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Alan Stocker, Eero Simoncelli; A model of self-consistent perception. Journal of Vision 2008;8(6):1106. doi: 10.1167/8.6.1106.
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© ARVO (1962-2015); The Authors (2016-present)
Human perception is context-dependent. In addition to sensory context, data from recent psychophysical studies suggest that context can also include previous perceptual decisions (Baldassi etal. PLoS, 4(3):e56, 2006; Jazayeri and Movshon, Nature, 446:912ff, 2007). In both studies, subjects were asked to estimate a stimulus parameter (e.g., the exact orientation angle of a Gabor patch) after being forced to make a binary decision about that parameter (e.g., orientation to the left/right of vertical). On each individual trial, the subjects' estimates were consistent with their preceding decision (i.e., a decision of “left of vertical” was followed by an estimated direction left of vertical). In addition, the distributions of estimates were bimodal, indicating repulsion away from the decision boundary.
We present a probabilistic observer model that accounts for this perceptual behavior. Specifically, we adopt the general hypothesis that the brain attempts to perform optimal estimation of stimulus parameters based on noisy sensory evidence and prior expectations. However, we augment this hypothesis by assuming that the observer performs the secondary estimation task in the belief that his/her previous decision regarding the data was correct. Noisy sensory evidence may initially support both decisions, although with different probability. After making the decision, however, the observer discards all potential estimates that are not in agreement with the choice. This leads to the observed repulsive bias away from the decision boundary. The model fits the data well and makes quantitative predictions for novel experiments.
It is worth noting that the behavior of the model is suboptimal in terms of estimation performance. An optimal (Bayesian) observer model would compute estimates from the sensory evidence under each possible decision taken, and then average these estimates, weighting each according to the probability that the corresponding decision is correct. Thus, our model implies that humans sacrifice performance in order to maintain self-consistency.
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