Neurodynamics of Relational Aesthetic Engagement in Creative Arts Therapies

Review of General Psychology, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Aesthetic experiences, emerging saliently in the arts, play a pivotal role transformative learning and creative processes that elicit physiological, affective, cognitive responses associated with mental health indices. Interactions between subjects aesthetic objects (e.g., visual artwork, music, moving bodies) often entail elements of surprise uncertainty drive inference hidden causes subject’s internal external environment. These generate dynamics align action-oriented Predictive Processing framework brain function. Creative Arts Therapies (CATs) harness these by cultivating relational engagement using arts modalities, prompting affective processing. In this manuscript, we offer review conceptual analysis recent empirical findings theoretical premises underpin experiences their relation to psychotherapeutic use broad spectrum populations conditions. We present neuroscience-based approach intra- inter-personal integrating therapeutic change factors externalization-concretization, embodiment, symbolization functional network configurations, interpersonal brain-to-brain coupling, support predictive processing, learning, creativity. Present future interdisciplinary collaborations are underlined elucidate neurodynamic mechanisms driving psychological transformations, bridging neuroaesthetics CATs.

Language: Английский

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The Pleasurable Urge to Move to Music Through the Lens of Learning Progress

Journal of Cognition, Journal Year: 2023, Volume and Issue: 6(1)

Published: Jan. 1, 2023

Interacting with music is a uniquely pleasurable activity that ubiquitous across human cultures. Current theories suggest prominent driver of musical pleasure responses the violation and confirmation temporal predictions. For example, urge to move (PLUMM), which associated broader concept groove, higher for moderately complex rhythms compared simple rhythms. This inverted U-shaped relation between PLUMM rhythmic complexity thought result from balance predictability uncertainty. That is, lead strongly weighted prediction errors elicit an reinforce predictive model (i.e., meter). However, details these processes how they bring about positive affective are currently underspecified. We propose intrinsic motivation learning progress drives informs humans choose listen to, dance create. Here, reflects rate error minimization over time. Accordingly, reducible signal potential progress, producing pleasurable, curious state characterized by mobilization attentional memory resources. discuss this hypothesis in context current psychological neuroscientific research on PLUMM. theoretical focusing roles dopamine norepinephrine within feedback loop linking prediction-based learning, curiosity, memory. perspective provides testable predictions will motivate future further illuminate fundamental predictions, movement, reward.

Language: Английский

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The perceptual primacy of feeling: Affectless visual machines explain a majority of variance in human visually evoked affect

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(4)

Published: Jan. 23, 2025

Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive in absence of active physiology, deliberative thought, or any form feedback resembles human affective experience offer tools demystify relationship between and feeling, assess how much visually evoked experiences may be a straightforward function representation learning over natural image statistics. In this work, we deploy diverse sample 180 state-of-the-art deep neural network models trained only on canonical computer tasks predict ratings arousal, valence, beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets. Importantly, use features these without additional learning, linearly decoding responses activity same way neuroscientists decode information recordings. Aggregate analysis our survey, demonstrates predictions purely perceptual explain majority explainable variance average alike. Finer-grained within survey (e.g. comparisons shallower deeper layers, randomly initialized, category-supervised, self-supervised models) point rich, preconceptual abstraction (learned diversity visual experience) as key driver predictions. Taken together, results provide further computational evidence an information-processing account affect linked directly efficient statistics, hint locus aesthetic valuation immediately proximate perception.

Language: Английский

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Modelling individual aesthetic judgements over time

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2023, Volume and Issue: 379(1895)

Published: Dec. 18, 2023

Listening to music, watching a sunset-many sensory experiences are valuable us, degree that differs significantly between individuals, and within an individual over time. We have theorized (Brielmann & Dayan 2022

Language: Английский

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A Fisher Information Theory of Aesthetic Preference for Complexity

Entropy, Journal Year: 2024, Volume and Issue: 26(11), P. 901 - 901

Published: Oct. 24, 2024

When evaluating sensory stimuli, people tend to prefer those with not too little or much complexity. A recent theoretical proposal for this phenomenon is that preference has a direct link the Observed Fisher Information stimulus carries about environment. To make theory complete, one must specify model brain complexities in world. Here, we develop by first obtaining distributions of three indices complexity measured as normalized Shannon Entropy real-world images from seven environments. We then search parametric accounts these distributions. Finally, measure each image parameters model. The results show few exceptions, are unimodal, have negative skewness, and leptokurtotic. Moreover, sign magnitude skewness varies systematically location mode. After investigating tens models distributions, Logit-Losev function, generalization hyperbolic-secant distribution, fits them well. shows inverted-U-shape behavior preference. discuss ways test our Fisher-Information theory.

Language: Английский

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Aesthetics and predictive processing: grounds and prospects of a fruitful encounter

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2023, Volume and Issue: 379(1895)

Published: Dec. 18, 2023

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in arts aesthetics from variety perspectives cognitive scientists studying mind brain within predictive processing (PP) framework. This so far proven fruitful for both sides: while PP is increasingly adopted as framework understanding aesthetic phenomena, aesthetics, examined under lens PP, are starting to be seen important windows into our mental functioning. The result vast fast-growing research programme that promises deliver insights encounters well wide range psychological phenomena general interest. Here, we present this developing programme, describing its grounds highlighting prospects. We start by clarifying how study picture functioning (§1). then go on outline prospects encounter fields involved: philosophy history art (§2), psychology neuroaesthetics (§3) neuroscience more generally (§4). upshot an ambitious but well-defined which science can partner up illuminate crucial aspects human mind. article part theme issue 'Art, processing: theoretical empirical perspectives'.

Language: Английский

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A decision-theoretic model of multistability: perceptual switches as internal actions

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Abstract Perceptual multistability has been studied for centuries using a diverse collection of approaches. Insights derived from this phenomenon range core principles information processing, such as perceptual inference, to high-level concerns, visual awareness. The dominant computational explanations are based on the Helmholtzian view perception inverse inference. However, these approaches struggle account crucial role played by value, e.g., with percepts paired reward dominating longer periods than unpaired ones. In study, we formulate in terms dynamic, value-based, choice, employing formalism partially observable Markov decision process (POMDP). We use binocular rivalry an example, considering different explicit and implicit sources (and punishment) each percept. resulting values time-dependent influenced novelty form exploration. solution POMDP is optimal policy, show that can replicate explain several characteristics rivalry, ranging classic hallmarks apparently spontaneous random switches approximately gamma-distributed dominance more subtle aspects rich temporal dynamics switching rates. Overall, our decision-theoretic perspective not only accounts wealth unexplained data, but also opens up modern conceptions internal reinforcement learning service understanding phenomena, sensory processing generally.

Language: Английский

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Comparing activation typicality and sparsity in a deep CNN to predict facial beauty

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 4, 2024

Processing fluency, which describes the subjective sensation of ease with information is processed by sensory systems and brain, has become one most popular explanations aesthetic appreciation beauty. Two metrics have recently been proposed to model fluency: sparsity neuronal activation, characterizing extent neurons in brain are unequally activated a stimulus, statistical typicality activations, describing how well encoding stimulus matches reference representation stimuli category it belongs. Using Convolutional Neural Networks (CNNs) as for human visual system, this study compares ability these explain variation facial attractiveness. Our findings show that activations more robust predictor beauty than typicality. Refining single ethnicity or gender does not increase explanatory power However, predict based on different layers CNNs, suggesting they describe neural mechanisms underlying fluency.

Language: Английский

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Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty

Computational Brain & Behavior, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Language: Английский

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