The influence of internal models on feedback-related brain activity
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The influence of internal models on feedback-related brain activity Franz Wurm 1 & Benjamin Ernst 1 & Marco Steinhauser 1 Published online: 18 August 2020 # The Author(s) 2020
Abstract Decision making relies on the interplay between two distinct learning mechanisms, namely habitual model-free learning and goal-directed model-based learning. Recent literature suggests that this interplay is significantly shaped by the environmental structure as represented by an internal model. We employed a modified two-stage but one-decision Markov decision task to investigate how two internal models differing in the predictability of stage transitions influence the neural correlates of feedback processing. Our results demonstrate that fronto-central theta and the feedback-related negativity (FRN), two correlates of reward prediction errors in the medial frontal cortex, are independent of the internal representations of the environmental structure. In contrast, centro-parietal delta and the P3, two correlates possibly reflecting feedback evaluation in working memory, were highly susceptible to the underlying internal model. Model-based analyses of single-trial activity showed a comparable pattern, indicating that while the computation of unsigned reward prediction errors is represented by theta and the FRN irrespective of the internal models, the P3 adapts to the internal representation of an environment. Our findings further substantiate the assumption that the feedback-locked components under investigation reflect distinct mechanisms of feedback processing and that different internal models selectively influence these mechanisms. Keywords Event-related potentials . Feedback processing . Model-free learning . Model-based learning . Reinforcement learning . Time-frequency analysis
Introduction In our everyday life, decision making is usually accompanied by uncertainties. To resolve these uncertainties, a variety of informational cues can guide behavior. Past experience with decision outcomes can act as a valuable and straightforward criterion that indicates whether the decision maker should repeat or switch actions. For instance, if the last meal at a specific restaurant was of poor quality, maybe one should consider changing the restaurant next time. However, the history of past decision outcomes is not the only cue that can guide decision making. For example, internal models based on explicit knowledge, such as reviews on a restaurant`s quality, may serve as a good proxy for costly experience and thus can facilitate the optimization of decision making. It is assumed that these two sources of information—past experience and internal models—improve decision making via two
* Franz Wurm [email protected] 1
Catholic University of Eichstätt-Ingolstadt, Ostenstraße 27, 85072 Eichstätt, Germany
different learning mechanisms called model-free learning and model-based learning (Daw & O’Doherty, 2014; Dayan & Berridge, 2014; O’Doherty, Cockburn, & Pauli, 2017). Despite the long held assumption of computationally dissociable learning mech
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