People construct simplified mental representations to plan


  • Lewis, R. L., Howes, A. & Singh, S. Computational rationality: linking mechanism and conduct by way of bounded utility maximization. High. Cogn. Sci. 6, 279–311 (2014).

    PubMed 

    Google Scholar 

  • Griffiths, T. L., Lieder, F. & Goodman, N. D. Rational use of cognitive sources: ranges of study between the computational and the algorithmic. High. Cogn. Sci. 7, 217–229 (2015).

    PubMed 

    Google Scholar 

  • Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: a converging paradigm for intelligence in brains, minds, and machines. Science 349, 273–278 (2015).

    ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 

  • Newell, A. & Simon, H. A. Human Drawback Fixing (Prentice Corridor, 1972).

  • Russell, S. & Norvig, P. Synthetic Intelligence: A Fashionable Method third edn (Prentice Corridor, 2009).

  • Keramati, M., Smittenaar, P., Dolan, R. J. & Dayan, P. Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum. Proc. Natl Acad. Sci. USA 113, 12868–12873 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huys, Q. J. M. et al. Bonsai timber in your head: how the Pavlovian system sculpts goal-directed selections by pruning choice timber. PLoS Comput. Biol. 8, e1002410 (2012).

    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huys, Q. J. M. et al. Interaction of approximate planning methods. Proc. Natl Acad. Sci. USA 112, 3098–3103 (2015).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Callaway, F. et al. Rational use of cognitive sources in human planning. Nat. Hum. Behav. https://doi.org/10.1038/s41562-022-01332-8 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Sezener, C. E., Dezfouli, A. & Keramati, M. Optimizing the depth and the route of potential planning utilizing info values. PLoS Comput. Biol. 15, e1006827 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pezzulo, G., Donnarumma, F., Maisto, D. & Stoianov, I. Planning at choice time and within the background throughout spatial navigation. Curr. Opin. Behav. Sci. 29, 69–76 (2019).

    Google Scholar 

  • Miller, E. Ok. & Cohen, J. D. An integrative idea of prefrontal cortex perform. Ann. Rev. Neurosci. 24, 167–202 (2001).

    CAS 
    PubMed 

    Google Scholar 

  • Shenhav, A., Botvinick, M. M. & Cohen, J. D. The anticipated worth of management: an integrative idea of anterior cingulate cortex perform. Neuron 79, 217–240 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shenhav, A. et al. Towards a rational and mechanistic account of psychological effort. Ann. Rev. Neurosci. 40, 99–124 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Norman, D. A. & Shallice, T. in Consciousness and Self-Regulation (eds Davidson, R. J. et al.) 1–18 (Plenum Press, 1986).

  • Holland, J. H., Holyoak, Ok. J., Nisbett, R. E. & Thagard, P. R. Induction: Processes of Inference, Studying, and Discovery (MIT Press, 1989).

  • Newell, A. & Simon, H. A. Pc science as empirical inquiry: symbols and search. Commun. ACM 19, 113–126 (1976).

    MathSciNet 

    Google Scholar 

  • Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competitors between prefrontal and dorsolateral striatal methods for behavioral management. Nat. Neurosci. 8, 1704–1711 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • Gläscher, J., Daw, N., Dayan, P. & O’Doherty, J. P. States versus rewards: dissociable neural prediction error alerts underlying model-based and model-free reinforcement studying. Neuron 66, 585–595 (2010).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ramkumar, P. et al. Chunking as the results of an effectivity computation trade-off. Nat. Commun. 7, 12176 (2016).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Barsalou, L. W. Advert hoc classes. Mem. Cogn. 11, 211–227 (1983).

    CAS 

    Google Scholar 

  • Simon, H. A. The purposeful equivalence of drawback fixing abilities. Cogn. Psychol. 7, 268–288 (1975).

    Google Scholar 

  • Brooks, R. A. Intelligence with out illustration. Artif. Intell. 47, 139–159 (1991).

    Google Scholar 

  • Puterman, M. L. Markov Determination Processes: Discrete Stochastic Dynamic Programming (John Wiley & Sons, 1994).

  • Bellman, R. Dynamic Programming (Princeton Univ. Press, 1957).

  • Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic interplay between reinforcement studying and a spotlight in multidimensional environments. Neuron 93, 451–463 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hinton, G. E. Coaching merchandise of specialists by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002).

  • Whiteley, L. & Sahani, M. Consideration in a Bayesian framework. Entrance. Hum. Neurosci. 6, 100 (2012).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lieder, F. & Griffiths, T. L. Useful resource-rational evaluation: understanding human cognition because the optimum use of restricted computational sources. Behav. Mind Sci. 43, e1 (2020).

    Google Scholar 

  • Yoo, A. H., Klyszejko, Z., Curtis, C. E. & Ma, W. J. Strategic allocation of working reminiscence useful resource. Sci. Rep. 8, 16162 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Grünwald, P. Mannequin choice based mostly on minimal description size. J. Math. Psychol. 44, 133–152 (2000).

    MathSciNet 
    PubMed 
    MATH 

    Google Scholar 

  • Gabaix, X. A sparsity-based mannequin of bounded rationality. Q. J. Econ. 129, 1661–1710 (2014).

    MATH 

    Google Scholar 

  • Marr, D. Imaginative and prescient: A Computational Investigation into the Human Illustration and Processing of Visible Info (W. H. Freeman, 1982).

  • Anderson, J. R. The Adaptive Character of Thought (Lawrence Erlbaum Associates, 1990).

  • Gershman, S. J. The successor illustration: its computational logic and neural substrates. J. Neurosci. 38, 7193–7200 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Stachenfeld, Ok. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • Tversky, B. & Hemenway, Ok. Objects, components, and classes. J. Exp. Psychol. 113, 169–193 (1984).

    CAS 

    Google Scholar 

  • Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. How one can develop a thoughts: statistics, construction, and abstraction. Science 331, 1279–1285 (2011).

    ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 

  • Nassar, M. R. & Frank, M. J. Taming the beast: extracting generalizable information from computational fashions of cognition. Curr. Opin. Behav. Sci. 11, 49–54 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Sutton, R. S. & Barto, A. G. Reinforcement Studying: An Introduction (MIT Press, 2018).

  • Parr, R. & Russell, S. in Proc. Advances in Neural Info Processing Techniques (eds Jordan, M. I. et al.) 10 (MIT Press, 1997).

  • Virtanen, P. et al. SciPy 1.0: basic algorithms for scientific computing in Python. Nat. Strategies 17, 261–272 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Howard, R. A. Dynamic Programming and Markov Processes (MIT Press, 1960).

  • Barto, A. G., Bradtke, S. J. & Singh, S. P. Studying to behave utilizing real-time dynamic programming. Artif. Intell. 72, 81–138 (1995).

    Google Scholar 

  • Bonet, B. & Geffner, H. Labeled RTDP: bettering the convergence of real-time dynamic programming. In Proc. Worldwide Convention on Planning and Automated Scheduling Vol. 3 (ed. Giunchiglia, E.) 12–21 (AAAI Press, 2003).

  • Hansen, E. A. & Zilberstein, S. LAO: a heuristic search algorithm that finds options with loops. Artif. Intell. 129, 35–62 (2001).

    MathSciNet 
    MATH 

    Google Scholar 

  • Hart, P. E., Nilsson, N. J. & Raphael, B. A proper foundation for the heuristic willpower of minimal value paths. IEEE Trans. Syst. Sci. Cybern. 4, 100–107 (1968).

    Google Scholar 

  • Momennejad, I. et al. The successor illustration in human reinforcement studying. Nat. Hum. Behav. 1, 680–692 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Russek, E. M., Momennejad, I., Botvinick, M. M., Gershman, S. J. & Daw, N. D. Predictive representations can hyperlink model-based reinforcement studying to model-free mechanisms. PLoS Comput. Biol. 13, e1005768 (2017).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Solway, A. et al. Optimum behavioral hierarchy. PLoS Comput. Biol. 10, e1003779 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Shi, J. & Malik, J. Normalized cuts and picture segmentation. IEEE Trans. Sample Anal. Mach. Intell. 22, 888–905 (2000).

    Google Scholar 

  • Gureckis, T. M. et al. psiTurk: an open-source framework for conducting replicable behavioral experiments on-line. Behav. Res. Strategies 48, 829–842 (2016).

    PubMed 

    Google Scholar 

  • De Leeuw, J. R. jsPsych: a JavaScript library for creating behavioral experiments in an online browser. Behav. Res. Strategies 47, 1–12 (2015).

    ADS 
    PubMed 

    Google Scholar 

  • Bates, D., Mächler, M., Bolker, B. & Walker, S. Becoming linear mixed-effects fashions utilizing lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  • The rpy2 Contributors. rpy2 model 3.3.6. (2020); https://rpy2.github.io/

  • Leave a Reply