Chris R. Sims

The fundamental goal of perception is to aid in the achievement of behavioral objectives. This requires extracting and communicating useful information from noisy and uncertain sensory signals. At the same time, given the complexity of sensory information and the limitations of biological information processing, it is necessary that some information must be lost or discarded in the act of perception. Under these circumstances, what constitutes an 'optimal' perceptual system? This paper describes the mathematical framework of rate-distortion theory as the optimal solution to the problem of minimizing the costs of perceptual error subject to strong constraints on the ability to communicate or transmit information. Rate-distortion theory offers a general and principled theoretical framework for developing computational-level models of human perception (Marr, 1982). Models developed in this framework are capable of producing quantitatively precise explanations for human perceptual performance, while yielding new insights regarding the nature and goals of perception. This paper demonstrates the application of rate-distortion theory to two benchmark domains where capacity limits are especially salient in human perception: discrete categorization of stimuli (also known as absolute identification) and visual working memory. A software package written for the R statistical programming language is described that aids in the development of models based on rate-distortion theory.