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Brain Confidence: How Our Neurons Make Decisions
Confidence is a trait typically cast as a higher-order function in the brain. It’s at once the act of making a decision, recognizing the decision as thought, and measuring the degree to which that decision makes sense. An impartial judge it’s not, prophet even less, yet confidence surely requires self-awareness, a metacognition unmatched in the animal kingdom. Right?
Research from Adam Kepecs and his fellow neuroscientists at Cold Spring Harbor Laboratory says otherwise. Their work with rats suggests that confidence estimates—tracking one's confidence while making a decision and anticipating a result—are at work even in rodent brains. Moreover, his studies suggest “confidence estimation may be a fundamental and ubiquitous component of decision-making.”
Kepecs says his research seeks to understand the mechanisms of how confidence works at the level of the neuron. The goal is to “understand how their connections, how their architecture and how the dynamics that run on that architecture allows us or allows mammals in general to make decisions to compute confidence about decisions.”
His work centers on rats, though its implications stretch out across a broader set of mammals. “From an evolutionary perspective, rats are not that different from us,” he says. “Some of the fundamental brain architecture is really common and that really allows us to tap into a system that is simpler ... that is a great advantage.”
The neurology of decision-making has been the focus of a number of studies on both humans and other animals which have sought to map experiences ranging from mental deliberation to the processing of the resulting reward or regret.
Researchers in one human-based study used functional magnetic resonance imaging (fMRI) to track brain activity in the lead up to a decision by gradually revealing a photograph. Before the participants recognized the image—before the decision was made about what the picture showed—a network of brain activity registered across the brain. Yet, parts of the brain remained at lower-level activity until the very moment of recognition, at which point activity spiked and the regions lit up on the scans. The researchers concluded such findings begin to outline a hierarchy of neural pathways at play in decision-making in which the "eureka" moment is tracked in specific parts of the brain.
Such research has also allowed neuroscience to locate specific cells and brain regions critical to decision-making, such as the von Economo neurons and the striatum. Von Economo neurons, also called spindle neurons, are unique in big-brained mammals, including whales, dolphins and elephants. In humans, these neurons are thought to facilitate complex thinking and are found in limited areas of the cingulate cortex, the area just above the corpus callosum that connects the left and right brain hemispheres.
As well, research links the striatum, a cluster of neurons in the basal ganglia at the base of the forebrain, with both flexible and stimulus-response decision-making. Researchers can even pinpoint which parts of the striatum are at work in each type of decision, with the interior, side part of the striatum favoring stimulus-response strategies and the front and interior-middle favoring goal-directed strategies. Even with this pinpoint research that identifies specific regions as important to decision-making, the process itself plays out across the brain. This includes regions of the brain that evolved earlier and, in some form, are mirrored in the brain evolution of other mammals.
Similar neural architecture among mammals led Kepecs and his team to focus on rats. While rodent decision-making is of course not anywhere near as complex as that of humans, decision-making and access to the reward-based expectations created by confidence is, to some degree, innate to these animals, says Kepecs.
Kepecs and his team test for confidence estimations by training the rodents to perform a simple task involving the choice between options. With a correct option, and one that is correct all the time, confidence comes easily. The real test comes when the difficulty is ramped up, when the same actions don’t produce the same results, and when a delay is introduced between performing the action and the reward—in other words, when there is plenty of opportunity for confidence to drop as it comes in friction with results.
Performing tests hundreds and hundreds of times a day with varied difficulty, Kepecs says, allows researchers “to calibrate [the rats’] confidence in these simple decisions and it also allow us from a neurobiological perspective to actually look into their brain and what happens in their brains when they’re doing this task.”
“In some ways we feel that rodents have all the components that make them function, that make them able to decide, and it’s going to be easier for us to understand,” he says.
Neuroscience research is revealing how the chemistry and architecture of the brain make decisions for us, and how the brain's reaction to decision-making is based on confidence. Researchers has shown that a networked, hierarchical process is strung out across the brain to achieve these functions, even while specific neuron clusters are more active than others in helping us make decisions.
While human-focused research uses leading-edge brain scans, some scientists are studying the neurology of decision-making in rodents because their brains are evolutionarily similar to those of humans.
—Wheeler, M., et al., "Accumulation and the Moment of Recognition: Dissociating Perceptual Recognition Processes Using fMRI."
—Johnson, A., et al. "Integrating hippocampus and striatum in decision-making."
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.
Can computers do calculations in multiple universes? Scientists are working on it. Step into the world of quantum computing.
- While today's computers—referred to as classical computers—continue to become more and more powerful, there is a ceiling to their advancement due to the physical limits of the materials used to make them. Quantum computing allows physicists and researchers to exponentially increase computation power, harnessing potential parallel realities to do so.
- Quantum computer chips are astoundingly small, about the size of a fingernail. Scientists have to not only build the computer itself but also the ultra-protected environment in which they operate. Total isolation is required to eliminate vibrations and other external influences on synchronized atoms; if the atoms become 'decoherent' the quantum computer cannot function.
- "You need to create a very quiet, clean, cold environment for these chips to work in," says quantum computing expert Vern Brownell. The coldest temperature possible in physics is -273.15 degrees C. The rooms required for quantum computing are -273.14 degrees C, which is 150 times colder than outer space. It is complex and mind-boggling work, but the potential for computation that harnesses the power of parallel universes is worth the chase.