It is well known that you cannot tickle yourself. Here, we discuss the proposal that such attenuation of self-produced tactile stimulation is due to the sensory predictions made by an internal forward model of the motor system. A forward model predicts the sensory consequences of a movement based on the motor command. When a movement is self-produced, its sensory consequences can be accurately predicted, and this prediction can be used to attenuate the sensory effects of the movement. Studies are reviewed that demonstrate that as the discrepancy between predicted and actual sensory feedback increases during self-produced tactile stimulation there is a concomitant decrease in the level of sensory attenuation and an increase in tickliness. Functional neuroimaging studies have demonstrated that this sensory attenuation might be mediated by somatosensory cortex and anterior cingulate cortex: these areas are activated less by a self-produced tactile stimulus than by the same stimulus when it is externally produced. Furthermore, evidence suggests that the cerebellum might be involved in generating the prediction of the sensory consequences of movement. Finally, recent evidence suggests that this predictive mechanism is abnormal in patients with auditory hallucinations and/or passivity experiences.

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Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice.