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Article Dans Une Revue NeuroImage Année : 2017

Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning

Anne-Marike Schiffer
  • Fonction : Auteur
Kayla Siletti
  • Fonction : Auteur
Florian Waszak
Nick Yeung
  • Fonction : Auteur

Résumé

In any non-deterministic environment, unexpected events can indicate true changes in the world (and require behavioural adaptation) or reflect chance occurrence (and must be discounted). Adaptive behaviour requires distinguishing these possibilities. We investigated how humans achieve this by integrating high-level information from instruction and experience. In a series of EEG experiments, instructions modulated the perceived informativeness of feedback: Participants performed a novel prob-abilistic reinforcement learning task, receiving instructions about reliability of feedback or volatility of the environment. Importantly, our designs de-confound informativeness from surprise, which typically co-vary. Behavioural results indicate that participants used instructions to adapt their behaviour faster to changes in the environment when instructions indicated that negative feedback was more informative, even if it was simultaneously less surprising. This study is the first to show that neural markers of feedback anticipation (stimulus-preceding negativity) and of feedback processing (feedback-related negativity; FRN) reflect informativeness of unexpected feedback. Meanwhile, changes in P3 amplitude indicated imminent adjustments in behaviour. Collectively, our findings provide new evidence that high-level information interacts with experience-driven learning in a flexible manner, enabling human learners to make informed decisions about whether to persevere or explore new options, a pivotal ability in our complex environment.
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Dates et versions

hal-02384762 , version 1 (03-12-2019)

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Anne-Marike Schiffer, Kayla Siletti, Florian Waszak, Nick Yeung. Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning. NeuroImage, 2017, 146, pp.626-641. ⟨10.1016/j.neuroimage.2016.08.057⟩. ⟨hal-02384762⟩

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