Neural mechanisms underlying the hierarchical construction of perceived aesthetic value.

Little is known about how the brain computes the perceived aesthetic value of complex stimuli such as visual art. Here, we used computational methods in combination with functional neuroimaging to provide evidence that the aesthetic value of a visual stimulus is computed in a hierarchical manner via a weighted integration over both low and high level stimulus features contained in early and late visual cortex, extending into parietal and lateral prefrontal cortices. Feature representations in parietal and lateral prefrontal cortex may in turn be utilized to produce an overall aesthetic value in the medial prefrontal cortex. Such brain-wide computations are not only consistent with a feature-based mechanism for value construction, but also resemble computations performed by a deep convolutional neural network. Our findings thus shed light on the existence of a general neurocomputational mechanism for rapidly and flexibly producing value judgements across an array of complex novel stimuli and situations.

Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features.

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low- and high-level features of the image. Subjective value ratings could be predicted not only within but also across individuals, using a regression model with a common set of interpretable features. We also show that the features predicting aesthetic preference can emerge hierarchically within a deep convolutional neural network trained only for object recognition. Our findings suggest that human preferences for art can be explained at least in part as a systematic integration over the underlying visual features of an image.

Actin-binding protein ABP140 is a methyltransferase for 3-methylcytidine at position 32 of tRNAs in Saccharomyces cerevisiae.

Transfer RNAs contain various modified nucleotides that are introduced enzymatically at the post-transcriptional level. In Saccharomyces cerevisiae, 3-methylcytidine (m³C) is found at position 32 of the tRNAs for Thr and Ser. We used a systematic reverse genetic approach combined with mass spectrometry (ribonucleome analysis), and identified the actin-binding protein ABP140 as the protein responsible for m³C formation in both tRNA(Thr1) and tRNA(Ser1). ABP140 consists of an N-terminal actin-binding sequence and a C-terminal S-adenosylmethionine (Ado-Met) binding motif. Deletion of the actin-binding sequence in ABP140 did not affect m³C formation, indicating that subcellular localization of ABP140 to actin filaments is not involved in tRNA modification. m³C formation in tRNA(Thr1) could be reconstituted using recombinant Abp140p in the presence of Ado-Met, whereas m³C did not form in tRNA(Ser1) in vitro, indicating the absence of a factor(s) required for tRNA(Ser1) m³C formation. Thus, ABP140 has been designated TRM140 according to the preferred nomenclature. In addition, we observed a specific reduction of m³C formation in HeLa cells by siRNA-mediated knock down of the human ortholog of TRM140.