Human complex exploration strategies are enriched by noradrenaline-modulated heuristics
- University College London, United Kingdom
Abstract
An exploration-exploitation trade-off, the arbitration between sampling a lesser-known against a known rich option, is thought to be solved using computationally demanding exploration algorithms. Given known limitations in human cognitive resources, we hypothesised the presence of additional cheaper strategies. We examined for such heuristics in choice behaviour where we show this involves a value-free random exploration, that ignores all prior knowledge, and a novelty exploration that targets novel options alone. In a double-blind, placebo-controlled drug study, assessing contributions of dopamine (400mg amisulpride) and noradrenaline (40mg propranolol), we show that value-free random exploration is attenuated under the influence of propranolol, but not under amisulpride. Our findings demonstrate that humans deploy distinct computationally cheap exploration strategies and where value-free random exploration is under noradrenergic control.
Data availability
All necessary resources are publicly available at: https://github.com/MagDub.
Article and author information
Author details
Funding
Max-Planck-Gesellschaft
- Magda Dubois
Wellcome Sir Hendry Dale Fellowship (211155/Z/18/Z)
- Tobias U Hauser
Jacobs Foundation (2017-1261-04)
- Tobias U Hauser
Wellcome Trust Investigator Award (098362/Z/12/Z)
- Raymond J Dolan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by the UCL research committee (REC No 6218/002) and all subjects provided written informed consent
Copyright
© 2021, Dubois et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Human complex exploration strategies are enriched by noradrenaline-modulated heuristics
eLife 10:e59907.
https://doi.org/10.7554/eLife.59907
