The seemingly effortless process of inferring physical reality from the sensory input is highly influenced by previous knowledge, leading to perceptual biases. Two common ones are contraction bias (the tendency to perceive stimuli as similar to previous ones) and choice bias (the tendency to prefer a specific response). Combining human psychophysical experiments with computational modeling we show that these biases reflect two different learning processes. Contraction bias reflects unsupervised learning of stimuli statistics, whereas choice bias results from supervised or reinforcement learning. This dissociation reveals a hierarchical, two-stage process. The first, where stimuli statistics are learned and integrated with representations, is unsupervised. The second, where a binary judgment is applied to the combined percept, is learned in a supervised way.
Participants were presented with two, temporally-separated, pure tones and were instructed to report which one is larger. A, Responses of the human participants in the f_1×f_2 plane. Color denotes the probability of responding “f_1>f_2 “. B, responses of the fitted optimal Perceptrons.
Choice bias (a lateral shift of the psychometric curve) was readily modifiable by feedback. By contrast, contraction bias which manifests as a line of indifference whose slope is smaller than 1 was insensitive to the feedback.
