Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Veissière, Samuel P. L.
Constant, Axel
Ramstead, Maxwell J. D.
Friston, Karl J.
and
Kirmayer, Laurence J.
2020.
TTOM in action: Refining the variational approach to cognition and culture.
Behavioral and Brain Sciences,
Vol. 43,
Issue. ,
Vaisvaser, Sharon
2021.
The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies.
Frontiers in Psychology,
Vol. 12,
Issue. ,
Felisberti, Fatima M.
2022.
Experiences of Ugliness in Nature and Urban environments.
Empirical Studies of the Arts,
Vol. 40,
Issue. 2,
p.
192.
Spee, Blanca T. M.
Mikuni, Jan
Leder, Helmut
Scharnowski, Frank
Pelowski, Matthew
and
Steyrl, David
2023.
Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings.
Scientific Reports,
Vol. 13,
Issue. 1,
Van de Cruys, Sander
Frascaroli, Jacopo
and
Friston, Karl
2024.
Order and change in art: towards an active inference account of aesthetic experience.
Philosophical Transactions of the Royal Society B: Biological Sciences,
Vol. 379,
Issue. 1895,
Frascaroli, Jacopo
Leder, Helmut
Brattico, Elvira
and
Van de Cruys, Sander
2024.
Aesthetics and predictive processing: grounds and prospects of a fruitful encounter.
Philosophical Transactions of the Royal Society B: Biological Sciences,
Vol. 379,
Issue. 1895,
Servajean, Philippe
and
Wiese, Wanja
2024.
Processing Fluency and Predictive Processing: How the Predictive Mind Becomes Aware of its Cognitive Limitations.
Topics in Cognitive Science,
Pepperell, Robert
2024.
Being alive to the world: an artist's perspective on predictive processing.
Philosophical Transactions of the Royal Society B: Biological Sciences,
Vol. 379,
Issue. 1895,
Spee, Blanca T. M.
Arato, Jozsef
Mikuni, Jan
Tran, Ulrich S.
Pelowski, Matthew
and
Leder, Helmut
2024.
The dynamics of experiencing Gestalt and Aha in cubist art: pupil responses and art evaluations show a complex interplay of task, stimuli content, and time course.
Frontiers in Psychology,
Vol. 15,
Issue. ,
Vaisvaser, Sharon
King, Juliet L.
Orkibi, Hod
and
Aleem, Hassan
2024.
Neurodynamics of Relational Aesthetic Engagement in Creative Arts Therapies.
Review of General Psychology,
Vol. 28,
Issue. 3,
p.
203.
Spee, Blanca T. M.
Leder, Helmut
Mikuni, Jan
Scharnowski, Frank
Pelowski, Matthew
Steyrl, David
and
Vukadinovic, Maja
2024.
Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes.
PLOS ONE,
Vol. 19,
Issue. 9,
p.
e0304285.
Tiihonen, Marianne
Haumann, Niels Trusbak
Shtyrov, Yury
Vuust, Peter
Jacobsen, Thomas
and
Brattico, Elvira
2024.
The impact of crossmodal predictions on the neural processing of aesthetic stimuli.
Philosophical Transactions of the Royal Society B: Biological Sciences,
Vol. 379,
Issue. 1895,
Venkatesan, Tara
Attie‐Picker, Mario
Newman, George E.
and
Knobe, Joshua
2025.
Sad Art Gives Voice to Our Own Sadness.
Cognitive Science,
Vol. 49,
Issue. 1,
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.