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20 - Neurocomputational Models of Cognitive Control

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

By
Debbie M. Yee  and
Todd S. Braver
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Cognitive control, the ability to flexibly and selectively process information in the service of higher-level goals, is essential to daily functioning. However, despite the burgeoning research in this domain, much remains to be understood regarding its underlying neurocomputational mechanisms. This chapter highlights several prominent models that have made significant progress towards understanding the core principles of neural information processing and computation that are central to cognitive control. Neural network models are reviewed that characterize: (1) how tasks are represented, updated, and learned (e.g., attentional control, task-switching, structure learning); and (2) how cognitive control is evaluated and allocated based on assessments of demand (e.g., conflict monitoring, outcome prediction, and expected value of control). This brief survey of influential theoretical models provides an important foundational introduction into the primary mechanisms of cognitive control, and concludes with key open questions and future directions aimed at developing a fuller understanding of this domain.

Keywords

cognitive controlattentiontask-setstask-switchingstructure learningconflict monitoringdorsal anterior cingulate cortex
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 664 - 702
Publisher: Cambridge University Press
Print publication year: 2023

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References

Aarts, E., & Roelofs, A. (2011). Attentional control in anterior cingulate cortex based on probabilistic cueing. Journal of Cognitive Neuroscience, 23(3), 716–727. https://doi.org/10.1162/jocn.2010.21435Google Scholar
Alexander, W. H., & Brown, J. W. (2010). Computational models of performance monitoring and cognitive control. Topics in Cognitive Science, 2(4), 658–677. https://doi.org/10.1111/j.1756-8765.2010.01085.xGoogle Scholar
Alexander, W. H., & Brown, J. W. (2011). Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14(10), 1338–1344. https://doi.org/10.1038/nn.2921Google Scholar
Alexander, W. H., & Brown, J. W. (2014). A general role for medial prefrontal cortex in event prediction. Frontiers in Computational Neuroscience, 8, 1–11. https://doi.org/10.3389/fncom.2014.00069Google Scholar
Alexander, W. H., & Brown, J. W. (2015). Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354–2410.Google Scholar
Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. Psychological Review, 115(3), 602–639. https://doi.org/10.1037/0033-295x.115.3.602Google Scholar
Anderson, J. R. (1996). A simple theory of complex cognition. American Psychologist, 51(4), 355–365. https://doi.org/10.1037//0003-066x.51.4.355Google Scholar
Ardid, S., Wang, X.-J., & Compte, A. (2007). An integrated microcircuit model of attentional processing in the neocortex. The Journal of Neuroscience, 27(32), 8486–8495. https://doi.org/10.1523/jneurosci.1145-07.2007Google Scholar
Aston-Jones, G., & Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine: adaptive gain and optimal performance. Annual Review of Neuroscience, 28(1), 403–450. https://doi.org/10.1146/annurev.neuro.28.061604.135709Google Scholar
Badre, D., Bhandari, A., Keglovits, H., & Kikumoto, A. (2021). The dimensionality of neural representations for control. Current Opinion in Behavioral Sciences, 38, 20–28. https://doi.org/10.1016/j.cobeha.2020.07.002CrossRefGoogle ScholarPubMed
Barch, D. M., & Ceaser, A. (2012). Cognition in schizophrenia: core psychological and neural mechanisms. Trends in Cognitive Sciences, 16(1), 27–34. https://doi.org/10.1016/j.tics.2011.11.015Google Scholar
Barch, D. M., Culbreth, A., & Sheffield, J. (2018). Systems level modeling of cognitive control in psychiatric disorders: a focus on schizophrenia. In A. Anticevic & J. Murray (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (pp. 145–173). London: Elsevier.Google Scholar
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214–1221. https://doi.org/10.1038/nn1954Google Scholar
Bench, C. J., Frith, C. D., Grasby, P. M., et al. (1993). Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia, 31(9), 907–922. https://doi.org/10.1016/0028-3932(93)90147-rGoogle Scholar
Bengtsson, S. L., Haynes, J.-D., Sakai, K., Buckley, M. J., & Passingham, R. E. (2008). The representation of abstract task rules in the human prefrontal cortex. Cerebral Cortex, 19(8), 1929–1936. https://doi.org/10.1093/cercor/bhn222Google Scholar
Berlyne, D. E. (1957). Uncertainty and conflict: a point of contact between information-theory and behavior-theory concepts. Psychological Review, 64(6), 329–339. https://doi.org/10.1037/h0041135Google Scholar
Blais, C., Harris, M. B., Guerrero, J. V., & Bunge, S. A. (2012). Rethinking the role of automaticity in cognitive control. The Quarterly Journal of Experimental Psychology, 65(2), 268–276. https://doi.org/10.1080/17470211003775234Google Scholar
Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2), 7. https://doi.org/10.1145/1667053.1667056Google Scholar
Botvinick, M. M. (2007). Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 356–366. https://doi.org/10.3758/cabn.7.4.356Google Scholar
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652. https://doi.org/10.1037/0033-295x.108.3.624Google Scholar
Botvinick, M. M., & Cohen, J. D. (2014). The computational and neural basis of cognitive control: charted territory and new frontiers. Cognitive Science, 38, 1249–1285. https://doi.org/10.1111/cogs.12126Google Scholar
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: an update. Trends in Cognitive Sciences, 8(12), 539–546. https://doi.org/10.1016/j.tics.2004.10.003Google Scholar
Botvinick, M. M., Niv, Y., & Barto, A. C. (2009). Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition, 113(3), 262–280. https://doi.org/10.1016/j.cognition.2008.08.011Google Scholar
Boureau, Y., Sokol-Hessner, P., & Daw, N. D. (2015). Deciding how to decide: self-control and meta-decision making. Trends in Cognitive Sciences, 19(11), 700–710. https://doi.org/10.1016/j.tics.2015.08.013Google Scholar
Brass, M., Ullsperger, M., Knoesche, T. R., Cramon, D. Y. von, & Phillips, N. A. (2005). Who comes first? The role of the prefrontal and parietal cortex in cognitive control. Journal of Cognitive Neuroscience, 17(9), 1367–1375. https://doi.org/10.1162/0898929054985400Google Scholar
Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106–113. https://doi.org/10.1016/j.tics.2011.12.010Google Scholar
Braver, T. S., Barch, D. M., & Cohen, J. D. (1999). Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function. Biological Psychiatry, 46(3), 312–328. http://www.ncbi.nlm.nih.gov/pubmed/10435197Google Scholar
Braver, T. S., Barch, D. M., Keys, B. A., et al. (2001). Context processing in older adults: evidence for a theory relating cognitive control to neurobiology in healthy aging. Journal of Experimental Psychology: General, 130(4), 746–763. https://doi.org/10.1037//0096-3445.130.4.746CrossRefGoogle ScholarPubMed
Braver, T. S., & Cohen, J. D. (2000). On the control of control: the role of dopamine in regulating prefrontal function and working memory. In Monsell, S. & Driver, J. (Eds.), Making Working Memory Work (pp. 551–581). Cambridge, MA: MIT Press. https://doi.org/10.1016/s0165-0173(03)00143-7Google Scholar
Braver, T. S., & Cohen, J. D. (2001). Working memory, cognitive control, and the prefrontal cortex: computational and empirical studies. Cognitive Processing, 2, 25–55.Google Scholar
Braver, T. S., & Ruge, H. (2006). Functional neuroimaging of executive functions. In Cabeza, R. & Kingstone, A. (Eds.), Handbook of Functional Neuroimaging of Cognition (2nd ed., pp. 307–348). Cambridge, MA: MIT Press.Google Scholar
Brown, J. W. (2013). Beyond conflict monitoring: cognitive control and the neural basis of thinking before you act. Current Directions in Psychological Science, 22(3), 179–185. https://doi.org/10.1177/0963721412470685Google Scholar
Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307(5712), 1110–1121.Google Scholar
Brown, J. W., Reynolds, J. R., & Braver, T. S. (2007). A computational model of fractionated conflict-control mechanisms in task-switching. Cognitive Psychology, 55(1), 37–85. https://doi.org/10.1016/j.cogpsych.2006.09.005Google Scholar
Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., & Cohen, J. (2021). Learning to overexert cognitive control in a Stroop task. Cognitive, Affective, & Behavioral Neuroscience, 21(3), 453–471. https://doi.org/10.3758/s13415-020-00845-xGoogle Scholar
Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D., & Cohen, J. D. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280(5364), 747–749. https://doi.org/10.1126/science.280.5364.747Google Scholar
Carter, C. S., & Veen, V. van. (2007). Anterior cingulate cortex and conflict detection: an update of theory and data. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 367–379. https://doi.org/10.3758/cabn.7.4.367Google Scholar
Cavanagh, J. F., Masters, S. E., Bath, K., & Frank, M. J. (2014). Conflict acts as an implicit cost in reinforcement learning. Nature Communications, 5, 1–10. https://doi.org/10.1038/ncomms6394Google Scholar
Chatham, C. H., Herd, S. A., Brant, A. M., et al. (2011). From an executive network to executive control: a computational model of the N-back task. Journal of Cognitive Neuroscience, 11(23), 3598–3619. https://doi.org/10.1162/jocn_a_00047Google Scholar
Chen, Y., Spagna, A., Wu, T., et al. (2019). Testing a cognitive control model of human intelligence. Scientific Reports, 9(1), 1–17. https://doi.org/10.1038/s41598-019-39685-2Google Scholar
Chong, T. T. J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017). Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biology, 15(2), 1–28. https://doi.org/10.1371/journal.pbio.1002598Google Scholar
Cohen, J. D. (2017). Cognitive control: core constructs and current considerations. In T. Egner (Ed.), The Wiley Handbook of Cognitive Control (pp. 3–27). Oxford: Wiley-Blackwell.Google Scholar
Cohen, J. D., Braver, T. S., & Brown, J. W. (2002). Computational perspectives on dopamine function in prefrontal cortex. Current Opinion in Neurobiology, 12(2), 223–229. www.sciencedirect.com/science/article/pii/S0959438802003148Google Scholar
Cohen, J. D., Braver, T. S., & O’Reilly, R. C. (1996). A computational approach to prefrontal cortex, cognitive control and schizophrenia: recent developments and current challenges. Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, 351, 1515–1527.Google Scholar
Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: a parallel distributed processing account of the Stroop effect. Psychological Review, 97(3), 332–361. https://doi.org/10.1037/0033-295x.97.3.332Google Scholar
Cohen, J. D., & Huston, T. A. (1994). Progress in the use of interactive models for understanding attention and performance. In Umilta, C. & Moscovitch, M. (Eds.), Attention and Performance XV: Conscious and Nonconscious Information Processing (pp. 453–476). Cambridge, MA: MIT Press.Google Scholar
Cohen, J. D., Usher, M., & McClelland, J. L. (1998). A PDP approach to set size effects within the Stroop task: reply to Kanne, Balota, Spieler, and Faust (1998). Psychological Review, 105(1), 188–194. https://doi.org/10.1037/0033-295x.105.1.188Google Scholar
Cole, M. W., Ito, T., & Braver, T. S. (2016). The behavioral relevance of task information in human prefrontal cortex. Cerebral Cortex, 26(6), 2497–2505. https://doi.org/10.1093/cercor/bhv072Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 8988–8999. https://doi.org/10.1523/jneurosci.0536-12.2012Google Scholar
Cole, M. W., Yeung, N., Freiwald, W. A., & Botvinick, M. (2009). Cingulate cortex: diverging data from humans and monkeys. Trends in Neurosciences, 32(11), 566–574. https://doi.org/10.1016/j.tins.2009.07.001Google Scholar
Collins, A. G. E. (2017). The cost of structure learning. Journal of Cognitive Neuroscience, 29(10), 1646–1655. https://doi.org/10.1162/jocn_a_01128Google Scholar
Collins, A. G. E., Cavanagh, J. F., & Frank, M. J. (2014). Human EEG uncovers latent generalizable rule structure during learning. The Journal of Neuroscience, 34(13), 4677–4685. https://doi.org/10.1523/jneurosci.3900-13.2014Google Scholar
Collins, A. G. E., & Frank, M. J. (2013). Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychological Review, 120(1), 190–229. https://doi.org/10.1037/a0030852Google Scholar
Collins, A. G. E., & Frank, M. J. (2016). Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition, 152, 160–169. https://doi.org/10.1016/j.cognition.2016.04.002Google Scholar
Cools, R. (2016). The costs and benefits of brain dopamine for cognitive control. Wiley Interdisciplinary Reviews: Cognitive Science, 7, 317–329. https://doi.org/10.1002/wcs.1401Google Scholar
Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E. J., & Rushworth, M. F. S. (2009). Effort-based cost-benefit valuation and the human brain. Journal of Neuroscience, 29(14), 4531–4541. https://doi.org/10.1523/jneurosci.4515-08.2009Google Scholar
D’Ardenne, K., Eshel, N., Luka, J., et al. (2012). Role of prefrontal cortex and the midbrain dopamine system in working memory updating. Proceedings of the National Academy of Sciences, 109(49), 19900–19909. https://doi.org/10.1073/pnas.1116727Google Scholar
Dayan, P. (2012). How to set the switches on this thing. Current Opinion in Neurobiology, 22(6), 1068–1074. https://doi.org/10.1016/j.conb.2012.05.011Google Scholar
Dayan, P., & Yu, A. J. (2009). Phasic norepinephrine: a neural interrupt signal for unexpected events. Network: Computation in Neural Systems, 17(4), 335–350. https://doi.org/10.1080/09548980601004024Google Scholar
De Pisapia, N. D., Repovš, G., & Braver, T. S. (2008). Computational models of attention and cognitive control. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (pp. 422–450). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9780511816772.019Google Scholar
Deco, G., & Rolls, E. T. (2003). Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex. European Journal of Neuroscience, 18(8), 2374–2390. https://doi.org/10.1046/j.1460-9568.2003.02956.xGoogle Scholar
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193–222. https://doi.org/10.1146/annurev.ne.18.030195.001205Google Scholar
Dixon, M. L., & Christoff, K. (2012). The decision to engage cognitive control is driven by expected reward-value: neural and behavioral evidence. PLoS One, 7(12). https://doi.org/10.1371/journal.pone.0051637Google Scholar
Dixon, M. L., Vega, A. D. L., Mills, C., et al. (2018). Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proceedings of the National Academy of Sciences, 115(7), 201715766. https://doi.org/10.1073/pnas.1715766115Google Scholar
Domenech, P., & Koechlin, E. (2015). Executive control and decision-making in the prefrontal cortex. Current Opinion in Behavioral Sciences, 1, 101–106. https://doi.org/10.1016/j.cobeha.2014.10.007Google Scholar
Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15(4–6), 495–506. https://doi.org/10.1016/s0893-6080(02)00044-8Google Scholar
Dreisbach, G., & Fischer, R. (2012). The role of affect and reward in the conflict-triggered adjustment of cognitive control. Frontiers in Human Neuroscience, 6, 342. https://doi.org/10.3389/fnhum.2012.00342Google Scholar
Dreisbach, G., & Fischer, R. (2015). Conflicts as aversive signals for control adaptation. Current Directions in Psychological Science, 24(4), 255–260. https://doi.org/10.1177/0963721415569569Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends in Cognitive Sciences, 14(4), 172–179. https://doi.org/10.1016/j.tics.2010.01.004Google Scholar
Duncan, J. (2013). The structure of cognition: attentional episodes in mind and brain. Neuron, 80(1), 35–50. https://doi.org/10.1016/j.neuron.2013.09.015Google Scholar
Duncan, J., & Owen, A. M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23(10), 475–483. https://doi.org/10.1016/s0166-2236(00)01633-7Google Scholar
Durstewitz, D., & Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Networks, 15, 561–572.Google Scholar
Duverne, S., & Koechlin, E. (2017). Rewards and cognitive control in the human prefrontal cortex. Cerebral Cortex, 27(10), 1–16. https://doi.org/10.1093/cercor/bhx210Google Scholar
Egner, T. (Ed.). (2017). The Wiley Handbook of Cognitive Control. Oxford: Wiley Blackwell.Google Scholar
Egner, T., & Hirsch, J. (2005). Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information. Nature Neuroscience, 8(12), 1784–1790. https://doi.org/10.1038/nn1594Google Scholar
Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. H. Ross (Ed.),The Psychology of Learning and Motivation: Advances in Research and Theory (pp. 145–199). New York, NY: Academic Press. https://doi.org/10.1016/s0079-7421(03)44005-xGoogle Scholar
Eppinger, B., Goschke, T., & Musslick, S. (2021). Meta-control: from psychology to computational neuroscience. Cognitive, Affective, & Behavioral Neuroscience, 21(3), 447–452. https://doi.org/10.3758/s13415-021-00919-4Google Scholar
Feng, S. F., Schwemmer, M., Gershman, S. J., & Cohen, J. D. (2014). Multitasking versus multiplexing: toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cognitive, Affective, & Behavioral Neuroscience, 14(1), 129–146. https://doi.org/10.3758/s13415-013-0236-9Google Scholar
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, 1258–1270. https://doi.org/10.1016/j.neuron.2022.01.005Google Scholar
Frank, M. J., & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cerebral Cortex, 22(3), 509–526. https://doi.org/10.1093/cercor/bhr114Google Scholar
Freund, M., Etzel, J., & Braver, T. (2021). Neural coding of cognitive control: the representational similarity analysis approach. Trends in Cognitive Sciences, 25, 622–638. https://doi.org/10.1016/j.tics.2021.03.011Google Scholar
Friedman, N. P., & Robbins, T. W. (2021). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47(1), 1–18. https://doi.org/10.1038/s41386-021-01132-0Google Scholar
Fritz, J., & Dreisbach, G. (2013). Conflicts as aversive signals: conflict priming increases negative judgments for neutral stimuli. Cognitive, Affective, Behavioral Neuroscience, 13(2), 311–317. https://doi.org/10.3758/s13415-012-0147-1Google Scholar
Fröbose, M. I., & Cools, R. (2018). Chemical neuromodulation of cognitive control avoidance. Current Opinion in Behavioral Sciences, 22, 121–127. https://doi.org/10.1016/j.cobeha.2018.01.027Google Scholar
Frömer, R., Lin, H., Wolf, C. K. D., Inzlicht, M., & Shenhav, A. (2021). Expectations of reward and efficacy guide cognitive control allocation. Nature Communications, 12(1), 1030. https://doi.org/10.1038/s41467–021-21315-zCrossRefGoogle ScholarPubMed
Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: high dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 66–74. https://doi.org/10.1016/j.conb.2016.01.010Google Scholar
Gehring, W. J., Goss, B., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385–390. https://doi.org/10.1111/j.1467-9280.1993.tb00586.xGoogle Scholar
Gershman, S. J., Cohen, J. D., & Niv, Y. (2010). Learning to selectively attend. 32nd Annual Proceedings of the Cognitive Science Society, pp. 1270–1275.Google Scholar
Gilbert, S. J., & Shallice, T. (2002). Task switching: A PDP model. Cognitive Psychology, 44(3), 297–337. https://doi.org/10.1006/cogp.2001.0770Google Scholar
Grahek, I., Musslick, S., & Shenhav, A. (2020). A computational perspective on the roles of affect in cognitive control. International Journal of Psychophysiology, 151, 25–34. https://doi.org/10.1016/j.ijpsycho.2020.02.001Google Scholar
Gratton, G., Cooper, P., Fabiani, M., Carter, C. S., & Karayanidis, F. (2018). Dynamics of cognitive control: theoretical bases, paradigms, and a view for the future. Psychophysiology, 55, 1–29. https://doi.org/10.1111/psyp.13016Google Scholar
Gu, S., Pasqualetti, F., Cieslak, M., et al. (2015). Controllability of structural brain networks. Nature Communications, 6(1), 8414. https://doi.org/10.1038/ncomms9414Google Scholar
Hamid, A. A., Pettibone, J. R., Mabrouk, O. S., et al. (2016). Mesolimbic dopamine signals the value of work. Nature Neuroscience, 19(1), 117–126. https://doi.org/10.1038/nn.4173Google Scholar
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2007). Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 1601–1613. https://doi.org/10.1098/rstb.2007.2055Google Scholar
Herd, S. A., O’Reilly, R. C., Hazy, T. E., et al. (2014). A neural network model of individual differences in task switching abilities. Neuropsychologia, 62, 375–389. https://doi.org/10.1016/j.neuropsychologia.2014.04.014Google Scholar
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679–709. https://doi.org/10.1037//0033-295x.109.4.679Google Scholar
Holroyd, C. B., Nieuwenhuis, S., Yeung, N., et al. (2004). Dorsal anterior cingulate cortex shows fMRI response to internal and external error signals. Nature Neuroscience, 7(5), 497–498. https://doi.org/10.1038/nn1238Google Scholar
Holroyd, C. B., Yeung, N., Coles, M. G. H., & Cohen, J. D. (2005). A mechanism for error detection in speeded response time tasks. Journal of Experimental Psychology: General, 134(2), 163–191. https://doi.org/10.1037/0096-3445.134.2.163Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554Google Scholar
Kerns, J. G. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 1023–1026. https://doi.org/10.1126/science.1089910Google Scholar
Khamassi, M., Quilodran, R., Enel, P., Dominey, P. F., & Procyk, E. (2015). Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex. Cerebral Cortex, 25(9), 3197–3218. https://doi.org/10.1093/cercor/bhu114Google Scholar
Kool, W., & Botvinick, M. (2014). A labor/leisure tradeoff in cognitive control. Journal of Experimental Psychology: General, 143(1), 131–141. https://doi.org/10.1037/a0031048Google Scholar
Kool, W., Shenhav, A., & Botvinick, M. M. (2017). Cognitive control as cost-benefit decision making. In T. Egener (Ed.), The Wiley Handbook of Cognitive Control (pp. 167–189). Oxford: Wiley-Blackwell. https://doi.org/10.1002/9781118920497.ch10Google Scholar
Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12(7), 939–945. https://doi.org/10.1038/nn.2321CrossRefGoogle ScholarPubMed
Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proceedings of the National Academy of Sciences, 110(41), 16390–16395. https://doi.org/10.1073/pnas.1303547110Google Scholar
Leng, X., Yee, D., Ritz, H., & Shenhav, A. (2021). Dissociable influences of reward and punishment on adaptive cognitive control. PLoS Computational Biology, 17(12), 1–21. https://doi.org/10.1371/journal.pcbi.1009737Google Scholar
Lieder, F., & Griffiths, T. L. (2019). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, 1–85. https://doi.org/10.1017/s0140525x1900061xGoogle Scholar
Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T. L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology, 14(4), 1–27. https://doi.org/10.1371/journal.pcbi.1006043Google Scholar
Logan, G. D. (1989). Automaticity and cognitive control. In J. S. Uleman & J. A. Bargh, (Eds.), Unintended Thought (pp. 52–74). Hove: Guilford Press.Google Scholar
Luks, T. L., Simpson, G. V., Feiwell, R. J., & Miller, W. L. (2002). Evidence for anterior cingulate cortex involvement in monitoring preparatory attentional set. NeuroImage, 17(2), 792–802. https://doi.org/10.1006/nimg.2002.1210Google Scholar
MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288(5472), 1835–1838. https://doi.org/10.1126/science.288.5472.1835Google Scholar
MacLeod, C. M. (1991). Half a century of reseach on the Stroop effect: an integrative review. Psychological Bulletin, 109(2), 163–203. https://doi.org/10.1037/0033-2909.109.2.163Google Scholar
Masís, J. A., Musslick, S., & Cohen, J. (2021). The value of learning and cognitive control allocation. In Proceedings of the Annual Meeting of the Cognitive Science Society. https://escholarship.org/uc/item/7w0223v0Google Scholar
McClelland, J. L. (1979). On the time relations of mental processes: an examination of systems of processes in cascade. Psychological Review, 86(4), 287–330. https://doi.org/10.1037/0033-295x.86.4.287Google Scholar
McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences, 107(17), 7922–7926. https://doi.org/10.1073/pnas.0910662107Google Scholar
Melcher, T., & Gruber, O. (2009). Decomposing interference during Stroop performance into different conflict factors: an event-related fMRI study. Cortex, 45(2), 189–200. https://doi.org/10.1016/j.cortex.2007.06.004Google Scholar
Milham, M. P., & Banich, M. T. (2005). Anterior cingulate cortex: an fMRI analysis of conflict specificity and functional differentiation. Human Brain Mapping, 25(3), 328–335. https://doi.org/10.1002/hbm.20110Google Scholar
Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1, 59–65.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167Google Scholar
Minai, A. A. (2015). Computational models of cognitive and motor control. In Kacprzyk, J. & Pedrycz, W. (Eds.), Springer Handbook of Computational Intelligence (pp. 665–682). London: Springer. https://doi.org/10.1007/978-3-662-43505-2_35Google Scholar
Modirrousta, M., & Fellows, L. K. (2008). Medial prefrontal cortex plays a critical and selective role in ‘feeling of knowing’ meta-memory judgments. Neuropsychologia, 46(12), 2958–2965. https://doi.org/10.1016/j.neuropsychologia.2008.06.011Google Scholar
Momennejad, I., Russek, E. M., Cheong, J. H., Botvinick, M. M., Daw, N. D., & Gershman, S. J. (2017). The successor representation in human reinforcement learning. Nature Human Behaviour, 1(9), 680–692. https://doi.org/10.1038/s41562-017-0180-8Google Scholar
Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7(3), 134–140. https://doi.org/10.1016/s1364-6613(03)00028-7Google Scholar
Montague, P., Dayan, P., & Sejnowski, T. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16(5), 1936–1947. https://doi.org/10.1523/jneurosci.16-05-01936.1996Google Scholar
Musslick, S., & Cohen, J. (2020). Rationalizing constraints on the capacity for cognitive control. PsyArXiv. https://psyarxiv.com/vtknh/Google Scholar
Musslick, S., Cohen, J. D., & Shenhav, A. (2019). Decomposing individual differences in cognitive control: a model-based approach. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society.Google Scholar
Musslick, S., Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2015). A computational model of control allocation based on the expected value of control. In Reinforcement Learning and Decision Making Conference. Edmonton, Alberta, Canada.Google Scholar
Nassar, M. R., & Frank, M. J. (2016). Taming the beast: extracting generalizable knowledge from computational models of cognition. Current Opinion in Behavioral Sciences, 11, 49–54. https://doi.org/10.1016/j.cobeha.2016.04.003Google Scholar
Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S. (2012). Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, Behavioral Neuroscience, 12(2), 241–268. https://doi.org/10.3758/s13415-011-0083-5Google Scholar
Norman, D. A., & Shallice, T. (1986). Attention to action: willed and automatic control of behavior. In Davidson, R., Schwartz, G, & Shapiro, D (Eds.), Consciousness and Self-Regulation: Advances in Research and Theory (pp. 1–18). London: Springer.Google Scholar
O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 91–94. https://doi.org/10.1126/science.1127242Google Scholar
O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically-based computational model of working memory. In A. Miyake & P. Shah (Eds.), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 375–411). Cambridge: Cambridge University Press. https://doi.org/10.1017/cbo9781139174909Google Scholar
O’Reilly, R. C., & Frank, M. J. (2006). Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation, 18(2), 283–328. https://doi.org/10.1162/089976606775093909Google Scholar
O’Reilly, R. C., Herd, S. A., & Pauli, W. M. (2010). Computational models of cognitive control. Current Opinion in Neurobiology, 20(2), 367–377. https://doi.org/10.1016/j.conb.2010.01.008Google Scholar
O’Reilly, R. C., Munakata, Y., Frank, M. J., & Hazy, T. E. (2012). Computational Cognitive Neuroscience. Wiki Book, 4th ed. (2020). Available at: https://CompCogNeuro.orgGoogle Scholar
Ott, T., & Nieder, A. (2019). Dopamine and cognitive control in prefrontal cortex. Trends in Cognitive Sciences, 23(3), 213–234. https://doi.org/10.1016/j.tics.2018.12.006Google Scholar
Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In Solso, R. L. (Ed.), Information Processing and Cognition: The Loyola Symposium (pp. 55–85). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Ranti, C., Chatham, C. H., & Badre, D. (2015). Parallel temporal dynamics in hierarchical cognitive control. Cognition, 142, 205–229. https://doi.org/10.1016/j.cognition.2015.05.003Google Scholar
Reverberi, C., Görgen, K., & Haynes, J.-D. (2012). Compositionality of rule representations in human prefrontal cortex. Cerebral Cortex, 22(6), 1237–1246. https://doi.org/10.1093/cercor/bhr200Google Scholar
Reynolds, J. R., Braver, T. S., Brown, J. W., & Stigchel, S. V. der. (2006). Computational and neural mechanisms of task switching. Neurocomputing, 69(10–12), 1332–1336. https://doi.org/10.1016/j.neucom.2005.12.102Google Scholar
Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. Science, 306, 443–447.Google Scholar
Rigotti, M., Barak, O., Warden, M. R., et al. (2013). The importance of mixed selectivity in complex cognitive tasks. Nature, 497(7451), 585–590. https://doi.org/10.1038/nature12160Google Scholar
Roelofs, A., Turennout, M. van, & Coles, M. G. H. (2006). Anterior cingulate cortex activity can be independent of response conflict in Stroop-like tasks. Proceedings of the National Academy of Sciences, 103(37), 13884–13889. https://doi.org/10.1073/pnas.0606265103Google Scholar
Rogers, R. D., & Monsell, S. (1995). Costs of a predictible switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124(2), 207–231. https://doi.org/10.1037/0096-3445.124.2.207Google Scholar
Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and flexible cognitive control: rules without symbols. Proceedings of the National Academy of Sciences, 102(20), 7338–7343. https://doi.org/10.1073/pnas.0502455102Google Scholar
Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. In D. E. Rumelhart & J. L. McClelland, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1 (pp. 45–76). Cambridge, MA: MIT Press. www.csri.utoronto.ca/~hinton/absps/pdp2.pdfGoogle Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 2 (pp. 7–57). Cambridge, MA: MIT Press. https://doi.org/10.1016/b978-1-4832-1446-7.50020-0Google Scholar
Sakai, K. (2008). Task set and prefrontal cortex. Neuroscience, 31(1), 219–245. https://doi.org/10.1146/annurev.neuro.31.060407.125642Google Scholar
Schneider, W., & Chein, J. M. (2003). Controlled automatic processing: behavior, theory, and biological mechanisms. Cognitive Science, 27(3), 525–559. https://doi.org/10.1016/s0364-0213(03)00011-9Google Scholar
Servan-Schreiber, D., Printz, H., & Cohen, J. D. (1990). A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior. Science, 249(4971), 892–895. https://doi.org/10.1126/science.2392679Google Scholar
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240. https://doi.org/10.1016/j.neuron.2013.07.007Google Scholar
Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 1286–1291. https://doi.org/10.1038/nn.4384Google Scholar
Shenhav, A., Musslick, S., Lieder, F., et al. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40(1), 99–124. https://doi.org/10.1146/annurev-neuro-072116-031526Google Scholar
Sheth, S. A., Mian, M. K., Patel, S. R., et al. (2012). Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature, 488, 1–5. https://doi.org/10.1038/nature11239Google Scholar
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127–190. https://doi.org/10.1037/0033-295x.84.2.127Google Scholar
Silvetti, M., Vassena, E., Abrahamse, E., & Verguts, T. (2018). Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS Computational Biology, 14(8), e1006370. https://doi.org/10.1371/journal.pcbi.1006370Google Scholar
Sohn, M. H., & Anderson, J. R. (2001). Task preparation and task repetition: two-component model of task switching. Journal of Experimental Psychology: General, 130(4), 764–778. https://doi.org/10.1037/0096-3445.130.4.764Google Scholar
Spunt, R. P., Lieberman, M. D., Cohen, J. R., & Eisenberger, N. I. (2012). The phenomenology of error processing: the dorsal ACC response to stop-signal errors tracks reports of negative affect. Journal of Cognitive Neuroscience, 24(8), 1753–1765. https://doi.org/10.1162/jocn_a_00242Google Scholar
Steenbergen, H. van. (2014). Affective modulation of cognitive control: a biobehavioral perspective. In G. H. E. Gendolla, M. Tops, & S. L. Koole (Eds.), Handbook of Biobehavioral Approaches to Self-Regulation (pp. 89–107). New York, NY: Springer. https://doi.org/10.1007/978-1-4939-1236-0_7Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. https://doi.org/10.1037/h0054651Google Scholar
Sutton, R., & Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.Google Scholar
Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J. (2016). Toward the neural implementation of structure learning. Current Opinion in Neurobiology, 37, 99–105. https://doi.org/10.1016/j.conb.2016.01.014Google Scholar
Unsworth, N., & Robison, M. K. (2017). A locus coeruleus-norepinephrine account of individual differences in working memory capacity and attention control. Psychonomic Bulletin & Review, 24(4), 1282–1311. https://doi.org/10.3758/s13423-016-1220-5Google Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2017). Predicting motivation: computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease. Journal of Cognitive Neuroscience, 29(10), 1633–1645. https://doi.org/10.1162/jocn_a_01160Google Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2019). Task-specific prioritization of reward and effort information: novel insights from behavior and computational modeling. Cognitive, Affective, & Behavioral Neuroscience, 19(3), 619–636. https://doi.org/10.3758/s13415-018-00685-wGoogle Scholar
Vassena, E., Deraeve, J., & Alexander, W. H. (2020). Surprise, value and control in anterior cingulate cortex during speeded decision-making. Nature Human Behaviour, 4(4), 412–422. https://doi.org/10.1038/s41562-019-0801-5Google Scholar
Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017). Computational models of anterior cingulate cortex: at the crossroads between prediction and effort. Frontiers in Neuroscience, 11, 1–9. https://doi.org/10.3389/fnins.2017.00316Google Scholar
Veen, V. V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiology Behavior, 77, 477–482.Google Scholar
Venkatraman, V., Rosati, A. G., Taren, A. A., & Huettel, S. A. (2009). Resolving response, decision, and strategic control: evidence for a functional topography in dorsomedial prefrontal cortex. The Journal of Neuroscience, 29(42), 13158–13164. https://doi.org/10.1523/jneurosci.2708-09.2009Google Scholar
Verguts, T. (2017). Computational models of cognitive control. In Egner, T. (Ed.), The Wiley Handbook of Cognitive Control (pp. 125–142). Oxford: Wiley-Blackwell. https://doi.org/10.1002/9781118920497.ch8Google Scholar
Verguts, T., & Notebaert, W. (2008). Hebbian learning of cognitive control: dealing with specific and nonspecific adaptation. Psychological Review, 115(2), 518–525. https://doi.org/10.1037/0033-295x.115.2.518Google Scholar
Verguts, T., & Notebaert, W. (2009). Adaptation by binding: a learning account of cognitive control. Trends in Cognitive Sciences, 13(6), 252–257. https://doi.org/10.1016/j.tics.2009.02.007Google Scholar
Vermeylen, L., Wisniewski, D., Gonzalez-Garcia, C., Hoofs, V., Notebaert, W., & Braem, S. (2020). Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex. Journal of Neuroscience, 40(45), 8715–8725. https://doi.org/10.1523/jneurosci.1744-20.2020Google Scholar
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., et al. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21(6), 860–868. https://doi.org/10.1038/s41593-018-0147-8Google Scholar
Wang, X.-J. (2013). The prefrontal cortex as a quintessential “cognitive-type” neural circuit: working memory and decision making. In Stuss, D. T. & Knight, R. T. (Eds.), Principles of Frontal Lobe Function (pp. 226–248). Cambridge: Cambridge University Press.Google Scholar
Waszak, F., Hommel, B., & Allport, A. (2003). Task-switching and long-term priming: role of episodic stimulus–task bindings in task-shift costs. Cognitive Psychology, 46(4), 361–413. https://doi.org/10.1016/s0010-0285(02)00520-0Google Scholar
Westbrook, A., Bosch, R. van den, Määttä, J. I., et al. (2020). Dopamine promotes cognitive effort by biasing the benefits versus costs of cognitive work. Science, 367(6484), 1362–1366. https://doi.org/10.1126/science.aaz5891Google Scholar
Westbrook, A., & Braver, T. S. (2015). Cognitive effort: a neuroeconomic approach. Cognitive, Affective, Behavioral Neuroscience, 15, 395–415. https://doi.org/10.3758/s13415-015-0334-yGoogle Scholar
Westbrook, A., & Braver, T. S. (2016). Dopamine does double duty in motivating cognitive effort. Neuron, 89(4), 695–710. https://doi.org/10.1016/j.neuron.2015.12.029Google Scholar
Westbrook, A., Lamichhane, B., & Braver, T. (2019). The subjective value of cognitive effort is encoded by a domain-general valuation network. Journal of Neuroscience, 39(20), 3934–3947. https://doi.org/10.1523/jneurosci.3071-18.2019Google Scholar
Wood, J. N., & Grafman, J. (2003). Human prefrontal cortex: processing and representational perspectives. Nature Reviews Neuroscience, 4(2), 139–147. https://doi.org/10.1038/nrn1033Google Scholar
Woolgar, A., Hampshire, A., Thompson, R., & Duncan, J. (2011). Adaptive coding of task-relevant information in human frontoparietal cortex. Journal of Neuroscience, 31(41), 14592–14599. https://doi.org/10.1523/jneurosci.2616-11.2011Google Scholar
Wylie, G., & Allport, A. (2000). Task switching and the measurement of “switch costs.” Psychological Research, 63(3–4), 212–233. https://doi.org/10.1007/s004269900003Google Scholar
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22(2), 297–306. https://doi.org/10.1038/s41593-018-0310-2Google Scholar
Yee, D. M., & Braver, T. S. (2018). Interactions of motivation and cognitive control. Current Opinion in Behavioral Sciences, 19, 83–90. https://doi.org/10.1016/j.cobeha.2017.11.009Google Scholar
Yee, D. M., & Braver, T. S. (2020). Computational models of cognitive control: past and current approaches. In Series, P. (Ed.), Computational Psychiatry: A Primer (pp. 83–104). Cambridge, MA: MIT Press.Google Scholar
Yee, D. M., Crawford, J. L., Lamichhane, B., & Braver, T. S. (2021). Dorsal anterior cingulate cortex encodes the integrated incentive motivational value of cognitive task performance. Journal of Neuroscience, 41(16), 3707–3720. https://doi.org/10.1523/jneurosci.2550-20.2021Google Scholar
Yee, D. M., Leng, X., Shenhav, A., & Braver, T. S. (2022). Aversive motivation and cognitive control. Neuroscience and Biobehavioral Reviews, 133, 104493. https://doi.org/10.1016/j.neubiorev.2021.12.016Google Scholar
Yeung, N. (2013). Conflict monitoring and cognitive control. In Oschner, K. N. & Kosslyn, S. (Eds.), The Oxford Handbook of Cognitive Neuroscience: Volume 2: The Cutting Edges. Oxford: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199988709.013.0018Google Scholar
Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review, 111(4), 931–959. https://doi.org/10.1037/0033-295x.111.4.931Google Scholar
Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681–692. https://doi.org/10.1016/j.neuron.2005.04.026Google Scholar

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