Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Qualia have traditionally been considered difficult to measure objectively, but with the recent spread of fMRI (functional Magnetic Resonance Imaging) and other techniques, various experimental efforts have been made. In this paper, focusing on the qualia for color, we created 6 colors with different RGB values for reference colors of RED, light GREEN, BLUE, YELLOW, and PURPLE, and showed them to 306 subjects. For example, for RED and 5 generated colors, we asked them, "Choose a color that you feel is RED," and asked them to choose. A probability density function was defined for each of the five generated colors and the reference color, which is the primary color of RED, light GREEN, BLUE, YELLOW, and PURPLE, and the Kullback-Leibler divergence between the probability density function of the reference color and the generated color was calculated, the relationship between the number of samples of the selected color and the Kullback-Leibler divergence was obtained, and the difference in color sensation-qualia was calculated accordingly. As a result, it was confirmed that the larger the distance of the Kullback-Leibler divergence, the smaller the number of samples, but that the distribution shape in which the number of samples decreased for each color differed greatly. This suggests that if we see a color such as RED to PURPLE, we are randomly choosing a color that "feels."

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biosystems.2023.105011DOI Listing

Publication Analysis

Top Keywords

kullback-leibler divergence
16
color
12
number samples
12
red light
8
light green
8
green blue
8
blue yellow
8
yellow purple
8
generated colors
8
probability density
8

Similar Publications

In the gravitational-wave analysis of pulsar-timing-array datasets, parameter estimation is usually performed using Markov chain Monte Carlo methods to explore posterior probability densities. We introduce an alternative procedure that instead relies on stochastic gradient-descent Bayesian variational inference, whereby we obtain the weights of a neural-network-based approximation of the posterior by minimizing the Kullback-Leibler divergence of the approximation from the exact posterior. This technique is distinct from simulation-based inference with normalizing flows since we train the network for a single dataset, rather than the population of all possible datasets, and we require the computation of the data likelihood and its gradient.

View Article and Find Full Text PDF

Active-matter systems are inherently out-of-equilibrium and perform mechanical work by utilizing their internal energy sources. Breakdown of time-reversal symmetry (BTRS) is a hallmark of such dissipative nonequilibrium dynamics. We introduce a robust, experimentally accessible, noninvasive, quantitative measure of BTRS in terms of the Kullback-Leibler divergence in collision events, demonstrated in our novel artificial active matter, comprised of battery-powered spherical rolling robots whose energetics in different modes of motion can be measured with high precision.

View Article and Find Full Text PDF

Background: One of the most persistent questions in autism research is why males are more consistently diagnosed than females. Neuroimaging studies have sought to understand this disparity by examining sex differences, primarily through functional and structural connectivity. However, much less is known about how brain networks are organized in autism from a morphological perspective, and how this organization may help explain its sex-related characteristics.

View Article and Find Full Text PDF

Objective: Research profiles highlight scientists' research focus, enabling talent discovery and fostering collaborations, but they are often outdated. Automated, scalable methods are urgently needed to keep these profiles current.

Methods: In this study, we design and evaluate two Large Language Models (LLMs)-based methods to generate scientific interest profiles-one summarizing researchers' PubMed abstracts and the other generating a summary using their publications' Medical Subject Headings (MeSH) terms-and compare these machine-generated profiles with researchers' self-summarized interests.

View Article and Find Full Text PDF

Current brain-machine interfaces (BMIs) often rely on decoders trained for single tasks, limiting their flexibility in real-world applications. We propose an online learning framework that enables the transfer of neural-to-movement (knowledge) across tasks, supporting both task switching and memory retention. In our framework, neural activity is projected into a dynamical jPCA space to effectively dissociate into variant and invariant components.

View Article and Find Full Text PDF