Predicting instabilities: an embodied perspective on unstable experiences with art and design.

Predictive Processing (PP) theory suggests that perception works to make sensations predictable by updating predictions or seeking new experiences. This paper argues that art perception differs from problem-solving because people engage with contradictory elements without aiming to resolve them. The authors propose that experiences of 'Semantic Instability' (SeIns)—where perception is challenged by multiple possible meanings—can themselves generate aesthetic pleasure and interest. They update an earlier account of SeIns by adopting an embodied and situated view of perception and cognition, and discuss how embodied versions of PP might explain why such unstable but insightful sense-making processes are motivating and enjoyable.

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Abstract

Predictive Processing (PP) provides a theoretical framework that describes perception as a process attempting to increase the predictability of stimulations by updating predictions or exploring new sensations. Moreover, perception and action are assumed to be closely linked within this process. While organisms seem to strive for predictability, we sometimes expose ourselves to objects and situations that challenge sense-making-such conditions often break perceptual habits or offer multiple possible meanings. This paper updates a previous qualification of these experiences of 'Semantic Instability' (SeIns) by following an embodied and situated understanding of perception and cognition. We suggest that art perception essentially differs from problem-solving as in engaging with art, we typically integrate contradictory elements dynamically and without the ultimate goal of resolving the contradictions-on the contrary, SeIns itself can generate aesthetic hedonics and interest. We discuss how current embodied accounts of PP might help understand what motivates such unstable yet insightful and pleasurable nonlinear sense-making processes. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.