Prior probability and feature predictability interactively bias perceptual decisions

Anticipating a forthcoming sensory experience facilitates perception for expected stimuli but also hinders perception for less likely alternatives. Recent neuroimaging studies suggest that expectation biases arise from feature-level predictions that enhance early sensory representations and facilita...

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Bibliographic Details
Published in:Neuropsychologia Vol. 61; pp. 210 - 221
Main Authors: Dunovan, Kyle E., Tremel, Joshua J., Wheeler, Mark E.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.08.2014
Elsevier
Subjects:
Adult
Anticipation, Psychological
Biological and medical sciences
Cues
Decision Making
Discrimination, Psychological
Drift-diffusion model
Expectation
Face
Female
Fundamental and applied biological sciences. Psychology
Humans
Male
Models, Psychological
Neuropsychological Tests
Perception
Perceptual decision-making
Photic Stimulation - methods
Predictive coding
Probability
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Reaction Time
Vision
Visual Perception
Young Adult
Predictive coding
Expectation
Drift-diffusion model
Perceptual decision-making
Human
Decision making
Prediction
Cognition
Prior probability
Experimental study
Coding
Vision
Perception
Mathematical model
Cognitive bias
ISSN:0028-3932, 1873-3514, 1873-3514
Online Access:Get full text
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Summary:Anticipating a forthcoming sensory experience facilitates perception for expected stimuli but also hinders perception for less likely alternatives. Recent neuroimaging studies suggest that expectation biases arise from feature-level predictions that enhance early sensory representations and facilitate evidence accumulation for contextually probable stimuli while suppressing alternatives. Reasonably then, the extent to which prior knowledge biases subsequent sensory processing should depend on the precision of expectations at the feature level as well as the degree to which expected features match those of an observed stimulus. In the present study we investigated how these two sources of uncertainty modulated pre- and post-stimulus bias mechanisms in the drift-diffusion model during a probabilistic face/house discrimination task. We tested several plausible models of choice bias, concluding that predictive cues led to a bias in both the starting-point and rate of evidence accumulation favoring the more probable stimulus category. We further tested the hypotheses that prior bias in the starting-point was conditional on the feature-level uncertainty of category expectations and that dynamic bias in the drift-rate was modulated by the match between expected and observed stimulus features. Starting-point estimates suggested that subjects formed a constant prior bias in favor of the face category, which exhibits less feature-level variability, that was strengthened or weakened by trial-wise predictive cues. Furthermore, we found that the gain on face/house evidence was increased for stimuli with less ambiguous features and that this relationship was enhanced by valid category expectations. These findings offer new evidence that bridges psychological models of decision-making with recent predictive coding theories of perception. •Competing diffusion models were fit to probabilistic face/house decisions.•Probabilistic cues biased the starting-point and drift-rate of face/house decisions.•Stimulus discriminability decreased RT when consistent with expected category.•Expectations and stimulus discriminability interactively modulated evidence gain.
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ISSN:0028-3932
1873-3514
1873-3514
DOI:10.1016/j.neuropsychologia.2014.06.024