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Human noise blindness drives suboptimal cognitive inference.

Santiago Herce Castañón^1,2, Rani Moran^3,4, Jacqueline Ding⁵

¹Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK. s.hercecastanon@gmail.com.

Nature Communications

|April 14, 2019

Summary

This summary is machine-generated.

Humans are good at sensorimotor tasks but make biased cognitive judgments. This study shows cognitive biases arise from ignoring noise during information integration, not sensory encoding.

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Area of Science:

Cognitive psychology
Neuroscience
Computational modeling

Background:

Humans exhibit near-optimal sensorimotor judgments but systematic biases in cognitive judgments.
A key question is whether these cognitive biases stem from sensory encoding or later information integration processes.

Purpose of the Study:

To test the hypothesis that humans are blind to noise during cognitive information integration.
To investigate the source of the
optimality gap
in human cognitive judgments.

Main Methods:

Six psychophysical experiments involving human observers judging the average orientation of contrast gratings.
Manipulation of stimulus contrast (encoding noise) and orientation variability (integration noise).
Comparison of human performance with a Bayesian model blind to integration noise.

Main Results:

Participants adapted near-optimally to changes in encoding noise.
Increased integration noise led to suboptimal behaviors, including ignoring base rates and excessive confidence.
Suboptimal behaviors were accurately predicted by a Bayesian model that disregarded integration noise.

Conclusions:

The "optimality gap" in human cognitive judgments is primarily due to insensitivity to noise during information integration.
A computationally grounded explanation for suboptimal cognitive inference is provided.
Findings highlight the distinct processing of sensory encoding versus cognitive integration noise.