Rewards of beauty: the opioid system mediates social motivation in humans

By O. Chelnokova, Bruno Laeng, M. Eikemo, J. Riegels, G. Løseth, H. Maurud, Frode Willoch, S. Leknes. 2014

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation (2021, methodology): “The images in last two columns are results of out-of-distribution (Non-CelebA), i.e. , images from other face dataset such as [4, 6, 41], which illustrate that our method is prepared to produce pleasing results with images in the wild.”
Towards Open-World Text-Guided Face Image Generation and Manipulation (2021, methodology): “, images from other face dataset such as [66], [67], [68], which illustrate that our method is prepared to produce pleasing results for the in-the-wild images.”
TediGAN: Text-Guided Diverse Image Generation and Manipulation (2020, unknown): “To illustrate the out-of-distribution generalization of inversion module, we collect some real images from multiple face datasets [4, 8, 55] and paintings, caricatures, and black and white photos from the internet.”
Synchrony as an Adaptive Mechanism for Large-Scale Human Social Bonding (2016, unknown): “…evidence to suggest that interfering with the activity of the EOS in humans can affect the way that positive social stimuli are perceived (Chelnokova et al. 2014), and that areas of the brain with high concentrations of opioid receptors are responsive to social rejection and acceptance…”
APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs (2019, methodology): “We collect 6,655 frontal face photos taken from ten face datasets [37, 21, 6, 25, 24, 7, 35, 34, 4, 36].”