A computational model of perceptual expectation effect based on neural coding principles

Hideyoshi Yanagisawa

doi:10.1111/joss.12233

A computational model of perceptual expectation effect based on neural coding principles

Journal of Sensory Studies ◽

10.1111/joss.12233 ◽

2016 ◽

Vol 31 (5) ◽

pp. 430-439 ◽

Cited By ~ 9

Author(s):

Hideyoshi Yanagisawa

Keyword(s):

Computational Model ◽

Neural Coding ◽

Expectation Effect

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A Computational Model of Human Perception With Prior Expectation: Bayesian Integration and Efficient Coding

Volume 1B: 35th Computers and Information in Engineering Conference ◽

10.1115/detc2015-46669 ◽

2015 ◽

Cited By ~ 1

Author(s):

Hideyoshi Yanagisawa

Keyword(s):

Computational Model ◽

Human Perception ◽

External Noise ◽

Physical Variable ◽

Sensory Stimuli ◽

Efficient Coding ◽

Psychological Phenomena ◽

Bayesian Integration ◽

Physical Variables ◽

Expectation Effect

Human perception of sensory stimuli is affected by prior prediction of the sensory experience. For example, perception of weight of an object changes depending on weight predicted with size of the object appearance. We call such psychological phenomena expectation effect. The expectation effect is a key factor to explain a gap between physical variables and their perceptions. In this paper, we propose a novel computational model of human perception involving the expectation effect. We hypothesized that perceived physical variable was estimated using a Bayesian integration of prior prediction and sensory likelihood of a physical variable. We applied efficient coding hypothesis to form a shape of sensory likelihood. We formalized the expectation effect as a function of three factors: expectation error (difference between predicted and actual physical variables), prediction uncertainty (variance of prior distributions), and external noise (variance of noise distributions convolved with likelihood). Using the model, we conducted computer simulations to analyze the behavior of two opposite patterns of expectation effect, that is, assimilation and contrast. The results of the simulation revealed that 1) the pattern of expectation effect shifted from assimilation to contrast as the prediction error increased, 2) uncertainty decreased the extent of the expectation effect, 3) and external noise increased the assimilation.

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Plasticity of the neural coding metaphor: An unnoticed rhetoric in scientific discourse

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900133x ◽

2019 ◽

Vol 42 ◽

Author(s):

Giulia Frezza ◽

Pierluigi Zoccolotti

Keyword(s):

Discourse Analysis ◽

Neural Coding ◽

Scientific Discourse ◽

Convincing Argument ◽

Unified View ◽

Brain Behavior

Abstract The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.

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