The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities
This provides a unifying account for understanding human decision-making paradoxes, though it is incremental in building on existing noisy representation theories.
The study tackled the problem of how humans represent probabilities by proposing a model based on rational inference from noisy neural encoding, which accurately accounted for behavior in lottery and dot counting tasks and adaptation to a bimodal prior.
Understanding the representation of probability in the human mind has been of great interest to understanding human decision making. Classical paradoxes in decision making suggest that human perception distorts probability magnitudes. Previous accounts postulate a Probability Weighting Function that transforms perceived probabilities; however, its motivation has been debated. Recent work has sought to motivate this function in terms of noisy representations of probabilities in the human mind. Here, we present an account of the Probability Weighting Function grounded in rational inference over optimal decoding from noisy neural encoding of quantities. We show that our model accurately accounts for behavior in a lottery task and a dot counting task. It further accounts for adaptation to a bimodal short-term prior. Taken together, our results provide a unifying account grounding the human representation of probability in rational inference.