| Description |
1 online resource |
| Series |
Elsevier science & technology books |
| Contents |
880-01 Front Cover -- IFC -- PSYCHOPHYSICS: A PRACTICAL INTRODUCTION -- Copyright -- Dedication -- Contents -- About the Authors -- Preface to the Second Edition -- Acknowledgments -- 1 -- Introduction and Aims -- 1.1 WHAT IS PSYCHOPHYSICS? -- 1.2 AIMS OF THE BOOK -- 1.3 ORGANIZATION OF THE BOOK -- 1.4 WHAT'S NEW IN THE SECOND EDITION? -- References -- 2 -- Classifying Psychophysical Experiments∗ -- 2.1 INTRODUCTION -- 2.2 TASKS, METHODS, AND MEASURES -- 2.3 DICHOTOMIES -- 2.3.1 "Class A" versus "Class B" Observations -- 2.3.2 "Type 1" versus "Type 2" -- 2.3.3 "Performance" versus "Appearance" -- 2.3.4 "Forced-Choice" versus "Nonforced-Choice" -- 2.3.5 "Criterion-Free" versus "Criterion-Dependent" -- 2.3.6 "Objective" versus "Subjective" -- 2.3.7 "Detection" versus "Discrimination" -- 2.3.8 "Threshold" versus "Suprathreshold" -- 2.4 CLASSIFICATION SCHEME -- FURTHER READING -- EXERCISES -- References -- 3 -- Varieties of Psychophysical Procedures∗ -- 3.1 INTRODUCTION -- 3.2 PERFORMANCE-BASED PROCEDURES -- 3.2.1 Thresholds -- 3.2.1.1 Forced-Choice Threshold Procedures -- 3.2.1.1.1 N=1 (ONE STIMULUS PER TRIAL) -- METHOD OF LIMITS -- YES/NO -- SYMMETRIC -- 3.2.1.1.2 N=2 -- STANDARD 2AFC/2IFC -- 1AFC SAME-DIFFERENT -- 3.2.1.1.3 N=3 -- 3AFC ODDITY -- 2AFC MATCH-TO-SAMPLE -- 3.2.1.1.4 N=4 -- 2AFC/2IFC SAME-DIFFERENT -- 3.2.1.1.5 N 4 -- M-AFC TASKS -- 3.2.1.2 Nonforced-Choice Thresholds -- 3.2.1.2.1 METHOD OF ADJUSTMENT -- 3.2.2 Nonthreshold Tasks and Procedures -- 3.2.2.1 Accuracies and Reaction Times -- 3.3 APPEARANCE-BASED PROCEDURES -- 3.3.1 Matching -- 3.3.1.1 Forced-Choice Matching -- 3.3.1.1.1 N=2: MATCHING USING 2AFC/2IFC -- 3.3.1.2 Nonforced-Choice Matching -- 3.3.1.2.1 N=2: MATCHING BY ADJUSTMENT -- 3.3.1.2.2 N=2: NULLING BY ADJUSTMENT -- 3.3.2 Scaling -- 3.3.2.1 Types of Perceptual Scale -- 3.3.2.2 Forced-Choice Scaling Procedures |
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880-01/(S References -- 5 -- Adaptive Methods∗ -- 5.1 INTRODUCTION -- 5.2 UP/DOWN METHODS -- 5.2.1 Up/Down Method -- 5.2.2 Transformed Up/Down Method -- 5.2.3 Weighted Up/Down Method -- 5.2.4 Transformed and Weighted Up/Down Method -- 5.2.5 Termination Criteria and the Threshold Estimate -- 5.2.6 Some Practical Tips -- 5.3 "RUNNING FIT" METHODS: THE BEST PEST AND QUEST -- 5.3.1 The Best PEST -- 5.3.2 Quest -- 5.3.3 Termination Criteria and Threshold Estimate -- 5.3.4 Some Practical Tips -- 5.4 THE PSI METHOD AND VARIATIONS -- 5.4.1 The Psi Method -- 5.4.2 Termination Criteria and the Threshold and Slope Estimates -- 5.4.3 Some Practical Tips -- 5.4.4 Psi-Method Variations -- EXERCISES -- References -- 6 -- Signal Detection Measures∗ -- 6.1 INTRODUCTION -- 6.1.1 What is Signal Detection Theory (SDT)-- 6.1.2 A Recap on Some Terminology: N, m and, M -- 6.1.3 Why Measure d′-- 6.2 SECTION A: PRACTICE -- 6.2.1 Basic Assumptions -- 6.2.2 Converting Pc to d′ for Standard M-AFC Tasks -- 6.2.3 Measuring d′ for 1AFC Tasks -- 6.2.4 Performing a Rating Scale Experiment with 1AFC -- 6.2.5 Measuring d′ for 2AFC Tasks with Observer Bias -- 6.2.6 Measuring d′ for Same-Different Tasks -- 6.2.7 Measuring d′ for Match-to-Sample Tasks -- 6.2.8 Measuring d′ for M-AFC Oddity Tasks -- 6.2.9 Estimating Pcmax with Observer Bias -- 6.2.10 Comparing Pcs from d′s Across Different Tasks -- 6.2.11 Modeling Psychometric Functions with SDT -- 6.3 SECTION B: THEORY -- 6.3.1 Relationship Between z-Scores and Probabilities -- 6.3.2 Calculation of d′ for M-AFC -- 6.3.3 Calculation of d′ and Measures of Bias for 1AFC Tasks -- 6.3.3.1 d′ for 1AFC -- 6.3.3.2 Criterion C for 1AFC -- 6.3.3.3 Criterion lnβ for 1AFC -- 6.3.3.4 Criterion C′ for 1AFC -- 6.3.3.5 Pcmax for 1AFC -- 6.3.4 Calculation of d′ for Unbiased and Biased 2AFC Tasks -- 6.3.4.1 Alternative Calculation of d′ for Unbiased 2AFC |
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6.3.4.2 d′ for Biased 2AFC -- 6.3.4.3 Pcmax for Biased 2AFC -- 6.3.5 Calculation of d′ for Same-Different Tasks -- 6.3.5.1 d′ for a 2AFC Same-Different -- 6.3.5.2 d′ for a 1AFC Same-Different: Differencing Model -- 6.3.5.3 d′ for a 1AFC Same-Different: Independent Observation Model -- 6.3.6 Calculation of d′ for Match-to-Sample Tasks -- 6.3.6.1 Independent Observation Model -- 6.3.6.2 Differencing Model -- 6.3.7 Calculation of d′ for M-AFC Oddity Tasks -- 6.3.7.1 Differencing Model -- 6.3.7.2 Independent Observation Model -- FURTHER READING -- EXERCISES -- References -- 7 -- Summation Measures∗ -- 7.1 INTRODUCTION -- 7.1.1 Summation Types, Scenarios, and Frameworks -- 7.2 PART A: SUMMATION MODELED UNDER SIGNAL DETECTION THEORY (SDT) -- 7.2.1 Preliminaries -- 7.2.2 Additive Summation under SDT -- 7.2.2.1 Equations for Additive Summation -- 7.2.2.2 Many versus One with Additive Summation -- 7.2.2.3 Expressing Summation Using the Minkowski Formula -- 7.2.3 Probability Summation under SDT -- 7.2.3.1 Equations for Probability Summation -- 7.2.3.2 Applying the PSSDT Functions -- 7.2.3.3 Many versus One with Probability Summation -- 7.2.4 Using the SDT Summation Formulae -- 7.2.4.1 Modeling Summation with Simulated Psychometric Functions -- 7.2.4.2 Simulating Summation Squares -- 7.2.4.3 Working with Actual Psychometric Function Data -- 7.3 PART B: SUMMATION MODELED UNDER HIGH-THRESHOLD THEORY (HTT) -- 7.3.1 Probability Summation under HTT -- 7.3.1.1 A Simple Coin Tossing Exercise -- 7.3.1.2 Proportion Correct in Forced-Choice Tasks under HTT -- 7.3.1.3 Summation Psychometric Functions under HTT -- 7.3.1.4 Many versus One with Probability Summation under HTT -- 7.3.1.5 Quick Pooling Formula for Probability Summation under HTT -- 7.3.2 Additive Summation under HTT -- 7.3.2.1 Many versus One with Additive Summation under HTT -- FURTHER READING -- References |
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8 -- Scaling Methods∗ -- 8.1 INTRODUCTION -- 8.2 DISCRIMINATION SCALES -- 8.2.1 Fechner's Integration of Weber's Law -- 8.2.2 The Dipper Function -- 8.2.3 Limitations of Discrimination Scales -- 8.3 MAXIMUM LIKELIHOOD DIFFERENCE SCALING (MLDS) -- 8.3.1 How MLDS Works -- 8.3.2 MLDS Applied to Paired Comparisons -- 8.3.3 MLDS and Internal Noise -- 8.4 PARTITION SCALING -- FURTHER READING -- EXERCISE -- References -- 9 -- Model Comparisons∗ -- 9.1 INTRODUCTION -- 9.2 SECTION A: STATISTICAL INFERENCE -- 9.2.1 Standard Error Eyeballing -- 9.2.2 Model Comparisons -- 9.2.2.1 The Underlying Logic -- 9.2.3 Other Model Comparisons -- 9.2.3.1 Effect on Threshold -- 9.2.3.2 Effect on Slope -- 9.2.4 Goodness-of-Fit -- 9.2.5 More than Two Conditions -- 9.3 SECTION B: THEORY AND DETAILS -- 9.3.1 The Likelihood Ratio Test -- 9.3.2 Simple Example: Fairness of Coin -- 9.3.3 Composite Hypotheses -- 9.3.4 Specifying Models Using Reparameterization -- 9.3.4.1 Linear Contrasts -- 9.3.4.1.1 EXAMPLE: TREND ANALYSIS -- 9.3.4.1.2 EXAMPLE: PAIRWISE COMPARISONS -- 9.3.4.2 Nonlinear Reparameterizations -- 9.3.5 A Note on Failed Fits -- 9.3.6 Some Cautionary Words Regarding the Interpretation of p-Values -- 9.4 SOME ALTERNATIVE MODEL COMPARISON METHODS -- 9.4.1 Information Criteria: AIC and BIC -- 9.4.2 Bayes Factor and Posterior Odds -- FURTHER READING -- EXERCISES -- References -- Quick Reference Guide -- List of Acronyms -- Index -- A -- B -- C -- D -- F -- G -- H -- I -- J -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- W -- Z -- Back Cover |
| Summary |
Psychophysics: A Practical Introduction, Second Edition, is the primary scientific tool for understanding how the physical world of colors, sounds, odors, movements, and shapes translates into the sensory world of sight, hearing, touch, taste, and smell; in other words, how matter translates into mind. This timely revision provides a unique introduction to the techniques for researching and understanding how the brain translates the external physical world to the internal world of sensation. The revision expands and refines coverage of the basic tools of psychophysics research and better integrates the theory with the supporting software. The new edition continues to be the only book to combine, in a single volume, the principles underlying the science of psychophysical measurement and the practical tools necessary to analyze data from psychophysical experiments. The book, written in a tutorial style, will appeal to new researchers as well as to seasoned veterans. This introduction to psychophysics research methods will be of interest to students, scholars and researchers within sensory neuroscience, vision research, behavioral neuroscience, and the cognitive sciences |
| Bibliography |
Includes bibliographical references at the end of each chapters and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed March 15, 2016) |
| Subject |
Psychophysics.
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Psychophysiology -- Methodology
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Psychophysics -- methods
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Psychophysics
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PSYCHOLOGY -- Physiological Psychology.
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Psychophysics
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Psychophysiology -- Methodology
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Methode
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Psychophysik
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| Form |
Electronic book
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| Author |
Prins, Nicolaas, author
|
| ISBN |
9780080993812 |
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0080993818 |
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0124071562 |
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9780124071568 |
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