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Tonic exploration governs both flexibility and lapses

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  • R Becket Ebitz
  • Brianna J Sleezer
  • Hank P Jedema
  • Charles W Bradberry
  • Benjamin Y Hayden

Abstract

In many cognitive tasks, lapses (spontaneous errors) are tacitly dismissed as the result of nuisance processes like sensorimotor noise, fatigue, or disengagement. However, some lapses could also be caused by exploratory noise: randomness in behavior that facilitates learning in changing environments. If so, then strategic processes would need only up-regulate (rather than generate) exploration to adapt to a changing environment. This view predicts that more frequent lapses should be associated with greater flexibility because these behaviors share a common cause. Here, we report that when rhesus macaques performed a set-shifting task, lapse rates were negatively correlated with perseverative error frequency across sessions, consistent with a common basis in exploration. The results could not be explained by local failures to learn. Furthermore, chronic exposure to cocaine, which is known to impair cognitive flexibility, did increase perseverative errors, but, surprisingly, also improved overall set-shifting task performance by reducing lapse rates. We reconcile these results with a state-switching model in which cocaine decreases exploration by deepening attractor basins corresponding to rule states. These results support the idea that exploratory noise contributes to lapses, affecting rule-based decision-making even when it has no strategic value, and suggest that one key mechanism for regulating exploration may be the depth of rule states.Author summary: Why do we make mistakes? We seem to have the capacity to identify the best course of action, but we do not always choose it. Here, we report that at least some mistakes are due to exploration—a type of decision-making that is focused on discovery and learning, rather than on choosing the best option. This is surprising because many views of exploration assume that exploration only happens phasically—when the circumstances suggest that you should abandon your previous course of action and make a new plan. However, here, we find evidence that exploration drives decisions to change your behavior both when change is helpful and when it is a mistake. More work is needed to understand why we explore tonically, but it is possible that tonic exploration may been so useful over evolutionary time that our brains evolved to continue to explore today, even when it has no strategic benefit in the moment. For example, a tonic algorithm for exploration could reduce the effort required to make decisions or prepare us to take advantage of unexpected opportunities.

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  • R Becket Ebitz & Brianna J Sleezer & Hank P Jedema & Charles W Bradberry & Benjamin Y Hayden, 2019. "Tonic exploration governs both flexibility and lapses," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-37, November.
    • Handle: RePEc:plo:pcbi00:1007475
    DOI: 10.1371/journal.pcbi.1007475
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    as
    1. Shiva Farashahi & Katherine Rowe & Zohra Aslami & Daeyeol Lee & Alireza Soltani, 2017. "Feature-based learning improves adaptability without compromising precision," Nature Communications, Nature, vol. 8(1), pages 1-16, December.
    2. Trevor W. Robbins & Barry J. Everitt, 1999. "Drug addiction: bad habits add up," Nature, Nature, vol. 398(6728), pages 567-570, April.
    3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    4. Jonathan D. Wallis & Kathleen C. Anderson & Earl K. Miller, 2001. "Single neurons in prefrontal cortex encode abstract rules," Nature, Nature, vol. 411(6840), pages 953-956, June.
    5. Nathaniel D. Daw & John P. O'Doherty & Peter Dayan & Ben Seymour & Raymond J. Dolan, 2006. "Cortical substrates for exploratory decisions in humans," Nature, Nature, vol. 441(7095), pages 876-879, June.
    6. Bruno B Averbeck, 2015. "Theory of Choice in Bandit, Information Sampling and Foraging Tasks," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-28, March.
    7. Nuo Li & Kayvon Daie & Karel Svoboda & Shaul Druckmann, 2016. "Robust neuronal dynamics in premotor cortex during motor planning," Nature, Nature, vol. 532(7600), pages 459-464, April.
    8. Germain Lefebvre & Maël Lebreton & Florent Meyniel & Sacha Bourgeois-Gironde & Stefano Palminteri, 2017. "Behavioural and neural characterization of optimistic reinforcement learning," Nature Human Behaviour, Nature, vol. 1(4), pages 1-9, April.
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    1. Katarzyna Jurewicz & Brianna J. Sleezer & Priyanka S. Mehta & Benjamin Y. Hayden & R. Becket Ebitz, 2024. "Irrational choices via a curvilinear representational geometry for value," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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