May K, 2011, "Bayesian decoding of neural population responses explains many characteristics of contrast detection and discrimination" Perception 40 ECVP Abstract Supplement, page 51
Bayesian decoding of neural population responses explains many characteristics of contrast detection and discrimination
Contrast thresholds for detecting a signal added to a pedestal generate a "dipper function", which first falls as the pedestal contrast increases from zero, and then rises to give a "near-miss" to Weber's law. The psychometric functions are well-fitted by Weibull functions: the slope parameter, beta, is about 3 for zero pedestal (detection), and falls to around 1.3 with increasing pedestal. All of this can be explained by Bayesian decoding of a population of neurons with Naka–Rushton contrast-response functions [r=rmax*cq/(c50q+cq)], and a rectangular distribution of semi-saturation contrasts, c50, along the log contrast axis. I derive equations that accurately predict the model's performance and give insights into why it behaves this way. For Poisson-spiking neurons, the model's detection psychometric function is a Weibull function with beta equal to the Naka–Rushton exponent, q, which physiologically often takes a value of about 3; for high pedestals, beta is always about 1.3, regardless of the model parameters. As contrast increases from zero towards the bottom of the c50 range, the threshold dips; within the c50 range, Weber's law holds if rmax is constant across the neural population; a shallower/decreasing slope occurs if rmax is scaled to give a fixed response to 100% contrast.
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