Hostname: page-component-54dcc4c588-wlffp Total loading time: 0 Render date: 2025-09-26T22:30:45.308Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning how to reason and deciding when to decide

Published online by Cambridge University Press:  18 July 2023

Senne Braem Open the ORCID record for Senne Braem [Opens in a new window] ,
Leslie Held ,
Amitai Shenhav  and
Romy Frömer
Senne Braem
Affiliation:
Department of Experimental Psychology, Universiteit Gent, Gent, Belgium senne.braem@ugent.be; https://users.ugent.be/~sbraem/ leslie.held@ugent.be
Leslie Held
Affiliation:
Department of Experimental Psychology, Universiteit Gent, Gent, Belgium senne.braem@ugent.be; https://users.ugent.be/~sbraem/ leslie.held@ugent.be
Amitai Shenhav
Affiliation:
Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA amitai_shenhav@brown.edu; https://www.shenhavlab.org
Romy Frömer
Affiliation:
Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA amitai_shenhav@brown.edu; https://www.shenhavlab.org Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK r.froemer@bham.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

Research on human reasoning has both popularized and struggled with the idea that intuitive and deliberate thoughts stem from two different systems, raising the question how people switch between them. Inspired by research on cognitive control and conflict monitoring, we argue that detecting the need for further thought relies on an intuitive, context-sensitive process that is learned in itself.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e115
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abrahamse, E., Braem, S., Notebaert, W., & Verguts, T. (2016). Grounding cognitive control in associative learning. Psychological Bulletin, 142, 693–728.CrossRefGoogle ScholarPubMed
Berlyne, D. (1960). Conflict, arousal, and curiosity. McGraw-Hill.CrossRefGoogle Scholar
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113(4), 700.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Braem, S. (2017). Conditioning task switching behavior. Cognition, 166, 272–276.CrossRefGoogle ScholarPubMed
Braem, S., & Egner, T. (2018). Getting a grip on cognitive flexibility. Current Directions in Psychological Science, 27(6), 470–476.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLoS Computational Biology, 17(3), e1008863.CrossRefGoogle ScholarPubMed
Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14(11), 1462–1467.CrossRefGoogle ScholarPubMed
Dasgupta, I., & Gershman, S. J. (2021). Memory as a computational resource. Trends in Cognitive Sciences, 25(3), 240–251.CrossRefGoogle ScholarPubMed
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135.CrossRefGoogle ScholarPubMed
Doebel, S. (2020). Rethinking executive function and its development. Perspectives on Psychological Science, 15(4), 942–956.CrossRefGoogle ScholarPubMed
Frömer, R., & Shenhav, A. (2022). Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making. Neuroscience & Biobehavioral Reviews, 134, 104483.CrossRefGoogle ScholarPubMed
Gershman, S. (2021). What makes us smart: The computational logic of human cognition. Princeton University Press.Google Scholar
Grahek, I., Frömer, R., Prater Fahey, M., & Shenhav, A. (2023). Learning when effort matters: Neural dynamics underlying updating and adaptation to changes in performance efficacy. Cerebral Cortex, 33(5), 2395–2411.CrossRefGoogle ScholarPubMed
Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019). Doing more with less: Meta-reasoning and meta-learning in humans and machines. Current Opinion in Behavioral Sciences, 29, 24–30.CrossRefGoogle Scholar
Hunt, L. T., Daw, N. D., Kaanders, P., MacIver, M. A., Mugan, U., Procyk, E., … Kolling, N. (2021). Formalizing planning and information search in naturalistic decision-making. Nature Neuroscience, 24(8), 1051–1064.CrossRefGoogle ScholarPubMed
Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implications. Neuron, 87(2), 451–462.CrossRefGoogle ScholarPubMed
Jang, A. I., Sharma, R., & Drugowitsch, J. (2021). Optimal policy for attention-modulated decisions explains human fixation behavior. eLife, 10, e63436.CrossRefGoogle ScholarPubMed
Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T. L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology, 14(4), e1006043.CrossRefGoogle ScholarPubMed
Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95(4), 492.CrossRefGoogle Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167–202.CrossRefGoogle ScholarPubMed
Otto, A. R., Braem, S., Silvetti, M., & Vassena, E. (2022). Is the juice worth the squeeze? Learning the marginal value of mental effort over time. Journal of Experimental Psychology: General, 151(10), 2324–2341.CrossRefGoogle Scholar
Ratcliff, R., & Frank, M. J. (2012). Reinforcement-based decision making in corticostriatal circuits: Mutual constraints by neurocomputational and diffusion models. Neural Computation, 24(5), 1186–1229.CrossRefGoogle ScholarPubMed
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260–281.CrossRefGoogle ScholarPubMed
Shadlen, M. N., & Shohamy, D. (2016). Decision making and sequential sampling from memory. Neuron, 90(5), 927–939.CrossRefGoogle ScholarPubMed
Shenhav, A. (2017). The perils of losing control: Why self-control is not just another value-based decision. Psychological Inquiry, 28(2–3), 148–152.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Smith, S. M., & Krajbich, I. (2019). Gaze amplifies value in decision making. Psychological Science, 30(1), 116–128.CrossRefGoogle ScholarPubMed
Son, J. Y., Bhandari, A., & FeldmanHall, O. (2019). Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions. Scientific Reports, 9(1), 1–15.CrossRefGoogle ScholarPubMed
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148–174.CrossRefGoogle ScholarPubMed
Wang, J. X. (2021). Meta-learning in natural and artificial intelligence. Current Opinion in Behavioral Sciences, 38, 90–95.CrossRefGoogle Scholar
Yang, Q., Xing, J., Braem, S., & Pourtois, G. (2022). The selective use of punishments on congruent versus incongruent trials in the Stroop task. Neurobiology of Learning and Memory, 193, 107654.CrossRefGoogle ScholarPubMed