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Human inference reflects a normative balance of complexity and accuracy

Nature Human Behaviour volume 6, pages 1153–1168 (2022)Cite this article

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Abstract

We must often infer latent properties of the world from noisy and changing observations. Complex, probabilistic approaches to this challenge such as Bayesian inference are accurate but cognitively demanding, relying on extensive working memory and adaptive processing. Simple heuristics are easy to implement but may be less accurate. What is the appropriate balance between complexity and accuracy? Here we model a hierarchy of strategies of variable complexity and find a power law of diminishing returns: increasing complexity gives progressively smaller gains in accuracy. The rate of diminishing returns depends systematically on the statistical uncertainty in the world, such that complex strategies do not provide substantial benefits over simple ones when uncertainty is either too high or too low. In between, there is a complexity dividend. In two psychophysical experiments, we confirm specific model predictions about how working memory and adaptivity should be modulated by uncertainty.

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Fig. 1: A hierarchy of cognitive functions maps to a hierarchy of inference strategies.
Fig. 2: Gaussian change-point processes.
Fig. 3: Adaptive models reduce to calibrated simpler strategies when variability is low or high.
Fig. 4: Diminishing returns from increasing complexity.
Fig. 5: Simple inference strategies are usually sufficient.
Fig. 6: Optimal cognitive engagement.
Fig. 7: Human participants switch between simple and complex strategies as predicted by the theory in the Gaussian estimation task.
Fig. 8: Human participants switch between simple and complex strategies as indicated by the theory in the Bernoulli prediction task.

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Data availability

The experimental data used in Figs. 7 and 8 and Supplementary Figs. 6–10 are available on GitHub under the open-source license GNU-GPL-v3: https://github.com/gaiat/Modeling-Human-Inference. The data were collected using psiTurk (v.2.3.0)66 (http://psiturk.org/, https://github.com/NYUCCL/psiTurk), jsPsych (v.6.0)67 (https://www.jspsych.org/, https://github.com/jspsych/jsPsych/), chart.js (v.2.8.0) (https://www.chartjs.org/, https://github.com/chartjs/Chart.js) and papaparse.js (v.5.0) (https://www.papaparse.com/, https://github.com/mholt/PapaParse).

Code availability

The codes are available on GitHub under the open-source license GNU-GPL-v3: https://github.com/gaiat/Modeling-Human-Inference.

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Acknowledgements

We thank A. Filipowicz for sharing the codes to run the psychophysical experiment for the Bernoulli task. We also thank K. Krishnamurthy and E. Piasini for interesting discussions, and A. Cavagna and A. Ingrosso for pointing out a possible connection between one of our results and spin-glass systems. G.T. was supported by the Swartz Foundation (award no. 575556) and the Computational Neuroscience Initiative of the University of Pennsylvania, and is currently supported by Washington University in St. Louis. V.B. and J.I.G. are supported in part by NIH BRAIN Initiative grant no. R01EB026945. J.I.G. is also supported by grant nos R01 MH115557 and NSF-NCS 1533623. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors jointly supervised this work: Vijay Balasubramanian, Joshua I. Gold.

Authors and Affiliations

  1. Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA

    Gaia Tavoni

  2. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    Takahiro Doi

  3. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA

    Chris Pizzica, Vijay Balasubramanian & Joshua I. Gold

  4. Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA

    Vijay Balasubramanian

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  1. Gaia Tavoni
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  2. Takahiro Doi
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Contributions

G.T., V.B. and J.I.G. developed the ideas and wrote the paper. All the authors designed the psychophysics experiments. T.D. and C.P. performed the experiments. G.T. developed the theory and analysed the data.

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Correspondence to Gaia Tavoni.

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Tavoni, G., Doi, T., Pizzica, C. et al. Human inference reflects a normative balance of complexity and accuracy. Nat Hum Behav 6, 1153–1168 (2022). https://doi.org/10.1038/s41562-022-01357-z

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