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This chapter provides the necessary background for the study of visual pattern analyzers (e.g., spatial frequency and orientation-selective channels, direction-selective mechanisms). It briefly describes known properties of neurons in cortical areas V1 and V2, which may be the physiological substrate for the visual pattern analyzers. It introduces seventeen spatiotemporal dimensions of pattern vision along which neurons, and visual patterns and analyzers can differ. Several psychophysical paradigms are introduced that use near-threshold visual patterns. The chapter describes the models appropriate for interpreting results from these near-threshold paradigms and speculates about why these results have been particularly useful in uncovering the multiple-analyzer stage of visual processing.

This chapter presents a review of the development of quantum-statistical theories for visual functions such as absolute thresholds for steady and moving targets, contrast thresholds, visual acuity, adaptation, and color identification. Soon after the corpuscular nature of light was discovered by Planck, the minimum perceptible amount of light for human vision was compared with the energy of the quantum of light. At about the same time as the discovery of the quantum, fairly accurate measurements were made by different investigators of this minimum perceptible. It is argued in this chapter that, for each state of adaptation, the nerve system in the retina sets an organizational pattern through which action potentials can find their way to the higher centers. Instability phenomena in color identification and dependence of visual acuity on brightness are also discussed with reference to quantum-statistical approaches.

Observer models specify computations that are sufficient to predict the behavior of an observer for many different input stimuli with a small number of parameters. Observer models specify the transformations leading to the relevant internal representations from the stimulus, and define the decision rules for particular tasks. These models have a remarkable ability to summarize compactly the behavioral outcomes in many conditions and provide a conceptual framework within which to understand the responses of the sensory system. In this chapter, we consider both modern single-channel and multichannel observer models. Each observer in a task can be described by a small number of parameters that fully specify how the stimulus is recoded in an internal response and then subjected to a task-relevant decision. Once these parameters have been estimated from specific experimental tests, it is possible to make predictions about an observer’s responses to a wide range of stimuli and paradigms.