. Efficient neural codes that minimize Lp reconstruction error. Neural Comput 28:2656–86 [Google Scholar]
  186. Wark B, Fairhall A, Rieke F. 2009. Timescales of inference in visual adaptation. Neuron 61:750–61 [Google Scholar]
  187. Wark B, Lundstrom BN, Fairhall A. 2007. Sensory adaptation. Curr. Opin. Neurobiol. 17:423–29 [Google Scholar]
  188. Watanabe T, Sasaki Y. 2015. Perceptual learning: toward a comprehensive theory. Annu. Rev. Psychol. 66:197–221 [Google Scholar]
  189. Webster MA. 2011. Adaptation and visual coding. J. Vis. 11:53 [Google Scholar]
  190. Wei X-X, Stocker AA. 2015. A Bayesian observer model constrained by efficient coding can explain ‘anti-Bayesian’ percepts. Nat. Neurosci. 18:1509–17 [Google Scholar]
  191. Wei X-X, Stocker AA. 2016a. A new law of human perception. bioRxiv 091918 https://doi.org/10.1101/091918. December 2016 [Crossref]
  192. Wei X-X, Stocker AA. 2016b. Mutual information, fisher information, and efficient coding. Neural Comput 28:305–26 [Google Scholar]
  193. Weil RS, Furl N, Ruff CC, Symmonds M, Flandin G. et al. 2010. Rewarding feedback after correct visual discriminations has both general and specific influences on visual cortex. J. Neurophysiol. 104:1746–57 [Google Scholar]
  194. Wheeler ME, Woo SG, Ansel T, Tremel JJ, Collier AL. et al. 2015. The strength of gradually accruing probabilistic evidence modulates brain activity during a categorical decision. J. Cogn. Neurosci. 27:705–19 [Google Scholar]
  195. Wickelgren WA. 1977. Speed-accuracy tradeoff and information processing dynamics. Acta Psychol 41:67–85 [Google Scholar]
  196. Wilson RC, Nassar MR, Gold JI. 2010. Bayesian online learning of the hazard rate in change-point problems. Neural Comput 22:2452–76 [Google Scholar]
  197. Wu S, Nakahara H, Amari S. 2001. Population coding with correlation and an unfaithful model. Neural Comput 13:775–97 [Google Scholar]
  198. Wyart V, Nobre AC, Summerfield C. 2012. Dissociable prior influences of signal probability and relevance on visual contrast sensitivity. PNAS 109:3593–98 [Google Scholar]
  199. Zhang J-Y, Zhang G-L, Xiao L-Q, Klein SA, Levi DM, Yu C. 2010. Rule-based learning explains visual perceptual learning and its specificity and transfer. J. Neurosci. 30:12323–28 [Google Scholar]
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