Abstract
Despite the fact that the role of learning is recognised in empirical and theoretical work on sense of agency (SoA), the nature of this learning has, rather surprisingly, received little attention. In the present study we consider the contribution of associative mechanisms to SoA. SoA can be measured quantitatively as a temporal linkage between voluntary actions and their external effects. Using an outcome blocking procedure, it was shown that training action–outcome associations under conditions of increased surprise augmented this temporal linkage. Moreover, these effects of surprise were correlated with schizotypy scores, suggesting that individual differences in higher level experiences are related to associative learning and to its impact on SoA. These results are discussed in terms of models of SoA, and our understanding of disrupted SoA in certain disorders.
Highlights
The feeling of producing events through one’s own intentional behaviour is referred to as ‘Sense of Agency’ (SoA), and has been the focus of much research
Using an outcome blocking procedure we were able to show that training with more surprising outcomes augmented binding at test, indicative on an increased sense of agency (SoA) This result confirms the importance of learning in the context of SoA
It suggests that associative mechanisms may be involved
Summary
The feeling of producing events through one’s own intentional behaviour is referred to as ‘Sense of Agency’ (SoA), and has been the focus of much research. Various findings point towards the importance of learning in the context of SoA. Pharmacological manipulations suggest that SoA is dependent on neural circuits implicated in instrumental learning (Moore et al 2010). Certain theoretical models of SoA point towards the importance of learning. It has been suggested that SoA is informed by sensorimotor predictions generated as part of the normal system of motor control (Blakemore, Wolpert, & Frith, 2002; Frith, Blakemore, & Wolpert, 2000). In light of the apparent importance of learning for SoA it is surprising that the precise mechanisms of that learning have received little attention. This is something the current study wished to address
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