A neuro-computational account of procrastination behavior

Raphaël Le Bouc () and Mathias Pessiglione
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Raphaël Le Bouc: Sorbonne University, Inserm, CNRS, Pitié-Salpêtrière hospital
Mathias Pessiglione: Sorbonne University, Inserm, CNRS, Pitié-Salpêtrière hospital

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract Humans procrastinate despite being aware of potential adverse consequences. Yet, the neuro-computational mechanisms underlying procrastination remain poorly understood. Here, we use fMRI during intertemporal choice to inform a computational model that predicts procrastination behavior in independent tests. Procrastination is assessed in the laboratory as the preference for performing an effortful task on the next day as opposed to immediately, and at home as the delay taken in returning completed administrative forms. These procrastination behaviors are respectively modeled as unitary and repeated decisions to postpone a task until the next time step, based on a net expected value that integrates reward and effort attributes, both discounted with delay. The key feature that is associated with procrastination behavior across individuals (both in-lab and at-home) is the extent to which the expected effort cost (signaled by the dorsomedial prefrontal cortex) is attenuated by the delay before task completion. Thus, procrastination might stem from a cognitive bias that would make doing a task later (compared to now) appear as much less effortful but not much less rewarding.

Date: 2022
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DOI: 10.1038/s41467-022-33119-w

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