Naturally Biased: A Look at How Our Brains Learn Confidence
based on research paper on Nature.com The sources explore the concept of confidence bias through the lens of a deep neural network model. The model, designed to mimic human decision-making, was trained on visual classification tasks using image datasets like MNIST and CIFAR-10. Surprisingly, despite being optimized for accuracy, the model exhibited common human confidence biases, suggesting these biases stem from a rational adaptation to the statistics of our experiences. One bias the model replicated was the positive evidence bias, where confidence is higher when more evidence supports the correct choice, even if the signal-to-noise ratio is constant. This was consistently observed across datasets and tasks, including a reinforcement learning scenario where the model learned to opt out of decisions with low confidence. The model also mirrored a more nuanced bias where confidence becomes less accurate at predicting decision accuracy as decision accuracy increases, a phenomenon obse...