Despite the ubiquity of stochasticity in real-world environments, it remains an open question how people effectively balance the cognitive costs of planning against its potential benefits when faced with stochasticity. To study this, we designed a planning task where participants face one of three forms of stochasticity commonly encountered in the real world: reliability, volatility, and controllability. We find a robust pattern across all three manipulations: as stochasticity increases, people reduce their planning effort as measured by first-choice response times.
To understand the processes underlying this effect, we developed several computational cognitive models to account for participants’ choices. We find that rather than calculating expected values optimally, people choose a simpler strategy, acting as if the world were deterministic, a phenomenon known as determinizing. Consistent with our response time findings, we find that people reduce their effort by decreasing their sensitivity to values with increasing stochasticity, a signature of policy compression.
Our work highlights the often overlooked role of stochasticity in human planning and its impact on planning strategy and effort. Moreover, it reveals the limitations of studying stochasticity solely through single-shot decisions. We are grateful to Marcelo Mattar, Todd Gureckis, and Cristina Savin for their guidance and support throughout the development of this project, including their feedback at critical stages of experiment and model design.
This work was funded by award number 2341710 from the National Science Foundation. J.L. was funded by the Training Program in Computational Neuroscience (award number T90DA059110 from the National Institutes of Health). Center for Neural Science, New York University, New York, NY, USA Jordan Lei, Jeroen Olieslagers, Daisy Xinlei Lin & Wei Ji Ma Department of Psychology, New York University, New York, NY, USA The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Lei, J., Olieslagers, J., Arfaei, N. et al.
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