Abstract
Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Glimcher, P. & Fehr, E. Neuroeconomics: Decision Making and the Brain (Academic Press, 2014).
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).
Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80, 312–325 (2013).
Rangel, A., Camerer, C. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).
Kable, J. W. & Glimcher, P. W. The neurobiology of decision: consensus and controversy. Neuron 63, 733–745 (2009).
Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010).
Padoa-Schioppa, C. Neurobiology of economic choice: a good-based model. Annu. Rev. Neurosci. 34, 333–359 (2011).
Frank, M. J. in An Introduction to Model-Based Cognitive Neuroscience (eds Forstmann, B. & Wagenmakers, E.-J.) 159–177 (Springer, 2015).
Wang, X. J. Neural dynamics and circuit mechanisms of decision-making. Curr. Opin. Neurobiol. 22, 1039–1046 (2012).
Pezzulo, G. & Cisek, P. Navigating the affordance landscape: feedback control as a process model of behavior and cognition. Trends Cogn. Sci. 20, 414–424 (2016).
Wang, X. J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).
Rolls, E. T. Emotion and Decision Making Explained (Oxford Univ. Press, 2013).
Machens, C. K., Romo, R. & Brody, C. D. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307, 1121–1124 (2005).
Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).
Medler, D. A. A brief history of connectionism. Neural Comput. Surveys 1, 18–72 (1998).
Gardner, H. The Mind's New Science: A History of the Cognitive Revolution (Basic Books, 1985).
McClelland, J. L. & Rumelhart, D. E. Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises (MIT Press, 1989).
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Carpenter, G. A. & Grossberg, S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vision Graph. 37, 54–115 (1987).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C. & Behrens, T. E. Measuring macroscopic brain connections in vivo. Nat. Neurosci. 18, 1546–1555 (2015).
Fenno, L., Yizhar, O. & Deisseroth, K. The development and application of optogenetics. Annu. Rev. Neurosci. 34, 389–412 (2011).
Churchland, P. M. Eliminative materialism and the propositional attitudes. J. Philos. 78, 67 (1981).
Seeley, T. D. Honeybee Democracy (Princeton Univ. Press, 2010).
London, M. & Häusser, M. Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005).
Couzin, I. D. Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009).
O'Doherty, J. P. The problem with value. Neurosci. Biobehav. Rev. 43, 259–268 (2014).
Grillner, S. Neurobiological bases of rhythmic motor acts in vertebrates. Science 228, 143–149 (1985).
Kovac, M. & Davis, W. Behavioral choice: neural mechanisms in Pleurobranchaea. Science 198, 632–634 (1977).
Hunt, L. T. et al. Mechanisms underlying cortical activity during value-guided choice. Nat. Neurosci. 15, 470–476 (2012).
Hunt, L. T., Behrens, T. E., Hosokawa, T., Wallis, J. D. & Kennerley, S. W. Capturing the temporal evolution of choice across prefrontal cortex. eLife 4, e11945 (2015).
Jocham, G., Hunt, L. T., Near, J. & Behrens, T. E. A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex. Nat. Neurosci. 15, 960–961 (2012).
Strait, C. E., Blanchard, T. C. & Hayden, B. Y. Reward value comparison via mutual inhibition in ventromedial prefrontal cortex. Neuron 82, 1357–1366 (2014).
Strait, C. E., Sleezer, B. J. & Hayden, B. Y. Signatures of value comparison in ventral striatum neurons. PLoS Biol. 13, e1002173 (2015).
Rustichini, A. & Padoa-Schioppa, C. A neuro-computational model of economic decisions. J. Neurophysiol. 114, 1382–1398 (2015).
Hunt, L. T., Dolan, R. J. & Behrens, T. E. Hierarchical competitions subserving multi-attribute choice. Nat. Neurosci. 17, 1613–1622 (2014).
Chau, B. K., Kolling, N., Hunt, L. T., Walton, M. E. & Rushworth, M. F. A neural mechanism underlying failure of optimal choice with multiple alternatives. Nat. Neurosci. 17, 463–470 (2014).
Louie, K. & Glimcher, P. W. Separating value from choice: delay discounting activity in the lateral intraparietal area. J. Neurosci. 30, 5498–5507 (2010).
Pastor-Bernier, A. & Cisek, P. Neural correlates of biased competition in premotor cortex. J. Neurosci. 31, 7083–7088 (2011).
Cisek, P. Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26, 9761–9770 (2006).
Soltani, A. A biophysically based neural model of matching law behavior: melioration by stochastic synapses. J. Neurosci. 26, 3731–3744 (2006).
Rushworth, M. F., Kolling, N., Sallet, J. & Mars, R. B. Valuation and decision-making in frontal cortex: one or many serial or parallel systems? Curr. Opin. Neurobiol. 22, 946–955 (2012).
Cisek, P. Making decisions through a distributed consensus. Curr. Opin. Neurobiol. 22, 927–936 (2012).
Strait, C. E. et al. Neuronal selectivity for spatial positions of offers and choices in five reward regions. J. Neurophysiol. 115, 1098–1111 (2016).
Hunt, L. T., Woolrich, M. W., Rushworth, M. F. & Behrens, T. E. Trial-type dependent frames of reference for value comparison. PLoS Comput. Biol. 9, e1003225 (2013).
Passingham, R. E., Stephan, K. E. & Kötter, R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616 (2002).
Neubert, F. X., Mars, R. B., Sallet, J. & Rushworth, M. F. Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex. Proc. Natl Acad. Sci. USA 112, E2695–E2704 (2015).
Padoa-Schioppa, C. & Assad, J. A. Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).
Hosokawa, T., Kennerley, S. W., Sloan, J. & Wallis, J. D. Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex. J. Neurosci. 33, 17385–17397 (2013).
Rolls, E. T., Grabenhorst, F. & Deco, G. Choice, difficulty, and confidence in the brain. Neuroimage 53, 694–706 (2010).
Hare, T. A., Schultz, W., Camerer, C. F., O'Doherty, J. P. & Rangel, A. Transformation of stimulus value signals into motor commands during simple choice. Proc. Natl Acad. Sci. USA 108, 18120–18125 (2011).
Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015).
Klein-Flugge, M. C. & Bestmann, S. Time-dependent changes in human corticospinal excitability reveal value-based competition for action during decision processing. J. Neurosci. 32, 8373–8382 (2012).
O'Reilly, J. X., Woolrich, M. W., Behrens, T. E. J., Smith, S. M. & Johansen-Berg, H. Tools of the trade: psychophysiological interactions and functional connectivity. Soc. Cogn. Affect. Neurosci. 7, 604–609 (2012).
Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015).
Freidin, E. & Kacelnik, A. Rational choice, context dependence, and the value of information in European starlings (Sturnus vulgaris). Science 334, 1000–1002 (2011).
Bettman, J. R., Luce, M. F. & Payne, J. W. Constructive consumer choice processes. J. Consum. Res. 25, 187–217 (1998).
Rich, E. L. & Wallis, J. D. Decoding subjective decisions from orbitofrontal cortex. Nat. Neurosci. 19, 973–980 (2016).
LeCun, Y. et al. Backpropagation applied to handwritten ZIP code recognition. Neural Comput. 1, 541–551 (1989).
Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Systems 25, 1090–1098 (2012).
Yamins, D. L. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).
Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vision Sci. 1, 417–446 (2015).
Graves, A. Supervised Sequence Labelling with Recurrent Neural Networks (Springer, 2012).
Graves, A., Mohamed, A.-r. & Hinton, G. Speech recognition with deep recurrent neural networks. eprint at arXiv http://adsabs.harvard.edu/abs/2013arXiv1303.5778G (2013).
Weston, J., Chopra, S. & Bordes, A. Memory networks. eprint at arXiv http://adsabs.harvard.edu/abs/2014arXiv1410.3916W (2014).
Amari, S.-i. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27, 77–87 (1977).
Compte, A. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).
Wang, X.-J. in Principles of Frontal Lobe Function (eds Stuss, D. T. & Knight, R. T.) 226–248 (Oxford Univ. Press, 2013).
Elston, G. N. Pyramidal cells of the frontal lobe: all the more spinous to think with. J. Neurosci. 20, RC95 (2000).
Busemeyer, J. R. & Townsend, J. T. Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol. Rev. 100, 432–459 (1993).
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006).
Passingham, R. E. & Wise, S. P. The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight 1st edn (Oxford Univ. Press, 2012).
Stephens, D. W. & Krebs, J. R. Foraging Theory (Princeton Univ. Press, 1986).
Kolling, N., Behrens, T. E., Mars, R. B. & Rushworth, M. F. Neural mechanisms of foraging. Science 336, 95–98 (2012).
Hayden, B. Y., Pearson, J. M. & Platt, M. L. Neuronal basis of sequential foraging decisions in a patchy environment. Nat. Neurosci. 14, 933–939 (2011).
Lim, S. L., O'Doherty, J. P. & Rangel, A. The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. J. Neurosci. 31, 13214–13223 (2011).
Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
Stokes, M. G. 'Activity-silent' working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).
Cavanagh, S. E., Wallis, J. D., Kennerley, S. W. & Hunt, L. T. Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice. eLife 5, e18937 (2016).
Sussillo, D. & Abbott, L. F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009).
Hennequin, G., Vogels, T. P. & Gerstner, W. Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron 82, 1394–1406 (2014).
Sussillo, D. & Barak, O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 25, 626–649 (2013).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Song, H. F., Yang, G. R. & Wang, X. Reward-based training of recurrent neural networks for cognitive and value-based tasks. eLife 6, e21492 (2017).
Haber, S. N. & Behrens, T. E. The neural network underlying incentive-based learning: implications for interpreting circuit disruptions in psychiatric disorders. Neuron 83, 1019–1039 (2014).
Shallice, T. & Burgess, P. W. Deficits in strategy application following frontal lobe damage in man. Brain 114, 727–741 (1991).
Fuster, J. M. The prefrontal cortex — an update: time is of the essence. Neuron 30, 319–333 (2001).
Holroyd, C. B. & Yeung, N. Motivation of extended behaviors by anterior cingulate cortex. Trends Cogn. Sci. 16, 122–128 (2012).
Pascanu, R., Gulcehre, C., Cho, K. & Bengio, Y. How to construct deep recurrent neural networks. eprint at arXiv http://adsabs.harvard.edu/abs/2013arXiv1312.6026P (2013).
Karpathy, A. & Fei-Fei, L. Deep visual-semantic alignments for generating image descriptions. IEEE Trans. Pattern Anal. Mach. Intell. http://dx.doi.org/10.1109/TPAMI.2016.2598339 (2016).
Bernacchia, A., Seo, H., Lee, D. & Wang, X.-J. A reservoir of time constants for memory traces in cortical neurons. Nat. Neurosci. 14, 366–372 (2011).
Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).
Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H. & Wang, X.-J. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88, 419–431 (2015).
Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661–1663 (2014).
Purcell, B. A. & Kiani, R. Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy. Proc. Natl Acad. Sci. USA 113, E4531–E4540 (2016).
Boorman, E. D., Rushworth, M. F. & Behrens, T. E. Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multi-alternative choice. J. Neurosci. 33, 2242–2253 (2013).
Wittmann, M. K. et al. Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex. Nat. Commun. 7, 12327 (2016).
Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B. & Dolan, R. J. Cortical substrates for exploratory decisions in humans. Nature 441, 876–879 (2006).
Karlsson, M. P., Tervo, D. G. R. & Karpova, A. Y. Network resets in medial prefrontal cortex mark the onset of behavioral uncertainty. Science 338, 135–139 (2012).
Blanchard, T. C., Strait, C. E. & Hayden, B. Y. Ramping ensemble activity in dorsal anterior cingulate neurons during persistent commitment to a decision. J. Neurophysiol. 114, 2439–2449 (2015).
Desrochers, T. M., Chatham, C. H. & Badre, D. The necessity of rostrolateral prefrontal cortex for higher-level sequential behavior. Neuron 87, 1357–1368 (2015).
Badre, D. & D'Esposito, M. Is the rostro-caudal axis of the frontal lobe hierarchical? Nat. Rev. Neurosci. 10, 659–669 (2009).
Kouneiher, F., Charron, S. & Koechlin, E. Motivation and cognitive control in the human prefrontal cortex. Nat. Neurosci. 12, 939–945 (2009).
O'Reilly, R. C. The what and how of prefrontal cortical organization. Trends Neurosci. 33, 355–361 (2010).
Zarr, N. & Brown, J. W. Hierarchical error representation in medial prefrontal cortex. Neuroimage 124, 238–247 (2016).
Nee, D. E. & Brown, J. W. Rostral–caudal gradients of abstraction revealed by multi-variate pattern analysis of working memory. Neuroimage 63, 1285–1294 (2012).
Nee, D. E. & D'Esposito, M. The hierarchical organization of the lateral prefrontal cortex. eLife 5, e12112 (2016).
Badre, D., Kayser, A. S. & D'Esposito, M. Frontal cortex and the discovery of abstract action rules. Neuron 66, 315–326 (2010).
Frank, M. J. & Badre, D. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cereb. Cortex 22, 509–526 (2012).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
O'Reilly, R. C. & Frank, M. J. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18, 283–328 (2006).
Dursteler, M. R., Wurtz, R. H. & Newsome, W. T. Directional pursuit deficits following lesions of the foveal representation within the superior temporal sulcus of the macaque monkey. J. Neurophysiol. 57, 1262–1287 (1987).
Barton, J. J., Press, D. Z., Keenan, J. P. & O'Connor, M. Lesions of the fusiform face area impair perception of facial configuration in prosopagnosia. Neurology 58, 71–78 (2002).
Noonan, M. P. et al. Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex. Proc. Natl Acad. Sci. USA 107, 20547–20552 (2010).
Walton, M. E., Behrens, T. E., Buckley, M. J., Rudebeck, P. H. & Rushworth, M. F. Separable learning systems in the macaque brain and the role of orbitofrontal cortex in contingent learning. Neuron 65, 927–939 (2010).
Vaidya, A. R. & Fellows, L. K. Testing necessary regional frontal contributions to value assessment and fixation-based updating. Nat. Commun. 6, 10120 (2015).
Eslinger, P. J. & Damasio, A. R. Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR. Neurology 35, 1731–1741 (1985).
Szczepanski, S. M. & Knight, R. T. Insights into human behavior from lesions to the prefrontal cortex. Neuron 83, 1002–1018 (2014).
Shadlen, M. N. & Shohamy, D. Decision making and sequential sampling from memory. Neuron 90, 927–939 (2016).
Plassmann, H., O'Doherty, J. & Rangel, A. Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J. Neurosci. 27, 9984–9988 (2007).
Knutson, B., Taylor, J., Kaufman, M., Peterson, R. & Glover, G. Distributed neural representation of expected value. J. Neurosci. 25, 4806–4812 (2005).
McCoy, A. N. & Platt, M. L. Risk-sensitive neurons in macaque posterior cingulate cortex. Nat. Neurosci. 8, 1220–1227 (2005).
Kim, S., Hwang, J. & Lee, D. Prefrontal coding of temporally discounted values during intertemporal choice. Neuron 59, 161–172 (2008).
Louie, K., Grattan, L. E. & Glimcher, P. W. Reward value-based gain control: divisive normalization in parietal cortex. J. Neurosci. 31, 10627–10639 (2011).
Shuler, M. G. Reward timing in the primary visual cortex. Science 311, 1606–1609 (2006).
Philiastides, M. G., Biele, G. & Heekeren, H. R. A mechanistic account of value computation in the human brain. Proc. Natl Acad. Sci. USA 107, 9430–9435 (2010).
Peck, C. J., Lau, B. & Salzman, C. D. The primate amygdala combines information about space and value. Nat. Neurosci. 16, 340–348 (2013).
Cai, X., Kim, S. & Lee, D. Heterogeneous coding of temporally discounted values in the dorsal and ventral striatum during intertemporal choice. Neuron 69, 170–182 (2011).
Heilbronner, S. R. & Hayden, B. Y. Dorsal anterior cingulate cortex: a bottom-up view. Annu. Rev. Neurosci. 39, 149–170 (2016).
Vickery, T. J., Chun, M. M. & Lee, D. Ubiquity and specificity of reinforcement signals throughout the human brain. Neuron 72, 166–177 (2011).
Maunsell, J. H. R. Neuronal representations of cognitive state: reward or attention? Trends Cogn. Sci. 8, 261–265 (2004).
Leathers, M. L. & Olson, C. R. In monkeys making value-based decisions, LIP neurons encode cue salience and not action value. Science 338, 132–135 (2012).
Litt, A., Plassmann, H., Shiv, B. & Rangel, A. Dissociating valuation and saliency signals during decision-making. Cereb. Cortex 21, 95–102 (2010).
Vlaev, I., Chater, N., Stewart, N. & Brown, G. D. Does the brain calculate value? Trends Cogn. Sci. 15, 546–554 (2011).
Lichtenstein, S. & Slovic, P. Reversals of preference between bids and choices in gambling decisions. J. Exp. Psychol. 89, 46–55 (1971).
Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
Shenoy, K. V., Sahani, M. & Churchland, M. M. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337–359 (2013).
Hunt, L. T. What are the neural origins of choice variability? Trends Cogn. Sci. 18, 222–224 (2014).
Levy, D. J. & Glimcher, P. W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).
Svoboda, E., McKinnon, M. C. & Levine, B. The functional neuroanatomy of autobiographical memory: a meta-analysis. Neuropsychologia 44, 2189–2208 (2006).
Doeller, C. F., Barry, C. & Burgess, N. Evidence for grid cells in a human memory network. Nature 463, 657–661 (2010).
Schacter, D. L. et al. The future of memory: remembering, imagining, and the brain. Neuron 76, 677–694 (2012).
Van Overwalle, F. Social cognition and the brain: a meta-analysis. Hum. Brain Mapp. 30, 829–858 (2009).
Mansouri, F. A., Buckley, M. J. & Tanaka, K. The essential role of primate orbitofrontal cortex in conflict-induced executive control adjustment. J. Neurosci. 34, 11016–11031 (2014).
Sleezer, B. J., Castagno, M. D. & Hayden, B. Y. Rule encoding in orbitofrontal cortex and striatum guides selection. J. Neurosci. 36, 11223–11237 (2016).
Lopatina, N. et al. Lateral orbitofrontal neurons acquire responses to upshifted, downshifted, or blocked cues during unblocking. eLife 4, e11299 (2015).
McDannald, M. A. et al. Orbitofrontal neurons acquire responses to 'valueless' Pavlovian cues during unblocking. eLife 3, e02653 (2014).
Tsujimoto, S., Genovesio, A. & Wise, S. P. Neuronal activity during a cued strategy task: comparison of dorsolateral, orbital, and polar prefrontal cortex. J. Neurosci. 32, 11017–11031 (2012).
Kepecs, A., Uchida, N., Zariwala, H. A. & Mainen, Z. F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).
Genovesio, A., Tsujimoto, S., Navarra, G., Falcone, R. & Wise, S. P. Autonomous encoding of irrelevant goals and outcomes by prefrontal cortex neurons. J. Neurosci. 34, 1970–1978 (2014).
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Markov, N. T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2012).
Dombrowski, S. M., Hilgetag, C. C. & Barbas, H. Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey. Cereb. Cortex. 11, 975–988 (2001).
Alexander, G. E., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986).
Seeley, T. D. & Buhrman, S. C. Group decision making in swarms of honey bees. Behav. Ecol. Sociobiol. 45, 19–31 (1999).
Seeley, T. D. et al. Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335, 108–111 (2011).
Acknowledgements
The authors are grateful to many colleagues with whom interactions have shaped ideas within this article, in particular T. Behrens, T. Blanchard, S. Kennerley, J. Pearson, S. Piantadosi, M. Platt, M. Rushworth and T. Seeley. The authors thank H. Barron, A. de Berker, R. Dolan, M. A. Noonan and T. Seow for comments on a previous draft of the manuscript. L.T.H. is supported by a Sir Henry Wellcome Fellowship from the Wellcome Trust (098830/Z/12/Z). B.Y.H. is supported by a US National Institutes of Health award (DA037229).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
PowerPoint slides
Rights and permissions
About this article
Cite this article
Hunt, L., Hayden, B. A distributed, hierarchical and recurrent framework for reward-based choice. Nat Rev Neurosci 18, 172–182 (2017). https://doi.org/10.1038/nrn.2017.7
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrn.2017.7
This article is cited by
-
Temporally organized representations of reward and risk in the human brain
Nature Communications (2024)
-
Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans
Communications Biology (2023)
-
Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex
Nature Communications (2023)
-
Theta but not beta activity is modulated by freedom of choice during action selection
Scientific Reports (2022)
-
Advances in modeling learning and decision-making in neuroscience
Neuropsychopharmacology (2022)
