  Conventional and current wisdom assumes that the brain represents probability
as a continuous number to many decimal places. This assumption seems
implausible given finite and scarce resources in the brain. Quantization is an
information encoding process whereby a continuous quantity is systematically
divided into a finite number of possible categories. Rounding is a simple
example of quantization. We apply this information theoretic concept to develop
a novel quantized (i.e., discrete) probability distortion function. We develop
three conjunction probability gambling tasks to look for evidence of quantized
probability representations in the brain. We hypothesize that certain ranges of
probability will be lumped together in the same indifferent category if a
quantized representation exists. For example, two distinct probabilities such
as 0.57 and 0.585 may be treated indifferently. Our extensive data analysis has
found strong evidence to support such a quantized representation: 59/76
participants (i.e., 78%) demonstrated a best fit to 4-bit quantized models
instead of continuous models. This observation is the major development and
novelty of the present work. The brain is very likely to be employing a
quantized representation of probability. This discovery demonstrates a major
precision limitation of the brain's representational and decision-making
ability.
