Abstract

Abstract: A variety of experimental studies suggest that sensory systems are capable of performing estimation or decision tasks at near-optimal levels. In this chapter, I explore the use of optimal estimation in describing sensory computations in the brain. I define what is meant by optimality and provide three quite different methods of obtaining an optimal estimator, each based on different assumptions about the nature of the information that is available to constrain the problem. I then discuss how biological systems might go about computing (and learning to compute) optimal estimates. The brain is awash in sensory signals. How does it interpret these signals, so as to extract meaningful and consistent information about the environment? Many tasks require estimation of environmental parameters, and there is substantial evidence that the system is capable of representing and extracting very precise estimates of these parameters. This is particularly impressive when one considers the fact that the brain is built from a large number of low-energy unreliable components, whose responses are affected by many extraneous factors (e.g., temperature, hydration, blood glucose and oxygen levels). The problem of optimal estimation is well studied in the statistics and engineering communities, where a plethora of tools have been developed for designing, implementing, calibrating and testing such systems. In recent years, many of these tools have been used to provide benchmarks or models for biological perception. Specifically, the development of signal detection theory led to widespread use of statistical decision theory as a framework for assessing performance in perceptual experiments. More recently, optimal estimation theory (in particular, Bayesian estimation) has been used as a framework for describing human performance in perceptual tasks.

Citations