Human noise blindness drives suboptimal cognitive inference

Santiago Herce Castañón, Dan Bang, Rani Moran, Jacqueline Ding, Tobias Egner and Christopher Summerfield

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Received: 20th February 18

Humans typically make near-optimal sensorimotor judgements but show systematic biases when making more cognitive judgements. Here we test the hypothesis that, while humans are sensitive to the noise present during early sensory processing, the “optimality gap” arises because they are blind to noise introduced by later cognitive integration of variable or discordant information. In six psychophysical experiments, human observers judged the average orientation of an array of contrast gratings. We varied the stimulus contrast (encoding noise) and orientation variability (integration noise) of the array. Participants adapted optimally to changes in encoding noise, but, under increased integration noise, they displayed a range of suboptimal behaviours: they ignored stimulus base rates, reported excessive confidence in their choices, and refrained from opting out of objectively difficult trials. These overconfident behaviours were captured by a Bayesian model which is blind to integration noise. Our study provides a computationally grounded explanation of suboptimal cognitive inferences.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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