Georgia Tech researchers have developed a neural network, RTNet, that mimics human decision-making processes, including confidence and variability, improving its reliability and accuracy in tasks like digit recognition.
Humans make nearly 35,000 decisions each day, ranging from determining if it’s safe to cross the road to choosing what to have for lunch. Each decision involves evaluating options, recalling similar past situations, and feeling reasonably confident about the right choice. What might appear to be a snap decision actually results from gathering evidence from the environment. Additionally, the same person might make different decisions in identical scenarios at different times.
Neural networks do the opposite, making the same decisions each time. Now, Georgia Tech researchers in Associate Professor Dobromir Rahnev’s lab are training them to make decisions more like humans. This science of human decision-making is only just being applied to machine learning, but developing a neural network even closer to the actual human brain may make it more reliable, according to the researchers.
In a paper in Nature Human Behaviour, a team from the School of Psychology reveals a new neural network trained to make decisions similar to humans.
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