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Tuning in to art: A predictive processing account of negative emotion in art

Published online by Cambridge University Press:  29 November 2017

Sander Van de Cruys
Affiliation:
Brain and Cognition Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven BE-3000, Belgium. sander.vandecruys@kuleuven.bejohan.wagemans@kuleuven.bewww.sandervandecruys.be
Rebecca Chamberlain
Affiliation:
Brain and Cognition Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven BE-3000, Belgium. sander.vandecruys@kuleuven.bejohan.wagemans@kuleuven.bewww.sandervandecruys.be Department of Psychology, Goldsmiths, University of London, London SE14 6NW, United Kingdom. r.chamberlain@gold.ac.ukwww.gestaltrevision.be
Johan Wagemans
Affiliation:
Brain and Cognition Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven BE-3000, Belgium. sander.vandecruys@kuleuven.bejohan.wagemans@kuleuven.bewww.sandervandecruys.be

Abstract

We use the example of art-derived solace to discuss a broader mechanism by which negative affect is instrumental in creating positive appreciation of artworks. Based on the theory of predictive processing, we argue that increasing attunement or reduction of prediction errors, which implies increasing validation of the agents (models), is experienced as positive, even if the artwork's content is negative.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

We commend Menninghaus et al. for their thorough and insightful treatment of negative affect in aesthetic experience. As they illustrate, negative affect is ubiquitous and nonaccidental in art, so the question of why people seek out negative affect in art is a fascinating and important one. Although Menninghaus et al. highlight several possible explanations, we think they leave underexposed the central role of consolation in art appreciation.

Very often, people read novels to find solace concerning everyday or existential uncertainties and anxieties. They watch films and television series to remind themselves they are not alone in their failings, inner conflicts, or even idiosyncratic pleasures. Even if no concrete solutions for sorrows are offered in the artwork, the mere discovery of similar affective dynamics validates the existence of the perceivers and the cognitive/affective schemata with which they experience (and navigate through) the world. Although the content of these dynamics is often negative, the process is one of increasing attunement between artwork and perceiver.

On the face of it, this could be considered a kind of empathic affect (Jackendoff & Lerdahl Reference Jackendoff and Lerdahl2006) where the perceiver empathizes with the creator of the artwork or the performer of the dance or music act. But the point is not that we have compassion with themes or people in the artwork and subsequently, as a kind of meta-emotion, feel good about this prosocial response (what Menninghaus et al. do discuss but dismiss). More often, the “empathic” reflection is one of more direct attunement with the artwork, for example, with the structural aspects of the music that convey sadness, joy, and so forth, by way of modulation of tone, tempo, timbre, melodic contour, and so forth.

The “language” by which the musician or painter's technique allows us to “empathize” with the work directly is insufficiently understood, but probably relates to natural expressive speech or expressive behavior (Freedberg & Gallese Reference Freedberg and Gallese2007; Jackendoff & Lerdahl Reference Jackendoff and Lerdahl2006). What is clear is that we have implicit generative models of how particular artistic outputs are created (as for any other perceptual inputs [e.g., Kersten et al. Reference Kersten, Mamassian and Yuille2004]). Perceiving those outputs is inferring their hidden causes, which includes not only specific patterns of motor behavior (e.g., strumming a guitar, “stroking” a canvas), but also the (hierarchically) deeper causes, in terms of conceptual and emotional/motivational states. Meanwhile, the content of the artwork can set the context for a better alignment of the models of the perceiver and those implied in the work. The commonality in generative models that allows for synchronization of state dynamics has recently been used to study mutual understanding in social interactions, where a shared conceptual space enables agents to predict each other (Friston & Frith Reference Friston and Frith2015). However, the analysis here suggests this form of alignment is not limited to explicit social interactions.

The exceptional thing about art and music is that they often invoke affective narratives that are rarely explicitly articulated, but nonetheless find resonance in the affective models of perceivers. Here, art has the potential to resolve inner conflicts or ambiguities through attunement, by confirming models that allow seemingly contradicting states to coexist or by validating one model over the other.

But good artworks will also (temporarily) obstruct alignment, and this obstruction is often conducive to appreciation. To understand this, we can turn to predictive processing as an account that formalizes the mental mechanics of uncertainty reduction. This approach holds that our brain is continually attempting to minimize the prediction errors that reflect the mismatch between its hierarchical models (that we use to predict/interpret inputs) and the actual inputs from our senses (Clark Reference Clark2013; Friston Reference Friston2010). We previously hypothesized that the transition from a state of higher uncertainty to a state of lower uncertainty (i.e., an active reduction of prediction errors) is experienced as pleasurable, and that this may help explain our aesthetic experiences, even for “static” artworks (Van de Cruys & Wagemans Reference Van de Cruys and Wagemans2011). Hence, the increasing attunement of models and world (perceptual inputs) yields positive affect (again, even if the content of the models/inputs is about negative events). So, with respect to emotional effects, the dynamics of attunement are key. If violations of expectancies are the norm in art, this is because it allows viewers to make greater progress in reducing those “errors” on conceptual or perceptual levels.

If we extend the view above and characterize negative affect as a state of rapidly increasing uncertainty (i.e., the opposite of positive affect), then negative affect is indeed regularly present in our experience with art – in a formal way, not only in terms of negative content. The question of why we seek out negative affect in art then becomes very closely related to the much debated one about why we sometimes seek out uncertainty. One way out, within a predictive processing framework, is to argue that we are intrinsically motivated to seek predictive progress (Gottlieb et al. Reference Gottlieb, Oudeyer, Lopes and Baranes2013), which necessarily urges us to venture out of predictable zones. However, we do not do this haphazardly; we seem to contextually learn (meta)predictions on prediction error reduction rates (Van de Cruys Reference Van de Cruys, Metzinger and Wiese2017). Those expectations on the reducibility of errors can function as implicit appraisals of coping potential that are so important in our engagement with art (Silvia Reference Silvia2005b).

In sum, although we started out describing the important role of solace in art, we came to see it as just an example of broader mechanism by which negative affect is instrumental in creating positive appreciation of artworks. The presented account may serve as an explanation for the “mood congruency effect” (Derryberry Reference Derryberry1988), in which perceivers seek out artworks that correspond to their current or desired mood state (e.g., sad music on a funeral). The view also has some affinity with the processing fluency account, but it adds a process account and emphasizes the temporal dynamics in fluency (cf. relative fluency [Wänke & Hansen Reference Wänke and Hansen2015]). We end up with a view in which “being moved by” art is an almost literal consequence of moving through the prediction error gradients.

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