Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise

Johannes Burge, Priyank Jaini

Abstract: Author Summary In psychophysics and neurophysiology, the stimulus features that are manipulated in experiments are often selected based on intuition, trial-and-error, and historical precedence. Accuracy Maximization Analysis (AMA) is a Bayesian ideal observer method for determining the task-relevant features (i.e. filters) from natural stimuli that nervous systems should select for. In other words, AMA is a method for finding optimal receptive fields for specific tasks. Early results suggest that this method has the potential to be of fundamental importance to neuroscience and perception science. First, we develop AMA-SGD, a new version of AMA that significantly reduces filter-learning time, and use it to learn optimal filters for the classic task of binocular disparity estimation. Then, we find that measureable, task-relevant properties of natural stimuli are the most important determinants of the optimal filters; changes to the prior, cost function, and internal noise have little effect on the filters. Last, we demonstrate that some ubiquitous properties of neural systems, generally thought to be biophysical nuisances, can actually improve the fidelity of neural codes. In particular, we show for the first time that scaled additive noise and redundant (non-orthogonal) filters can interact to sculpt uncertainty due to internal noise to match task-irrelevant natural stimulus variability.