Functional specificity and neural integration in the aesthetic appreciation of artworks with implied motion

Abstract

Although there is growing interest in the neural foundations of aesthetic experience, it remains unclear how particular mental sub-systems (e.g., perceptual, affective, cognitive) are involved in different types of aesthetic judgments. Here we use fMRI to investigate the involvement of different neural networks during aesthetic judgments of visual artworks with implied motion cues. First, a behavioural experiment (N=45) confirmed a preference for paintings with implied motion over static cues. Subsequently, in a pre-registered fMRI experiment (N=27), participants made aesthetic and motion judgments towards paintings representing human bodies in dynamic and static postures. Using functional region-of-interest and Bayesian multilevel modelling approaches, we provide no compelling evidence for unique sensitivity within or between neural systems associated with body perception, motion and affective processing during the aesthetic evaluation of paintings with implied motion. However, we show suggestive evidence that motion and body-selective systems may integrate signals via functional connections with a separate neural network in dorsal parietal cortex, which may act as a relay or integration site. Our findings clarify the roles of basic visual and affective brain circuitry in evaluating a central aesthetic feature -implied motionwhilst also pointing towards promising future research directions, which involve modelling aesthetic preferences as hierarchical interplay between visual and affective circuits and integration processes in frontoparietal cortex.

Investigating the role of working memory resources across aesthetic and non-aesthetic judgements

“…The art stimuli dataset consisted of 80 images of representational paintings depicting either human bodies (40 images) or landscapes (40 images). The stimuli were validated previously across a range of dimensions: familiarity, aesthetic appreciation, implied dynamism, and evocativeness ( Bara, Darda, et al, 2021 ). The images were characterised by a realistic representational style in the 19th–20th century European and American pictorial tradition.…”

“…Avoiding the need for categorical divisions, feature mapping, or dimensional approaches, which have previously been used in social cognition and psychopathology ( Brown & Barlow, 2009 ; Cross & Ramsey, 2021 ; Oosterhof & Todorov, 2008 ), could provide a fruitful alternative perspective in defining “aesthetic” and “non-aesthetic.” According to dimensional perspectives, different stimulus or task features could be more or less aesthetically oriented. For example, the assessment of visual clarity ( Whittlesea et al, 1990 ), implied motion ( Bara, Darda, et al, 2021 ), or symmetry ( Jacobsen & Höfel, 2003 ; Jacobsen et al, 2006 ) could be regarded as less aesthetically oriented than assessing liking, preference, or beauty. Furthermore, this definition means that stimuli, tasks, and contexts that possess fewer aesthetic features are not necessarily devoid of any aesthetic features.…”

“…According to dimensional perspectives, different stimulus or task features could be more or less aesthetically oriented. For example, the assessment of visual clarity (Whittlesea et al, 1990), implied motion (Bara, Darda, et al, 2021), or symmetry (Jacobsen & Höfel, 2003;Jacobsen et al, 2006) could be regarded as less aesthetically oriented than assessing liking, preference, or beauty. Furthermore, this definition means that stimuli, tasks, and contexts that possess fewer aesthetic features are not necessarily devoid of any aesthetic features.…”

Domain‐specific and domain‐general neural network engagement during human–robot interactions

“…To provide further evidence on the strength of the relationship and evidence for the null and alternative hypothesis, we used Bayesian regression models implemented in the BRMS package (Version 2.21.5; Bürkner, 2017) with STAN (Version 2.21.2; Carpenter et al, 2017) For the ROI analysis, we specified the following linear model: value $ dynamics.d * interaction.d + (1 j sub), with value representing the beta estimates extracted for each event for each ROI averaged across networks, and dynamics and type of interaction as fixed effects and a random intercept for participants (sub). To allow for comparison with null hypothesis significance testing (http://talklab.psy.gla.ac.uk/tvw/catpred/; e.g., Bara et al, 2021), deviation coding was used with dynamics and type of interaction coded as .5 (listening and HHI) and À.5 (speaking and HRI). For the functional connectivity analyses, the following model was specified: corz $ run * interaction + (run j sub), with corz representing the Fisher z-transformed Pearson's correlation coefficients between the time courses for all possible combinations of ROIs within or between the network(s) with type of interaction (HHI and HRI) and run (1-4) as fixed effects and a random intercept and slope for participants (run j sub).…”

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

“…For example, Di Dio et al [ 21 ] found that aesthetic evaluations of portrait and landscape paintings involved the perception of motion information. Bara et al [ 22 ] demonstrated that paintings with motion cues were preferred over those with static cues. Researchers have discovered the neural mechanisms underlying motion information processing in aesthetic evaluations using functional magnetic resonance imaging experiments.…”