The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities

Xin Tong, Thi Thu Uyen Hoang, Xue-Xin Wei, Michael Hahn

Keywords: neuroscience; Bayesian inference; probability distortion; probability weighting function; probability perception; decision making; representation of probabilities

TL;DR: Our work provides a unifying normative account of human probability perception, showing that classic biases and flexible adaptation to new statistics both emerge from the principle of efficient coding.

Abstract:

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.

Primary Area: applications to neuroscience & cognitive science

Submission Number: 9593