The motivation for use of biosensors in audiovisual media is made by highlighting problem of signal loss due to wide variability in playback devices. A metadata system that allows creatives to steer signal modifications as a function of audience emotion and cognition as determined by biosensor analysis.
Attentional stances are particular ways in which the location of the source or seat of attention is situated within bodily experience. The goal of the study is to use this untapped cognitive resource to refine the construct of mindfulness by providing objective measurement and experimental control of these aspects of mindfulness. Location of the seat of attention, also described as self-location or egocenter , has been shown to have measurable impact on cognitive skill, emotional temperament, and self-construal, as well as social and moral attitudes. Our recent study has shown that the seat of attention can be volitionally self-regulated into various internal attentional stances that are stably associated with distinct patterns of cortical activation as measured with EEG. These results suggest that control of attentional stance should provide direct management of specific cognitive and emotional resources. Two specific global cognitive states associated with mindfulness are the positive emotional states of vipaśyanā and śamatha . The current study of the correlations of 11 attentional stances for with positive emotions reveal the association of vipaśyanā with a diffuse attentional stance centered on the abdomen, and śamatha with a focused attentional stance situated along the midline of the body. In addition to advancing denotation and differentiation of these two distinct elements of mindfulness, these results provide the opportunity for more efficient mindfulness training. Such opportunity would benefit anyone needing stress-reducing mindfulness training, and in particular, for underserved individuals in society who are most in need of stress management.
Lightness perception is a long-standing topic in research on human vision, but very few image-computable models of lightness have been formulated. Recent work in computer vision has used artifical neural networks and deep learning to estimate surface reflectance and other intrinsic image properties. Here we investigate whether such networks are useful as models of human lightness perception. We train a standard deep learning architecture on a novel image set that consists of simple geometric objects with a few different surface reflectance patterns. We find that the model performs well on this image set, generalizes well across small variations, and outperforms three other computational models. The network has partial lightness constancy, much like human observers, in that illumination changes have a systematic but moderate effect on its reflectance estimates. However, the network generalizes poorly beyond the type of images in its training set: it fails on a lightness matching task with unfamiliar stimuli, and does not account for several lightness illusions experienced by human observers.
Changes in the footballing world’s approach to technology and innovation contributed to the decision by the International Football Association Board (IFAB) to introduce Video Assistant Referees (VAR). The change meant that under strict protocols referees could use video replays to review decisions in the event of a “clear and obvious error” or a “serious missed incident”. This led to the need by Fédération Internationale de Football Association (FIFA) to develop methods for quality control of the VAR-systems, which was done in collaboration with RISE Research Institutes of Sweden AB. One of the important aspects is the video quality. The novelty of this study is that it has performed a user study specifically targeting video experts i.e., to measure the perceived quality of video professionals working with video production as their main occupation. An experiment was performed involving 25 video experts. In addition, six video quality models have been benchmarked against the user data and evaluated to show which of the models could provide the best predictions of perceived quality for this application. Video Quality Metric for variable frame delay (VQM_VFD) had the best performance for both formats, followed by Video Multimethod Assessment Fusion (VMAF) and VQM General model.
Accurate models of the electroretinogram are important both for understanding the multifold processes of light transduction to ecologically useful signals by the retina, but also its diagnostic capabilities for the identification of the array of retinal diseases. The present neuroanalytic model of the human rod ERG is elaborated from the same general principles as that of Hood & Birch (1992), but incorporates the more recent understanding of the early stages of ERG generation by Robson & Frishman (2014). As a result, it provides a significantly better match in six different waveform features of the canonical ERG flash intensity series than previous models of rod responses.
