1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Physiology
  4. Volume 85, 2023
  5. Article

Annual Review of Physiology

Volume 85, 2023

Neural Mechanisms That Make Perceptual Decisions Flexible

Abstract

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

Keyword(s): decision policy, distributed neural networks, flexible decision making, sensory-guided behavior, stimulus-action mapping, task switch
Loading

Article metrics loading...

/content/journals/10.1146/annurev-physiol-031722-024731
2023-02-10
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/physiol/85/1/annurev-physiol-031722-024731.html?itemId=/content/journals/10.1146/annurev-physiol-031722-024731&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Waskom ML, Okazawa G, Kiani R. 2019. Designing and interpreting psychophysical investigations of cognition. Neuron 104:1100–12
     [Google Scholar]
  2. 2.
    O'Connell RG, Kelly SP 2021. Neurophysiology of human perceptual decision-making. Annu. Rev. Neurosci. 44:495–516
     [Google Scholar]
  3. 3.
    Hanks TD, Summerfield C. 2017. Perceptual decision making in rodents, monkeys, and humans. Neuron 93:115–31
     [Google Scholar]
  4. 4.
    Shadlen MN, Kiani R. 2013. Decision making as a window on cognition. Neuron 80:3791–806
     [Google Scholar]
  5. 5.
    Miller EK, Cohen JD. 2001. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24:167–202
     [Google Scholar]
  6. 6.
    Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. 2013. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16:91348–55
     [Google Scholar]
  7. 7.
    Britten KH, Shadlen MN, Newsome WT, Movshon JA. 1992. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12:124745–65
     [Google Scholar]
  8. 8.
    Roitman JD, Shadlen MN. 2002. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22:219475–89
     [Google Scholar]
  9. 9.
    Drugowitsch J, DeAngelis GC, Klier EM, Angelaki DE, Pouget A. 2014. Optimal multisensory decision-making in a reaction-time task. eLife 3:e03005
     [Google Scholar]
  10. 10.
    Okazawa G, Sha L, Kiani R 2021. Linear integration of sensory evidence over space and time underlies face categorization. J. Neurosci. 41:377876–93
     [Google Scholar]
  11. 11.
    Drugowitsch J, Moreno-Bote R, Churchland AK, Shadlen MN, Pouget A. 2012. The cost of accumulating evidence in perceptual decision making. J. Neurosci. 32:113612–28
     [Google Scholar]
  12. 12.
    Deneve S. 2012. Making decisions with unknown sensory reliability. Front. Neurosci. 6:75
     [Google Scholar]
  13. 13.
    Khalvati K, Kiani R, Rao RPN. 2021. Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy. Nat. Commun. 12:5704
     [Google Scholar]
  14. 14.
    Gold JI, Shadlen MN. 2007. The neural basis of decision making. Annu. Rev. Neurosci. 30:535–74
     [Google Scholar]
  15. 15.
    Mochol G, Kiani R, Moreno-Bote R. 2021. Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior. Curr. Biol. 31:61234–44.e6
     [Google Scholar]
  16. 16.
    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. 31:176339–52
     [Google Scholar]
  17. 17.
    Urai AE, de Gee JW, Tsetsos K, Donner TH. 2019. Choice history biases subsequent evidence accumulation. eLife 8:e46331
     [Google Scholar]
  18. 18.
    Noorbaloochi S, Sharon D, McClelland JL 2015. Payoff information biases a fast guess process in perceptual decision making under deadline pressure: evidence from behavior, evoked potentials, and quantitative model comparison. J. Neurosci. 35:3110989–1011
     [Google Scholar]
  19. 19.
    Wang XJ. 2002. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36:5955–68
     [Google Scholar]
  20. 20.
    Wong KF, Wang XJ. 2006. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26:41314–28
     [Google Scholar]
  21. 21.
    Beck JM, Ma WJ, Kiani R, Hanks T, Churchland AK et al. 2008. Probabilistic population codes for Bayesian decision making. Neuron 60:61142–52
     [Google Scholar]
  22. 22.
    Palmer J, Huk AC, Shadlen MN. 2005. The effect of stimulus strength on the speed and accuracy of a perceptual decision. J. Vis. 5:5376–404
     [Google Scholar]
  23. 23.
    Smith PL, Vickers D. 1988. The accumulator model of two-choice discrimination. J. Math. Psychol. 32:2135–68
     [Google Scholar]
  24. 24.
    Kiani R, Shadlen MN. 2009. Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324:5928759–64
     [Google Scholar]
  25. 25.
    Kiani R, Corthell L, Shadlen MN. 2014. Choice certainty is informed by both evidence and decision time. Neuron 84:61329–42
     [Google Scholar]
  26. 26.
    Horwitz GD, Newsome WT. 1999. Separate signals for target selection and movement specification in the superior colliculus. Science 284:54171158–61
     [Google Scholar]
  27. 27.
    Ding L, Gold JI. 2010. Caudate encodes multiple computations for perceptual decisions. J. Neurosci. 30:4715747–59
     [Google Scholar]
  28. 28.
    Ding L, Gold JI. 2012. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22:51052–67
     [Google Scholar]
  29. 29.
    Kim B, Basso MA. 2008. Saccade target selection in the superior colliculus: a signal detection theory approach. J. Neurosci. 28:122991–3007
     [Google Scholar]
  30. 30.
    Donner TH, Siegel M, Fries P, Engel AK. 2009. Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr. Biol. 19:181581–85
     [Google Scholar]
  31. 31.
    O'Connell RG, Dockree PM, Kelly SP. 2012. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15:121729–35
     [Google Scholar]
  32. 32.
    Philiastides MG, Heekeren HR, Sajda P. 2014. Human scalp potentials reflect a mixture of decision-related signals during perceptual choices. J. Neurosci. 34:5016877–89
     [Google Scholar]
  33. 33.
    Shadlen MN, Newsome WT. 2001. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86:41916–36
     [Google Scholar]
  34. 34.
    Shadlen MN, Newsome WT. 1996. Motion perception: seeing and deciding. PNAS 93:2628–33
     [Google Scholar]
  35. 35.
    Kiani R, Cueva CJ, Reppas JB, Newsome WT. 2014. Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials. Curr. Biol. 24:131542–47
     [Google Scholar]
  36. 36.
    Mante V, Sussillo D, Shenoy KV, Newsome WT. 2013. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503:747478–84
     [Google Scholar]
  37. 37.
    Cisek P, Kalaska JF. 2005. Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45:5801–14
     [Google Scholar]
  38. 38.
    Peixoto D, Verhein JR, Kiani R, Kao JC, Nuyujukian P et al. 2021. Decoding and perturbing decision states in real time. Nature 591:7851604–9
     [Google Scholar]
  39. 39.
    Chandrasekaran C, Peixoto D, Newsome WT, Shenoy KV. 2017. Laminar differences in decision-related neural activity in dorsal premotor cortex. Nat. Commun. 8:1614
     [Google Scholar]
  40. 40.
    Purcell BA, Heitz RP, Cohen JY, Schall JD, Logan GD, Palmeri TJ. 2010. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117:41113–43
     [Google Scholar]
  41. 41.
    Hanes DP, Schall JD. 1996. Neural control of voluntary movement initiation. Science 274:5286427–30
     [Google Scholar]
  42. 42.
    Wimmer K, Compte A, Roxin A, Peixoto D, Renart A, de la Rocha J. 2015. Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT. Nat. Commun. 6:6177
     [Google Scholar]
  43. 43.
    Deco G, Rolls ET, Albantakis L, Romo R. 2013. Brain mechanisms for perceptual and reward-related decision-making. Prog. Neurobiol. 103:194–213
     [Google Scholar]
  44. 44.
    Ditterich J. 2006. Stochastic models of decisions about motion direction: behavior and physiology. Neural. Netw. 19:8981–1012
     [Google Scholar]
  45. 45.
    Usher M, McClelland JL. 2001. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108:3550–92
     [Google Scholar]
  46. 46.
    Tajima S, Drugowitsch J, Pouget A. 2016. Optimal policy for value-based decision-making. Nat. Commun. 7:12400
     [Google Scholar]
  47. 47.
    Louie K, LoFaro T, Webb R, Glimcher PW. 2014. Dynamic divisive normalization predicts time-varying value coding in decision-related circuits. J. Neurosci. 34:4816046–57
     [Google Scholar]
  48. 48.
    Ratcliff R, Starns JJ. 2013. Modeling confidence judgments, response times, and multiple choices in decision making: recognition memory and motion discrimination. Psychol. Rev. 120:3697–719
     [Google Scholar]
  49. 49.
    Shadlen MN, Shohamy D. 2016. Decision making and sequential sampling from memory. Neuron 90:5927–39
     [Google Scholar]
  50. 50.
    Purcell BA, Kiani R. 2016. Neural mechanisms of post-error adjustments of decision policy in parietal cortex. Neuron 89:3658–71
     [Google Scholar]
  51. 51.
    Zylberberg A. 2021. Decision prioritization and causal reasoning in decision hierarchies. PLOS Comput. Biol. 17:12e1009688
     [Google Scholar]
  52. 52.
    Rao V, DeAngelis GC, Snyder LH. 2012. Neural correlates of prior expectations of motion in the lateral intraparietal and middle temporal areas. J. Neurosci. 32:2910063–74
     [Google Scholar]
  53. 53.
    Fan Y, Gold JI, Ding L. 2020. Frontal eye field and caudate neurons make different contributions to reward-biased perceptual decisions. eLife 9:e60535
     [Google Scholar]
  54. 54.
    Doi T, Fan Y, Gold JI, Ding L. 2020. The caudate nucleus contributes causally to decisions that balance reward and uncertain visual information. eLife 9:e56694
     [Google Scholar]
  55. 55.
    Hagura N, Haggard P, Diedrichsen J. 2017. Perceptual decisions are biased by the cost to act. eLife 6:e18422
     [Google Scholar]
  56. 56.
    Zylberberg A, Fetsch CR, Shadlen MN. 2016. The influence of evidence volatility on choice, reaction time and confidence in a perceptual decision. eLife 5:e17688
     [Google Scholar]
  57. 57.
    Levi AJ, Yates JL, Huk AC, Katz LN. 2018. Strategic and dynamic temporal weighting for perceptual decisions in humans and macaques. eNeuro 5:5ENEURO.0169–18.2018
     [Google Scholar]
  58. 58.
    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 105:4517538–42
     [Google Scholar]
  59. 59.
    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. 35:61865–97
     [Google Scholar]
  60. 60.
    Hanks T, Kiani R, Shadlen MN. 2014. A neural mechanism of speed-accuracy tradeoff in macaque area LIP. eLife 3:e02260
     [Google Scholar]
  61. 61.
    Akrami A, Kopec CD, Diamond ME, Brody CD. 2018. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554:7692368–72
     [Google Scholar]
  62. 62.
    Heitz RP, Schall JD. 2012. Neural mechanisms of speed-accuracy tradeoff. Neuron 76:3616–28
     [Google Scholar]
  63. 63.
    Okazawa G, Sha L, Purcell BA, Kiani R. 2018. Psychophysical reverse correlation reflects both sensory and decision-making processes. Nat. Commun. 9:13479
     [Google Scholar]
  64. 64.
    Cavanagh JF, Wiecki TV, Cohen MX, Figueroa CM, Samanta J et al. 2011. Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nat. Neurosci. 14:111462–67
     [Google Scholar]
  65. 65.
    Stine GM, Trautmann EM, Jeurissen D, Shadlen MN. 2022. A neural mechanism for terminating decisions. bioRxiv 490327. https://doi.org/10.1101/2022.05.02.490327
  66. 66.
    Lo CC, Wang XJ. 2006. Cortico-basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nat. Neurosci. 9:7956–63
     [Google Scholar]
  67. 67.
    Yeung N, Nystrom LE, Aronson JA, Cohen JD. 2006. Between-task competition and cognitive control in task switching. J. Neurosci. 26:51429–38
     [Google Scholar]
  68. 68.
    Wimmer RD, Schmitt LI, Davidson TJ, Nakajima M, Deisseroth K, Halassa MM. 2015. Thalamic control of sensory selection in divided attention. Nature 526:7575705–9
     [Google Scholar]
  69. 69.
    Okazawa G, Hatch CE, Mancoo A, Machens CK, Kiani R. 2021. Representational geometry of perceptual decisions in the monkey parietal cortex. Cell 184:143748–61.e18
     [Google Scholar]
  70. 70.
    Raposo D, Kaufman MT, Churchland AK. 2014. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17:121784–92
     [Google Scholar]
  71. 71.
    Kumano H, Suda Y, Uka T. 2016. Context-dependent accumulation of sensory evidence in the parietal cortex underlies flexible task switching. J. Neurosci. 36:4812192–202
     [Google Scholar]
  72. 72.
    Stoet G, Snyder LH. 2004. Single neurons in posterior parietal cortex of monkeys encode cognitive set. Neuron 42:61003–12
     [Google Scholar]
  73. 73.
    Roy JE, Riesenhuber M, Poggio T, Miller EK. 2010. Prefrontal cortex activity during flexible categorization. J. Neurosci. 30:258519–28
     [Google Scholar]
  74. 74.
    Siegel M, Buschman TJ, Miller EK. 2015. Cortical information flow during flexible sensorimotor decisions. Science 348:62411352–55
     [Google Scholar]
  75. 75.
    Rodgers CC, DeWeese MR. 2014. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron 82:51157–70
     [Google Scholar]
  76. 76.
    Flesch T, Juechems K, Dumbalska T, Saxe A, Summerfield C 2022. Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron 110:71258–70
     [Google Scholar]
  77. 77.
    Kelly SP, O'Connell RG 2013. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33:5019434–41
     [Google Scholar]
  78. 78.
    Tosoni A, Galati G, Romani GL, Corbetta M. 2008. Sensory-motor mechanisms in human parietal cortex underlie arbitrary visual decisions. Nat. Neurosci. 11:121446–53
     [Google Scholar]
  79. 79.
    Heekeren HR, Marrett S, Ruff DA, Bandettini PA, Ungerleider LG. 2006. Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality. PNAS 103:2610023–28
     [Google Scholar]
  80. 80.
    Mansouri FA, Freedman DJ, Buckley MJ. 2020. Emergence of abstract rules in the primate brain. Nat. Rev. Neurosci. 21:11595–610
     [Google Scholar]
  81. 81.
    Buckley MJ, Mansouri FA, Hoda H, Mahboubi M, Browning PG et al. 2009. Dissociable components of rule-guided behavior depend on distinct medial and prefrontal regions. Science 325:593652–58
     [Google Scholar]
  82. 82.
    Kamigaki T, Fukushima T, Miyashita Y. 2009. Cognitive set reconfiguration signaled by macaque posterior parietal neurons. Neuron 61:6941–51
     [Google Scholar]
  83. 83.
    Sasaki R, Uka T. 2009. Dynamic readout of behaviorally relevant signals from area MT during task switching. Neuron 62:1147–57
     [Google Scholar]
  84. 84.
    Katzner S, Busse L, Treue S. 2009. Attention to the color of a moving stimulus modulates motion-signal processing in macaque area MT: evidence for a unified attentional system. Front. Syst. Neurosci. 3:12
     [Google Scholar]
  85. 85.
    De Lafuente V, Jazayeri M, Shadlen MN. 2015. Representation of accumulating evidence for a decision in two parietal areas. J. Neurosci. 35:104306–18
     [Google Scholar]
  86. 86.
    Ho TC, Brown S, Serences JT. 2009. Domain general mechanisms of perceptual decision making in human cortex. J. Neurosci. 29:278675–87
     [Google Scholar]
  87. 87.
    Liu T, Pleskac TJ. 2011. Neural correlates of evidence accumulation in a perceptual decision task. J. Neurophysiol. 106:52383–98
     [Google Scholar]
  88. 88.
    Shadlen MN, Kiani R, Hanks TD, Churchland AK 2008. Neurobiology of decision making: an intentional framework. Better Than Conscious? Decision Making, the Human Mind, and Implications for Institutions C Engel, W Singer 71–101. Cambridge, MA: MIT Press
     [Google Scholar]
  89. 89.
    Snyder LH, Batista AP, Andersen RA. 2000. Intention-related activity in the posterior parietal cortex: a review. Vis. Res. 40:10–121433–41
     [Google Scholar]
  90. 90.
    Cisek P. 2012. Making decisions through a distributed consensus. Curr. Opin. Neurobiol. 22:6927–36
     [Google Scholar]
  91. 91.
    Salzman CD, Newsome WT. 1994. Neural mechanisms for forming a perceptual decision. Science 264:5156231–37
     [Google Scholar]
  92. 92.
    Bennur S, Gold JI. 2011. Distinct representations of a perceptual decision and the associated oculomotor plan in the monkey lateral intraparietal area. J. Neurosci. 31:3913–21
     [Google Scholar]
  93. 93.
    Wang M, Montanede C, Chandrasekaran C, Peixoto D, Shenoy KV, Kalaska JF. 2019. Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known. Nat. Commun. 10:1793
     [Google Scholar]
  94. 94.
    Twomey DM, Kelly SP, O'Connell RG. 2016. Abstract and effector-selective decision signals exhibit qualitatively distinct dynamics before delayed perceptual reports. J. Neurosci. 36:287346–52
     [Google Scholar]
  95. 95.
    Shushruth S, Zylberberg A, Shadlen MN. 2022. Sequential sampling from memory underlies action selection during abstract decision-making. Curr. Biol. 32:91949–60
     [Google Scholar]
  96. 96.
    Gold JI, Shadlen MN. 2003. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23:2632–51
     [Google Scholar]
  97. 97.
    Duan CA, Pagan M, Piet AT, Kopec CD, Akrami A et al. 2021. Collicular circuits for flexible sensorimotor routing. Nat. Neurosci. 24:81110–20
     [Google Scholar]
  98. 98.
    Asaad WF, Rainer G, Miller EK 1998. Neural activity in the primate prefrontal cortex during associative learning. Neuron 21:61399–407
     [Google Scholar]
  99. 99.
    Johnston K, Levin HM, Koval MJ, Everling S. 2007. Top-down control-signal dynamics in anterior cingulate and prefrontal cortex neurons following task switching. Neuron 53:3453–62
     [Google Scholar]
  100. 100.
    Stokes MG, Kusunoki M, Sigala N, Nili H, Gaffan D, Duncan J. 2013. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78:2364–75
     [Google Scholar]
  101. 101.
    van den Brink RL, Hagena K, Wilming N, Murphy PR, Buechel C, Donner TH. 2022. Large-scale circuit configuration for flexible sensory-motor decisions. bioRxiv 483758, version 1. https://doi.org/10.1101/2022.03.10.483758
  102. 102.
    Wu Z, Litwin-Kumar A, Shamash P, Taylor A, Axel R, Shadlen MN 2020. Context-dependent decision making in a premotor circuit. Neuron 106:2316–28.e6
     [Google Scholar]
  103. 103.
    Izquierdo A, Brigman JL, Radke AK, Rudebeck PH, Holmes A. 2017. The neural basis of reversal learning: an updated perspective. Neuroscience 345:12–26
     [Google Scholar]
  104. 104.
    Rudebeck PH, Murray EA. 2014. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84:61143–56
     [Google Scholar]
  105. 105.
    Muhammad R, Wallis JD, Miller EK. 2006. A comparison of abstract rules in the prefrontal cortex, premotor cortex, inferior temporal cortex, and striatum. J. Cogn. Neurosci. 18:6974–89
     [Google Scholar]
  106. 106.
    Bondy AG, Haefner RM, Cumming BG. 2018. Feedback determines the structure of correlated variability in primary visual cortex. Nat. Neurosci. 21:4598–606
     [Google Scholar]
  107. 107.
    Liu Y, Xin Y, Xu NL. 2021. A cortical circuit mechanism for structural knowledge-based flexible sensorimotor decision-making. Neuron 109:122009–24.e6
     [Google Scholar]
  108. 108.
    Wang TY, Liu J, Yao H. 2020. Control of adaptive action selection by secondary motor cortex during flexible visual categorization. eLife 9:e54474
     [Google Scholar]
  109. 109.
    Crapse TB, Lau H, Basso MA. 2018. A role for the superior colliculus in decision criteria. Neuron 97:1181–94.e6
     [Google Scholar]
  110. 110.
    Ferrera VP, Yanike M, Cassanello C. 2009. Frontal eye field neurons signal changes in decision criteria. Nat. Neurosci. 12:111458–62
     [Google Scholar]
  111. 111.
    Jaramillo S, Borges K, Zador AM. 2014. Auditory thalamus and auditory cortex are equally modulated by context during flexible categorization of sounds. J. Neurosci. 34:155291–301
     [Google Scholar]
  112. 112.
    Xin Y, Zhong L, Zhang Y, Zhou T, Pan J, Xu NL. 2019. Sensory-to-category transformation via dynamic reorganization of ensemble structures in mouse auditory cortex. Neuron 103:5909–21.e6
     [Google Scholar]
  113. 113.
    Quinn KR, Seillier L, Butts DA, Nienborg H. 2021. Decision-related feedback in visual cortex lacks spatial selectivity. Nat. Commun. 12:4473
     [Google Scholar]
  114. 114.
    Zhao Y, Yates JL, Levi AJ, Huk AC, Park IM. 2020. Stimulus-choice (mis)alignment in primate area MT. PLOS Comput. Biol. 16:5e1007614
     [Google Scholar]
  115. 115.
    Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM. 2021. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLOS Comput. Biol. 17:11e1009517
     [Google Scholar]
  116. 116.
    Waskom ML, Kiani R. 2018. Decision making through integration of sensory evidence at prolonged timescales. Curr. Biol. 28:233850–56.e9
     [Google Scholar]
  117. 117.
    Stine GM, Zylberberg A, Ditterich J, Shadlen MN. 2020. Differentiating between integration and non-integration strategies in perceptual decision making. eLife 9:e55365
     [Google Scholar]
  118. 118.
    Romo R, de Lafuente V. 2013. Conversion of sensory signals into perceptual decisions. Prog. Neurobiol. 103:41–75
     [Google Scholar]
  119. 119.
    Machens CK, Romo R, Brody CD. 2005. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307:57121121–24
     [Google Scholar]
  120. 120.
    Bernardi S, Benna MK, Rigotti M, Munuera J, Fusi S, Salzman CD. 2020. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183:4954–67.e21
     [Google Scholar]
  121. 121.
    Yang GR, Joglekar MR, Song HF, Newsome WT, Wang XJ. 2019. Task representations in neural networks trained to perform many cognitive tasks. Nat. Neurosci. 22:2297–306
     [Google Scholar]
  122. 122.
    Mohan K, Zhu O, Freedman DJ. 2021. Interaction between neuronal encoding and population dynamics during categorization task switching in parietal cortex. Neuron 109:4700–12.e4
     [Google Scholar]
  123. 123.
    Cole MW, Etzel JA, Zacks JM, Schneider W, Braver TS. 2011. Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex. Front. Hum. Neurosci. 5:142
     [Google Scholar]
  124. 124.
    Koida K, Komatsu H. 2007. Effects of task demands on the responses of color-selective neurons in the inferior temporal cortex. Nat. Neurosci. 10:1108–16
     [Google Scholar]
  125. 125.
    Chowdhury SA, DeAngelis GC. 2008. Fine discrimination training alters the causal contribution of macaque area MT to depth perception. Neuron 60:2367–77
     [Google Scholar]
  126. 126.
    Koldaeva A, Schaefer AT, Fukunaga I. 2019. Rapid task-dependent tuning of the mouse olfactory bulb. eLife 8:e43558
     [Google Scholar]
  127. 127.
    Solway A, Botvinick MM. 2015. Evidence integration in model-based tree search. PNAS 112:3711708–13
     [Google Scholar]
  128. 128.
    Huys QJ, Lally N, Faulkner P, Eshel N, Seifritz E et al. 2015. Interplay of approximate planning strategies. PNAS 112:103098–103
     [Google Scholar]
  129. 129.
    Van Opheusden B, Ma WJ. 2019. Tasks for aligning human and machine planning. Curr. Opin. Behav. Sci. 29:127–33
     [Google Scholar]
  130. 130.
    Badre D, Nee DE. 2018. Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22:2170–88
     [Google Scholar]
  131. 131.
    Riley MR, Qi XL, Zhou X, Constantinidis C. 2018. Anterior-posterior gradient of plasticity in primate prefrontal cortex. Nat. Commun. 9:3790
     [Google Scholar]
  132. 132.
    Yang Q, Lin Z, Zhang W, Li J, Chen X et al. 2022. Monkey plays Pac-Man with compositional strategies and hierarchical decision-making. eLife 11:e74500
     [Google Scholar]
  133. 133.
    Ashwood ZC, Roy NA, Stone IR, Int. Brain Lab., Urai AE et al. 2022. Mice alternate between discrete strategies during perceptual decision-making. Nat. Neurosci. 25:2201–12
     [Google Scholar]
  134. 134.
    Churchland AK, Kiani R. 2016. Three challenges for connecting model to mechanism in decision-making. Curr. Opin. Behav. Sci. 11:74–80
     [Google Scholar]
  135. 135.
    Bartolo R, Averbeck BB. 2020. Prefrontal cortex predicts state switches during reversal learning. Neuron 106:61044–54.e4
     [Google Scholar]
  136. 136.
    Murray EA, Rudebeck PH. 2018. Specializations for reward-guided decision-making in the primate ventral prefrontal cortex. Nat. Rev. Neurosci. 19:7404–17
     [Google Scholar]
  137. 137.
    Purcell BA, Kiani R. 2016. Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy. PNAS 113:31E4531–40
     [Google Scholar]
  138. 138.
    Sarafyazd M, Jazayeri M. 2019. Hierarchical reasoning by neural circuits in the frontal cortex. Science 364:6441eaav8911
     [Google Scholar]
  139. 139.
    Chiu YC, Yantis S. 2009. A domain-independent source of cognitive control for task sets: shifting spatial attention and switching categorization rules. J. Neurosci. 29:123930–38
     [Google Scholar]
  140. 140.
    Xu Y. 2018. The posterior parietal cortex in adaptive visual processing. Trends Neurosci. 41:11806–22
     [Google Scholar]
  141. 141.
    Tajima S, Koida K, Tajima CI, Suzuki H, Aihara K, Komatsu H. 2017. Task-dependent recurrent dynamics in visual cortex. eLife 6:e26868
     [Google Scholar]
  142. 142.
    Akrami A, Liu Y, Treves A, Jagadeesh B. 2009. Converging neuronal activity in inferior temporal cortex during the classification of morphed stimuli. Cereb. Cortex 19:4760–76
     [Google Scholar]
  143. 143.
    Pinto L, Rajan K, DePasquale B, Thiberge SY, Tank DW, Brody CD. 2019. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104:4810–24.e9
     [Google Scholar]
  144. 144.
    Heekeren HR, Marrett S, Bandettini PA, Ungerleider LG. 2004. A general mechanism for perceptual decision-making in the human brain. Nature 431:7010859–62
     [Google Scholar]
  145. 145.
    Cisek P. 2022. Evolution of behavioural control from chordates to primates. Philos. Trans. R. Soc. B 377:184420200522
     [Google Scholar]
  146. 146.
    Hunt LT, Hayden BY. 2017. A distributed, hierarchical and recurrent framework for reward-based choice. Nat. Rev. Neurosci. 18:3172–82
     [Google Scholar]
  147. 147.
    Fine JM, Hayden BY. 2022. The whole prefrontal cortex is premotor cortex. Philos. Trans. R. Soc. B 377:184420200524
     [Google Scholar]
  148. 148.
    Lee TS, Mumford D. 2003. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20:71434–48
     [Google Scholar]
  149. 149.
    Preuss TM, Wise SP. 2022. Evolution of prefrontal cortex. Neuropsychopharmacology 47:13–19
     [Google Scholar]
  150. 150.
    Calhoun AJ, Pillow JW, Murthy M. 2019. Unsupervised identification of the internal states that shape natural behavior. Nat. Neurosci. 22:122040–49
     [Google Scholar]
  151. 151.
    Wolff SB, Olveczky BP. 2018. The promise and perils of causal circuit manipulations. Curr. Opin. Neurobiol. 49:84–94
     [Google Scholar]
  152. 152.
    Bredenberg C, Savin C, Kiani R. 2021. Recurrent neural circuits overcome partial inactivation by compensation and re-learning. bioRxiv 468273. https://doi.org/10.1101/2021.11.12.468273
  153. 153.
    Li N, Daie K, Svoboda K, Druckmann S. 2016. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532:7600459–64
     [Google Scholar]
  154. 154.
    Katz LN, Yates JL, Pillow JW, Huk AC. 2016. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535:7611285–88
     [Google Scholar]
  155. 155.
    Zhou Y, Freedman DJ. 2019. Posterior parietal cortex plays a causal role in perceptual and categorical decisions. Science 365:6449180–85
     [Google Scholar]
  156. 156.
    Jeurissen D, Shushruth S, El-Shamayleh Y, Horwitz GD, Shadlen MN. 2022. Deficits in decision-making induced by parietal cortex inactivation are compensated at two timescales. Neuron 110:121924–31
     [Google Scholar]
  157. 157.
    Yao JD, Gimoto J, Constantinople CM, Sanes DH. 2020. Parietal cortex is required for the integration of acoustic evidence. Curr. Biol. 30:173293–3303.e4
     [Google Scholar]
  158. 158.
    Zhong L, Zhang Y, Duan CA, Deng J, Pan J, Xu NL. 2019. Causal contributions of parietal cortex to perceptual decision-making during stimulus categorization. Nat. Neurosci. 22:6963–73
     [Google Scholar]
  159. 159.
    Erlich JC, Brunton BW, Duan CA, Hanks TD, Brody CD. 2015. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife 4:e05457
     [Google Scholar]
  160. 160.
    Jun EJ, Bautista AR, Nunez MD, Allen DC, Tak JH et al. 2021. Causal role for the primate superior colliculus in the computation of evidence for perceptual decisions. Nat. Neurosci. 24:81121–31
     [Google Scholar]
  161. 161.
    Koay SA, Thiberge SY, Brody CD, Tank DW. 2019. Neural correlates of cognition in primary visual versus neighboring posterior cortices during visual evidence-accumulation-based navigation. bioRxiv 568766. https://doi.org/10.1101/568766
  162. 162.
    Musall S, Kaufman MT, Juavinett AL, Gluf S, Churchland AK. 2019. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22:101677–86
     [Google Scholar]
  163. 163.
    Stringer C, Pachitariu M, Steinmetz N, Reddy CB, Carandini M, Harris KD. 2019. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364:6437255
     [Google Scholar]
  164. 164.
    Kobak D, Brendel W, Constantinidis C, Feierstein CE, Kepecs A et al. 2016. Demixed principal component analysis of neural population data. eLife 5:e10989
     [Google Scholar]
  165. 165.
    Williams AH, Kim TH, Wang F, Vyas S, Ryu SI et al. 2018. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98:61099–1115.e8
     [Google Scholar]
  166. 166.
    Kriegeskorte N, Wei XX. 2021. Neural tuning and representational geometry. Nat. Rev. Neurosci. 22:11703–18
     [Google Scholar]
  167. 167.
    Ebitz RB, Hayden BY. 2021. The population doctrine in cognitive neuroscience. Neuron 109:193055–68
     [Google Scholar]
  168. 168.
    Kohn A, Jasper AI, Semedo JD, Gokcen E, Machens CK, Yu BM. 2020. Principles of corticocortical communication: proposed schemes and design considerations. Trends Neurosci. 43:9725–37
     [Google Scholar]
  169. 169.
    Fetsch CR. 2016. The importance of task design and behavioral control for understanding the neural basis of cognitive functions. Curr. Opin. Neurobiol. 37:16–22
     [Google Scholar]
  170. 170.
    Roxin A, Ledberg A. 2008. Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation. PLOS Comput. Biol. 4:3e1000046
     [Google Scholar]
  171. 171.
    Cohen JD, Dunbar K, McClelland JL. 1990. On the control of automatic processes: a parallel distributed processing account of the Stroop effect. Psychol. Rev. 97:3332–61
     [Google Scholar]
  172. 172.
    Salinas E. 2004. Context-dependent selection of visuomotor maps. BMC Neurosci. 5:47
     [Google Scholar]
  173. 173.
    Loh M, Deco G. 2005. Cognitive flexibility and decision-making in a model of conditional visuomotor associations. Eur. J. Neurosci. 22:112927–36
     [Google Scholar]
  174. 174.
    Fusi S, Asaad WF, Miller EK, Wang XJ. 2007. A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. Neuron 54:2319–33
     [Google Scholar]
  175. 175.
    Zylberberg A, Fernandez Slezak D, Roelfsema PR, Dehaene S, Sigman M. 2010. The brain's router: a cortical network model of serial processing in the primate brain. PLOS Comput. Biol. 6:4e1000765
     [Google Scholar]
  176. 176.
    Yang GR, Murray JD, Wang XJ. 2016. A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7:12815
     [Google Scholar]
  177. 177.
    Akam T, Kullmann DM. 2010. Oscillations and filtering networks support flexible routing of information. Neuron 67:2308–20
     [Google Scholar]
  178. 178.
    Kremkow J, Aertsen A, Kumar A. 2010. Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition. J. Neurosci. 30:4715760–68
     [Google Scholar]
  179. 179.
    Song HF, Yang GR, Wang XJ. 2017. Reward-based training of recurrent neural networks for cognitive and value-based tasks. eLife 6:e21492
     [Google Scholar]
  180. 180.
    Chaisangmongkon W, Swaminathan SK, Freedman DJ, Wang XJ. 2017. Computing by robust transience: how the fronto-parietal network performs sequential, category-based decisions. Neuron 93:61504–17.e4
     [Google Scholar]
  181. 181.
    Wang J, Narain D, Hosseini EA, Jazayeri M. 2018. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 21:1102–10
     [Google Scholar]
  182. 182.
    Sussillo D, Barak O. 2013. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25:3626–49
     [Google Scholar]
  183. 183.
    Zhang X, Liu S, Chen ZS. 2021. A geometric framework for understanding dynamic information integration in context-dependent computation. iScience 24:8102919
     [Google Scholar]
  184. 184.
    Maheswaranathan N, Williams AH, Golub MD, Ganguli S, Sussillo D. 2019. Universality and individuality in neural dynamics across large populations of recurrent networks. Adv. Neural Inf. Process Syst. 2019:15629–41
     [Google Scholar]
  185. 185.
    Kohn A, Coen-Cagli R, Kanitscheider I, Pouget A. 2016. Correlations and neuronal population information. Annu. Rev. Neurosci. 39:237–56
     [Google Scholar]
  186. 186.
    Kaufman MT, Churchland MM, Ryu SI, Shenoy KV. 2015. Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex. eLife 4:e04677
     [Google Scholar]
  187. 187.
    Keemink SW, Machens CK. 2019. Decoding and encoding (de)mixed population responses. Curr. Opin. Neurobiol. 58:112–21
     [Google Scholar]
  188. 188.
    Perich MG, Rajan K. 2020. Rethinking brain-wide interactions through multi-region ‘network of networks’ models. Curr. Opin. Neurobiol. 65:146–51
     [Google Scholar]
  189. 189.
    Kleinman M, Chandrasekaran C, Kao JC. 2020. Recurrent neural network models of multi-area computation underlying decision-making. bioRxiv 798553. https://doi.org//10.1101/798553
  190. 190.
    Chaudhuri R, Knoblauch K, Gariel MA, Kennedy H, Wang XJ. 2015. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88:2419–31
     [Google Scholar]
  191. 191.
    Xue C, Kramer LE, Cohen MR 2022. Dynamic task-belief is an integral part of decision-making. Neuron 110:152503–11
     [Google Scholar]
/content/journals/10.1146/annurev-physiol-031722-024731
Loading
Neural Mechanisms That Make Perceptual Decisions Flexible
Annual Review of Physiology 85, 191 (2023); https://doi.org/10.1146/annurev-physiol-031722-024731
/content/journals/10.1146/annurev-physiol-031722-024731
/content/journals/10.1146/annurev-physiol-031722-024731
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error