Investigating cognitive placebo effects in reinforcement learning:A computational modelling approach

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Placebos and nocebos are physiologically inert substances (e.g. pharmaceuticals) or simulated interventions (e.g. medical devices), which produce complex psychobiological responses in the participants despite the fact that they do not have any direct therapeutic effects. Whereas the term "placebo" describes positive effects (i.e., improvement in symptoms), "nocebo" relates to any negative effects (i.e., worsening of symptoms) of a simulated treatment. So far, the fundamental paradigms of placebo/nocebo effects have been primarily confined to the pain or motor domains and our knowledge about cognitive placebo effects remains poorly understood. In this research fellowship, we will ask the questions (1) whether verbal suggestions in conjunction with active sham protocols of transcranial direct current stimulation (tDCS) can induce cognitive placebo effects in different forms of reward learning and (2) characterize the role of uncertainty-based verbal suggestions in inducing cognitive placebo effects. To address these questions, we will focus on reward learning by using a computational modeling approach called reinforcement learning. We will concentrate on two forms of control systems of reinforcement learning that are called model-free (MF) and model-based (MB) systems. In order to dissociate between the MF and MB control systems, we will employ a well-characterized sequential two-choice Markov decision task. To induce cognitive placebo effects, we will use active sham protocols of tDCS in conjunctions with the systematic manipulation of uncertainty about treatment efficacy by employing verbal suggestions. To better understand the relationship between uncertainty about treatment efficacy and placebo response, participant will be randomly allocated into one control and three placebo manipulation groups, namely low-, mid- and high-uncertainty groups. The present research fellowship will contribute to the understanding how cognitive placebo effects are generated and maintained, which is a fundamental question in basic neuroscience research and it has important implications in clinical practice. Furthermore, this research fellowship will greatly extend my expertise in Bayesian statistical analysis and in computational modeling of reinforcement learning.