Study: Brain Signals in the Visual Area Can Indicate the Colors Observers Are Viewing

Do different observers experience similar neural activity in response to the same color? Does color produce distinct response patterns in specific brain areas? To explore these inquiries, researchers at the University of Tübingen utilized existing knowledge of color responses from various observers’ brains to predict the colors an individual is perceiving based on their brain activity. By estimating general brain commonality and responding to achromatic, spatial stimuli, the authors successfully aligned disparate brain responses within a common response framework linked to the retina. In this framework, derived independently of specific color responses, the perceived color can be decoded across individuals, revealing distinct spatial color biases between regions.

Using a sample of male and female volunteers, Michael M. Bannert & Andreas Bartels examined whether spatial color biases are shared among human observers and whether these biases differ among various regions. Image credit: Vat Loai.

Employing functional MRI scans, researchers Michael Banert and Andreas Bartels from the University of Tübingen captured images of subjects’ brains while they viewed visual stimuli, identifying various signals related to red, green, and yellow colors.

Remarkably, the patterns of brain activity appeared similar among subjects who had not participated previously. This suggests that the colors perceived can be accurately predicted by comparing them to the brain images of other participants.

The representation of color in the brain proves to be much more consistent than previously believed.

While it was already feasible to identify the colors an individual observed using functional magnetic resonance imaging (fMRI), this was only applicable to the same brain.

“We aimed to investigate whether similar colors are encoded across different brains,” Dr. Banert stated.

“In other words, if we only have neuronal color signals from another person’s brain, can we predict the colors they’re perceiving?”

“It’s well established that different brains exhibit roughly similar functional structures.”

“For instance, specific areas are more active when viewing faces, bodies, or simply colors.”

During the color experiment, researchers employed specific classification algorithms to analyze fMRI data, systematically differentiating signals originating from the brains of various groups of individuals by color.

Subsequently, data from new subjects were utilized to ascertain the colors they were perceiving using neuronal signals.

To frame each brain’s orientation, scientists spatially mapped how they responded to stimuli at different locations within their visual field using fMRI measurements.

“At this stage, we did not incorporate colors to avoid any bias in our results—only black and white patterns,” Professor Bartels explained.

“By simply merging this mapping data with color information from another person’s brain, we ensured we correctly identified the ‘new’ brain activity related to what the person was observing at that moment.”

“I was surprised to discover that even subtle variations in individual colors show remarkable similarity across brain activity patterns in specific visual processing regions, something previously unknown.”

Spatial color coding in the brain is domain-specific and organized consistently among individuals.

“There must be functional or evolutionary factors contributing to this uniform development, but further clarification is needed,” the authors noted.

The study was published this week in the Journal of Neuroscience.

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Michael M. Bannert and Andreas Bartels. Large-scale color biases in the functional architecture of the retina are domain-specific and shared throughout the human brain. Journal of Neuroscience Published online on September 8th, 2025. doi: 10.1523/jneurosci.2717-20.2025

Source: www.sci.news

Researchers Utilize ‘Mobile Observers’ to Uncover Previously Uncharted Air Pollutants

A groundbreaking study conducted by the University of Utah and EDF used Google Street View vehicles to closely monitor air quality in the Salt Lake Valley. This study revealed highly localized pollution hotspots, highlighted issues of environmental justice, and represents a major advance in understanding and addressing the uneven impacts of urban air pollution.

In the Salt Lake Valley, vehicles equipped with advanced air quality measurement tools similar to Google Street View vehicles drove through neighborhoods and collected highly detailed air quality data. This comprehensive sampling revealed clear variations in pollution levels within different regions. Additionally, new atmospheric modeling techniques have been developed to accurately identify these sources of pollution emissions.

In 2019, a team of atmospheric scientists at the University of Utah, in collaboration with the Environmental Defense Fund and other partners, introduced an innovative approach to air quality monitoring in the Salt Lake Valley. They equipped two Google Street View cars with air quality measurement tools, creating mobile air pollution detectors capable of identifying hyper-local pollution hotspots.

Over the next few months, John Lin, a professor of atmospheric science at the university, developed a breakthrough modeling technique. The method combined wind pattern modeling and statistical analysis to trace pollutants to their exact source. This technology provided a level of detail in pollution tracking that exceeded the more extensive and less accurate methods of traditional air quality monitoring, which typically assessed air quality across urban areas.

According to a study led by the United States and the Environmental Defense Fund (EFD) recently published in the journal atmospheric environment, the results are out. “With mobile vehicles, you can literally send them anywhere you can drive and find out more about pollution, including off-road sources that traditional monitoring has missed,” said Lin, who is also deputy director of the Wilkes Climate Science Center. “We can put up a map,” he said. policy. “I think the idea of ​​patrolling lifeguards is pretty viable in many cities.”

Researchers equipped vehicles with air quality instruments and asked drivers to explore their neighborhoods street by street, taking air samples once every second, from May 2019 to March 2020. This created a huge dataset of air pollutant concentrations in the Salt Lake Valley. It is the highest-resolution map showing pollution hotspots at a detailed scale, with data capturing fluctuations within 200 meters, or about the width of two football fields.

The air quality pattern was as expected, with higher pollution around traffic and industrial areas. Neighborhoods with lower average incomes and higher proportions of black residents had more pollutants, confirming well-known issues of environmental justice. This pattern traces its legacy to his century-old redlining policy, in which Homeowner’s Loan Corp. created maps outlining “dangerous” areas in red ink.

“Air quality is not a new problem. It’s been around for decades, and it was probably worse back then,” Lin said. “The Interstate 15 corridor runs along red-light districts. And sadly, there is quite a bit of research supporting the fact that the red-light districts of 80 years ago are still important. These areas still struggle with air quality issues. These areas tend to be underinvested, so the legacy of racism remains.”

Research-grade equipment in Google Street View vehicles measures the ambient air that is pumped in from the surrounding area and detects major emissions. The researchers tested Lin’s new atmospheric modeling approach using two case studies of well-known pollution sources. The model was then applied to analyze previously unknown areas of PM elevation.2.5

The authors hope to use atmospheric models for projects such as Air Tracker, a web-based tool developed in partnership with EDF and Carnegie Mellon University that helps users find possible sources of air pollution in their neighborhood.

This research was funded by the Environmental Defense Fund. Other authors of this article are also cited, and the study utilized the resources of the National Center for High Performance Computing.

Source: scitechdaily.com