Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Distinct hypothalamic control of same- and opposite-sex mounting behaviour in mice

Animal behaviours that are superficially similar can express different intents in different contexts, but how this flexibility is achieved at the level of neural circuits is not understood. For example, males of many species can exhibit mounting behaviour towards same- or opposite-sex conspecifics, but it is unclear whether the intent and neural encoding of these behaviours are similar or different. Here we show that female- and male-directed mounting in male laboratory mice are distinguishable by the presence or absence of ultrasonic vocalizations (USVs), respectively. These and additional behavioural data suggest that most male-directed mounting is aggressive, although in rare cases it can be sexual. We investigated whether USV⁺ and USV⁻ mounting use the same or distinct hypothalamic neural substrates. Micro-endoscopic imaging of neurons positive for oestrogen receptor 1 (ESR1) in either the medial preoptic area (MPOA) or the ventromedial hypothalamus, ventrolateral subdivision (VMHvl) revealed distinct patterns of neuronal activity during USV⁺ and USV⁻ mounting, and the type of mounting could be decoded from population activity in either region. Intersectional optogenetic stimulation of MPOA neurons that express ESR1 and vesicular GABA transporter (VGAT) (MPOA^(ESR1∩VGAT) neurons) robustly promoted USV⁺ mounting, and converted male-directed attack to mounting with USVs. By contrast, stimulation of VMHvl neurons that express ESR1 (VMHvl^(ESR1) neurons) promoted USV⁻ mounting, and inhibited the USVs evoked by female urine. Terminal stimulation experiments suggest that these complementary inhibitory effects are mediated by reciprocal projections between the MPOA and VMHvl. Together, these data identify a hypothalamic subpopulation that is genetically enriched for neurons that causally induce a male reproductive behavioural state, and indicate that reproductive and aggressive states are represented by distinct population codes distributed between MPOA^(ESR1) and VMHvl^(ESR1) neurons, respectively. Thus, similar behaviours that express different internal states are encoded by distinct hypothalamic neuronal populations