Contents -- Part I. INTRODUCTION -- 1. Neurophysiology and Psychophysics -- 1.1 Two Themes-Pattern Vision and Analyzers -- 1.2 Analyzers in Color Vision as an Example -- 1.3 Two Cautions About Analyzers -- 1.4 Terminology-Responses Versus Outputs -- 1.5 Models of Near-Threshold Pattern Vision-An Overview -- 1.6 Neurophysiology and Pattern Vision -- 1.7 Spatial Characteristics of Visual Neurons -- 1.8 Temporal Characteristics of Visual Neurons -- 1.9 Other Characteristics of Visual Neurons -- 1.10 Some Terminology: Analyzer, Neuron, Mechanism, Channel, and Element -- 1.11 Five Psychophysical Paradigms -- 1.12 Some Practical Matters -- 1.13 Summary -- Notes -- 2. Some Mathematics -- 2.1 Sinusoids and Fourier Analysis -- 2.2 Lines and Points and Impulses (Delta Functions) -- 2.3 Windowed Sine Waves-Gabor Functions -- 2.4 The Fourier Transform of a Sinusoidal Patch -- 2.5 Linear Systems and Points -- 2.6 Linear Systems and Sines -- 2.7 How Stimulus Decompositions are Useful -- 2.8 Selectivity of Analyzers -- 2.9 Summary -- 2.10 Appendix. Fourier Transforms of Sinusoidal, Delta, Gaussian, and Gabor Functions -- Part II. ADAPTATION -- 3. Models of Selective Effects -- 3.1 A Typical Adaptation Experiment -- 3.2 A Simple Fatigue Model -- 3.3 The General Stiles Model -- 3.4 Empirical Discrepancies in Spatial-Frequency Adaptation -- 3.5 Some Stiles-Type Models Assuming Many Analyzers -- 3.6 Even More General Fatigue Models -- 3.7 Inhibition Plus Fatigue -- 3.8 Inhibition Only -- 3.9 Point-by-Point Fatigue (Afterimage) Explanations -- 3.10 What Is the Function of Pattern-Selective Adaptation? -- 3.11 Summary -- Notes -- Part III. SUMMATION -- 4. Models for Far-Apart Values -- 4.1 A Typical Summation Experiment -- 4.2 An Additive Single-Analyzer Model -- 4.3 A Nonadditive, Uniform, Single-Analyzer Model (a Single-Channel Model).

4.4 Multiple-Analyzers Model -- 4.5 A Single Nonuniform Channel (Example of Interaction Between Two Dimensions) -- 4.6 Multiple-Analyzers Models Incorporating Variability -- 4.7 High-Threshold Multiple-Analyzers Model -- 4.8 Quick Pooling Model -- 4.9 Quick Pooling Model Predictions for Summation of Far-Apart Components -- 4.10 Summary -- 4.11 Appendix. Derivation of Observable Quick Pooling Formulas -- Notes -- 5. Far-Apart Values on Spatial Dimensions -- 5.1 Overview -- 5.2 Far-Apart Orientations -- 5.3 The Effect of Probability Summation Across Space on Spatial-Frequency and Orientation Models -- 5.4 Summation of Far-Apart Spatial Positions -- 5.5 Summation Experiments on the Spatial-Extent Dimension -- 5.6 Summation Experiments on the Spatial-Phase Dimension -- 5.7 Summary -- Notes -- 6. Close Values on Spatial Dimensions -- 6.1 Overview -- 6.2 Two Additive, Deterministic Analyzers (the Naive Model) -- 6.3 More Sophisticated Multiple-Analyzers Models -- 6.4 Summation of Close Spatial Frequencies -- 6.5 Summation of Close Orientations -- 6.6 Summation of Close Spatial Positions -- 6.7 Summation of Close Spatial Frequencies-Interaction with Spatial Extent -- 6.8 Summary -- 6.9 Appendix. Details of Two Multiple-Mechanisms Models -- Notes -- Part IV. UNCERTAINTY -- 7. Extrinsic Uncertainty and Summation Revisited -- 7.1 Introduction -- 7.2 Single-Attention-Band Models -- 7.3 Independent-Analyzers (Attention-Sharing, Noise-Limited, Multiple-Band) Models -- 7.4 Predictions of Independent-Analyzers Models -- 7.5 Summary -- 7.6 Appendix. Calculating the Independent-Analyzers Predictions -- Notes -- 8. Intrinsic Uncertainty and Transducer Functions -- 8.1 Intrinsic-Uncertainty Version of Independent-Analyzers Model -- 8.2 Adding Analyzers' Transducer Functions to Independent-Analyzers Models -- 8.3 High-Threshold Version with Transducer Function.

8.4 Gaussian Version with Power Function Transducer -- 8.5 The Quick Pooling Formula without a High Threshold -- 8.6 Intrinsic-Uncertainty Version with Linear Microanalyzer Transducer -- 8.7 A Physiological Aside-The Probability Distribution of a Single Neurons' Outputs -- 8.8 Attentional Control and Individual Differences -- 8.9 Summary -- 8.10 Appendix -- Notes -- Part V. IDENTIFICATION -- 9. Discrimination -- 9.1 Introduction -- 9.2 Discrimination and Classification Paradigms -- 9.3 Review of Previous Assumptions of Independent-Analyzers Models -- 9.4 Classification Decision Rules -- 9.5 Discrimination Decision Rules -- 9.6 Composite Analyzer Composed of Multiple Entities -- 9.7 Predictions of Some Multiple-Analyzers Models for Discrimination -- 9.8 Distance (Nonprobabilistic, Geometric, Vector) Models -- 9.9 Near-Threshold Discrimination of Spatial Frequency -- 9.10 Summary -- 9.11 Appendix. About Vectors -- Notes -- 10. Three More Paradigms and Transducer Functions -- 10.1 Simple Detection and Identification Paradigm -- 10.2 2 × 2 Paradigm -- 10.3 Predictions of Independent-Analyzers Models for 2 × 2 Paradigm -- 10.4 Concurrent Paradigm -- 10.5 Predictions for Concurrent Experiments -- 10.6 Comparison of Independent-Analyzers Models' Predictions with Spatial-Frequency Results -- 10.7 Predictions of More General Independent-Analyzers Models -- 10.8 Adding a Transducer Function -- 10.9 Summary -- 10.10 Appendix. Methods for Calculating Predictions -- Notes -- Part VI. MULTIPLE DIMENSIONS -- 11. Some General Considerations -- 11.1 Review and Preview -- 11.2 Doubly versus Singly Selective Analyzers -- 11.3 Covariations Hidden by Broad-Band Stimuli on a Nonexperimental Dimension -- 11.4 Separability -- 11.5 Parametric Contrast Sensitivity Experiments -- 11.6 How Visual Pattern Analyzers Might Exist along 17 Dimensions.

11.7 Interrelationships among Pattern Dimensions -- 11.8 Special Points about Interpreting Experiments on Each Pattern Dimension -- 11.9 Summary -- 12. Results of Analyzer-Revealing Experiments -- 12.1 Introduction -- 12.2 Analyzers on Spatial Dimensions -- 12.3 Analyzers on Temporal Dimensions -- 12.4 The Other Dimensions -- 12.5 About Discrepancies between Identification and Adaptation/Summation Results -- 12.6 Correlation and Inhibition -- 12.7 An Aside about Physiology-Selective Sensitivity along Visual Pattern Dimensions -- 12.8 Summary -- 12.9 Description of List of References to Analyzer-Revealing Experiments -- 12.10 The List of References to Analyzer-Revealing Experiments -- Notes -- 13. Results of Parametric Experiments -- 13.1 Introduction -- 13.2 Sensitivity as a Function of Spatial Frequency -- 13.3 Sensitivity as a Function of Orientation -- 13.4 Sensitivity as a Function of Spatial Position -- 13.5 Sensitivity as a Function of Spatial Extent and Spatial Phase -- 13.6 Sensitivity as a Function of Temporal Frequency -- 13.7 Sensitivity on the Other Temporal Dimensions -- 13.8 Performance as a Function of Contrast -- 13.9 Sensitivity as a Function of Mean Luminance -- 13.10 Sensitivity Is Generally the Same for the Two Eyes -- 13.11 Effects of Other Factors -- 13.12 Equipment Including Surrounds -- 13.13 An Aside about Physiology-Possible Substrates for Parametric Sensitivity -- 13.14 Summary -- 13.15 Description of List of References to Parametric Experiments -- 13.16 The List of References to Parametric Experiments -- Note -- Appendix -- Assumptions According to Function in Multiple-Analyzers Models -- Definitions of Assumptions in Sequential Order -- References -- Index of Assumptions -- A -- B -- C -- D -- E -- F -- G -- H -- I -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y -- Index -- A -- B -- C -- D -- E -- F -- G.

H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.