Redefining A in RGBA: Towards a Standard for Graphical 3D Printing

Advances in multimaterial 3D printing have the potential to reproduce various visual appearance attributes of an object in addition to its shape. Since many existing 3D file formats encode color and translucency by RGBA textures mapped to 3D shapes, RGBA information is particularly important for practical applications. In contrast to color (encoded by RGB), which is specified by the object's reflectance, selected viewing conditions and a standard observer, translucency (encoded by A) is neither linked to any measurable physical nor perceptual quantity. Thus, reproducing translucency encoded by A is open for interpretation. In this paper, we propose a rigorous definition for A suitable for use in graphical 3D printing, which is independent of the 3D printing hardware and software, and which links both optical material properties and perceptual uniformity for human observers. By deriving our definition from the absorption and scattering coefficients of virtual homogeneous reference materials with an isotropic phase function, we achieve two important properties. First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is re-scaled for printing. Second, determining the value of A for a real (potentially non-homogeneous) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials. Such measurements can be conducted by commercial spectrophotometers used in graphic arts. Finally, we conduct visual experiments employing the method of constant stimuli, and derive from them an embedding of A into a nearly perceptually uniform scale of translucency for the reference materials.Comment: 20 pages (incl. appendices), 20 figures. Version with higher quality images: https://cloud-ext.igd.fraunhofer.de/s/pAMH67XjstaNcrF (main article) and https://cloud-ext.igd.fraunhofer.de/s/4rR5bH3FMfNsS5q (appendix). Supplemental material including code: https://cloud-ext.igd.fraunhofer.de/s/9BrZaj5Uh5d0cOU/downloa

Effects of Face and Background Color on Facial Expression Perception

Detecting others’ emotional states from their faces is an essential component of successful social interaction. However, the ability to perceive emotional expressions is reported to be modulated by a number of factors. We have previously found that facial color modulates the judgment of facial expression, while another study has shown that background color plays a modulatory role. Therefore, in this study, we directly compared the effects of face and background color on facial expression judgment within a single experiment. Fear-to-anger morphed faces were presented in face and background color conditions. Our results showed that judgments of facial expressions was influenced by both face and background color. However, facial color effects were significantly greater than background color effects, although the color saturation of faces was lower compared to background colors. These results suggest that facial color is intimately related to the judgment of facial expression, over and above the influence of simple color

Team flow is a unique brain state associated with enhanced information integration and neural synchrony

Team flow occurs when a group of people reaches high task engagement while sharing a common goal as in sports teams and music bands. While team flow is a superior enjoyable experience to individuals experiencing flow or regular socialization, the neural basis for such superiority is still unclear. Here, we addressed this question utilizing a music rhythm task and electroencephalogram hyper-scanning. Experimental manipulations held the motor task constant while disrupted the hedonic musical correspondence to blocking flow or occluded the partner’s body and task feedback to block social interaction. The manipulations’ effectiveness was confirmed using psychometric ratings and an objective measure for the depth of flow experience through the inhibition of the auditory-evoked potential to a task-irrelevant stimulus. Spectral power analysis revealed higher beta/gamma power specific to team flow at the left temporal cortex. Causal interaction analysis revealed that the left temporal cortex receives information from areas encoding individual flow or socialization. The left temporal cortex was also significantly involved in integrated information at both the intra- and inter-brains levels. Moreover, team flow resulted in enhanced global inter-brain integrated information and neural synchrony. Thus, our report presents neural evidence that team flow results in a distinct brain state and suggests a neurocognitive mechanism by which the brain creates this unique experience

Team flow is a unique brain state associated with enhanced information integration and neural synchrony

Team flow occurs when a group of people reaches high task engagement while sharing a common goal as in sports teams and music bands. While team flow is a superior enjoyable experience to individuals experiencing flow or regular socialization, the neural basis for such superiority is still unclear. Here, we addressed this question utilizing a music rhythm task and electroencephalogram hyper-scanning. Experimental manipulations held the motor task constant while disrupted the hedonic musical correspondence to blocking flow or occluded the partner’s body and task feedback to block social interaction. The manipulations’ effectiveness was confirmed using psychometric ratings and an objective measure for the depth of flow experience through the inhibition of the auditory-evoked potential to a task-irrelevant stimulus. Spectral power analysis revealed higher beta/gamma power specific to team flow at the left temporal cortex. Causal interaction analysis revealed that the left temporal cortex receives information from areas encoding individual flow or socialization. The left temporal cortex was also significantly involved in integrated information at both the intra- and inter-brains levels. Moreover, team flow resulted in enhanced global inter-brain integrated information and neural synchrony. Thus, our report presents neural evidence that team flow results in a distinct brain state and suggests a neurocognitive mechanism by which the brain creates this unique experience

Universality and superiority in preference for chromatic composition of art paintings

Color composition in paintings is a critical factor affecting observers' aesthetic judgments. We examined observers' preferences for the color composition of Japanese and Occidental paintings when their color gamut was rotated. In the experiment, observers were asked to select their preferred image from original and three hue-rotated images in a four-alternative forced choice paradigm. Despite observers' being unfamiliar with the presented artwork, the original paintings (0 degrees) were preferred more frequently than the hue-rotated ones. Furthermore, the original paintings' superiority was observed when the images were divided into small square pieces and their positions randomized (Scrambled condition), and when the images were composed of square pieces collected from different art paintings and composed as patchwork images (Patchwork condition). Therefore, the original paintings' superiority regarding preference was quite robust, and the specific objects in the paintings associated with a particular color played only a limited role. Rather, the original paintings' general trend in color statistics influenced hue-angle preference. Art paintings likely share common statistical regulations in color distributions, which may be the basis for the universality and superiority of the preference for original paintings.- We thank Dr. Yukinori Misaki at Kagawa National Institute of Technology, Japan and Ms. Nobuyo Okada and Ms. Kanako Maruchi at Toyohashi City Museum of Art and History, Japan for assisting in the measurement of art paintings. This work was supported by JSPS KAKENHI Grant Number JP19H01119 and 20H05956, and by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UIDB/04650/2020

How good are RGB cameras retrieving colors of natural scenes and paintings?—A study based on hyperspectral imaging

RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ΔE*ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ΔE*ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.This work was supported by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UIDB/04650/2020

Cooperative update of beliefs and state-transition functions in human reinforcement learning

It is widely known that reinforcement learning systems in the brain contribute to learning via interactions with the environment. These systems are capable of solving multidimensional problems, in which some dimensions are relevant to a reward, while others are not. To solve these problems, computational models use Bayesian learning, a strategy supported by behavioral and neural evidence in human. Bayesian learning takes into account beliefs, which represent a learner’s confidence in a particular dimension being relevant to the reward. Beliefs are given as a posterior probability of the state-transition (reward) function that maps the optimal actions to the states in each dimension. However, when it comes to implementing this learning strategy, the order in which beliefs and state-transition functions update remains unclear. The present study investigates this update order using a trial-by-trial analysis of human behavior and electroencephalography signals during a task in which learners have to identify the reward-relevant dimension. Our behavioral and neural results reveal a cooperative update—within 300 ms after the outcome feedback, the state-transition functions are updated, followed by the beliefs for each dimension