What is agentic about the Spatial Agency Bias? How pragmatic relevance contributes to the spatial representations of actions

The mental imagination of (social) actions has been shown to follow a left‐to‐right trajectory, with the thematic agent associated with the left position (Spatial Agency Bias, Suitner & Maass in Advances in Experimental Social Psychology, 53, 2016, p. 245–301). For example, individuals asked to choose a picture that visualizes the sentence “Tom kicks George” tend to choose an image where the agent, Tom, is positioned on the left‐hand side rather than on the right‐hand side. However, as an alternative to the thematic role of the agent, such findings may reflect a mental representation following pragmatic relevance. Specifically, a pragmatic perspective holds that word order and syntactic functions are strategic devices to communicate that the element is important for the sentence. Thus, positioning in the described picture‐matching task may actually reflect the agent's pragmatic relevance instead of agency per se. As a test, we vary whether sentences are written in the active versus the passive voice. Results from five studies indicate that the passive voice results in the tendency to place the agent on the right‐hand side rather than on the left‐hand side of a picture. Instead, the acted‐upon person is positioned on the left‐hand side of a picture. A sixth experiment reveals that for the passive voice, the agent is still seen as more agentic than the receiver, but is considered less relevant. The findings are congruent with the proposed pragmatic relevance account. Implications for the Spatial Agency Bias as well as for building mental representations in general are discussed.     

Computational models of the “active self” and its disturbances in schizophrenia

The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the “active self”. We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.