The visual system must extract from the light that falls on the retina meaningful information about what is where in our environment. At an early stage it analyzes the incoming sensory data along many dimensions of pattern vision, e.g. spatial frequency, orientation, velocity, eye-of-origin. Visual Pattern Analyzers provides a definitive account of current knowledge about this stage of visual processing. Nowhere else can such a comprehensive summarty of the lower level pattern analyzers be found. The book's emphasis is on psychophysical experiments measuring the detection and identification of near-threshold patterns — and the mathematical models, such as multidimensional signal-detection theory, used to draw inferences from such experimental results — but neurophysiological evidence is presented and compared critically to the psychophysical evidence. Introductory material on psychophysical methods, signal detection theory, and the mathematics of Fourier analysis is given in order to make the book more accessible to all who are interested in the lower or higher levels of visual perception. This volume will be of great value to researchers and graduate students in the fields of vision and perception. Within the scientific community there is wide interest in the visual system, and the book will be of use to investigators in many fields, including psychophysics, neuroscience, ophthalmology and optics, computer science, and cognitive and experimental psychology.

"The author, a respected scholar in this field, has done an excellent job of organizing and summarizing a massive amount of data. This reviewer knows of no other work that has attempted such an exhaustive compilation of the studies in this field . . . .A clearly written and helpfully illustrated, but highly technical work." --Choice

PART I: Introduction

1. Neurophysiology and Psychophysics

2. Some Mathematics

PART II: Adaption

3. Models of Selective Effects

PART III: Summation

4. Models for Far-Apart Values

5. Far-Apart Values on Spatial Dimensions

6. Close Values on Spatial Dimensions

PART IV: Uncertainty

7. Extrinsic Uncertainty and Summation Revisited

8. Intrinsic Uncertainty and Transducer Functions

PART V: Identification

9. Discrimination

10. Three More Paradigms and Transducer Functions

PART VI: Multiple Dimensions

11. Some General Considerations

12. Results of Analyzer-Revealing Experiments

13. Results of Parametric Experiments

Appendix

Assumptions According to Function in Multiple-Analyzers Models

Definitions of Assumptions in Sequential Order

References

Index of Assumptions

Index