Neurochemical and behavioral dissections of decision-making in a rodent multistage task

Stephanie M. Groman, Bart Massi, Samuel R. Mathias, Daniel W. Curry, Daeyeol Lee, Jane R. Taylor

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Flexible decision-making in dynamic environments requires both retrospective appraisal of reinforced actions and prospective reasoning about the consequences of actions. These complementary reinforcement-learning systems can be characterized computationally with model-free and model-based algorithms, but how these processes interact at a neurobehavioral level in normal and pathological states is unknown. Here, we developed a translationally analogous multistage decision-making (MSDM) task to independently quantify model-free and model-based behavioral mechanisms in rats. We provide the first direct evidence that male rats, similar to humans, use both model-free and model-based learning when making value-based choices in the MSDM task and provide novel analytic approaches for independently quantifying these reinforcement-learning strategies. Furthermore, we report that ex vivo dopamine tone in the ventral striatum and orbitofrontal cortex correlate with model-based, but not model-free, strategies, indicating that the biological mechanisms mediating decision-making in the multistage task are conserved in rats and humans. This new multistage task provides a unique behavioral platform for conducting systems-level analyses of decision-making in normal and pathological states. Significance Statement Decision-making is influenced by both a retrospective “model-free” system and a prospective “model-based” system in humans, but the biobehavioral mechanisms mediating these learning systems in normal and disease states are unknown. Here, we describe a translationally analogous multistage decision-making task to provide abehavioral platform for conducting neuroscience studies of decision-making in rats. We provide the first evidence that choice behavior in rats is influenced by model-free and model-based systems and demonstrate that model-based, but not model-free, learning is associated with corticostriatal dopamine tone. This novel behavioral paradigm has the potential to yield critical insights into the mechanisms mediating decision-making alterations in mental disorders.

Original languageEnglish (US)
Pages (from-to)295-306
Number of pages12
JournalJournal of Neuroscience
Issue number2
StatePublished - Jan 9 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Institute on Drug Abuse (Public Health Service Grants DA041480 and DA043443), a NARSAD Young Investigator Award from the Brain and Behavior Research Foundation, and funding provided by the State of Connecticut. The authors declare no competing financial interests.


  • Computational psychiatry
  • Decision-making
  • Dopamine
  • Model-free and model-based reinforcement learning

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