Psychophysics (eBook)

FRONT COVER 1
PSYCHOPHYSICS: A PRACTICAL INTRODUCTION 4
COPYRIGHT PAGE 5
CONTENTS 8
PREFACE 12
ACKNOWLEDGEMENTS 14
ABOUT THE AUTHORS 16
CHAPTER 1. INTRODUCTION AND AIMS 18
1.1 What is Psychophysics? 18
1.2 Aims of the Book 19
1.3 Organization of the Book 19
1.4 Introducing Palamedes 20
1.4.1 Organization of Palamedes 22
1.4.2 Functions and Demonstration Programs in Palamedes 23
1.4.3 Error Messages in Palamedes 25
References 25
CHAPTER 2. CLASSIFYING PSYCHOPHYSICAL EXPERIMENTS 26
2.1 Introduction 26
2.2 Tasks, Methods, and Measures 27
2.3 Dichotomies 30
2.3.1 "Class A" Versus "Class B" Observations 30
2.3.2 "Objective" Versus "Subjective" 35
2.3.3 "Type 1" Versus "Type 2" 38
2.3.4 "Performance" Versus "Appearance" 39
2.3.5 "Forced-choice" Versus "Non-forced-choice" 43
2.3.6 "Criterion-free" Versus "Criterion-dependent" 46
2.3.7 "Detection" Versus "Discrimination" 47
2.3.8 "Threshold" Versus "Suprathreshold" 49
2.4 Classification Scheme 50
Further Reading 51
Exercises 52
References 52
CHAPTER 3. VARIETIES OF PSYCHOPHYSICAL PROCEDURE 56
3.1 Introduction 56
3.2 Performance-Based Procedures 58
3.2.1 Thresholds 58
3.2.2 Non-threshold Tasks Procedures 65
3.3 Appearance-Based Procedures 65
3.3.1 Matching 65
3.3.2 Scaling 68
3.4 Further Design Details 73
3.4.1 Method of Constant Stimuli 73
3.4.2 Adaptive Procedures 73
3.4.3 Timing of Stimulus Presentation 74
Further Reading 75
References 75
CHAPTER 4. PSYCHOMETRIC FUNCTIONS 76
4.1 Introduction 76
4.2 Section A: Practice 77
4.2.1 Overview of the Psychometric Function 77
4.2.2 Number of Trials and Stimulus Levels 78
4.2.3 Types and Choice of Function 81
4.2.4 Methods for Fitting Psychometric Functions 85
4.2.5 Estimating the Errors 87
4.2.6 Estimating the Goodness-of-Fit 89
4.2.7 Putting it All Together 90
4.3 Section B: Theory and Details 91
4.3.1 Psychometric Function Theories 91
4.3.2 Details of Function Types 98
4.3.3 Methods for Fitting Psychometric Functions 102
Further Reading 135
Exercises 135
References 135
CHAPTER 5. ADAPTIVE METHODS 138
5.1 Introduction 138
5.2 Up/Down Methods 139
5.2.1 Up/Down Method 139
5.2.2 Transformed Up/Down Method 141
5.2.3 Weighted Up/Down Method 142
5.2.4 Transformed and Weighted Up/Down Method 144
5.2.5 Termination Criteria and the Threshold Estimate 144
5.2.6 Up/Down Methods in Palamedes 145
5.2.7 Some Practical Tips 149
5.3 "Running Fit" Methods: The Best Pest and Quest 152
5.3.1 The Best PEST 152
5.3.2 Quest 154
5.3.3 Termination Criteria and Threshold Estimate 155
5.3.4 Running Fit Methods in Palamedes 155
5.3.5 Some Practical Tips 158
5.4 Psi Method 160
5.4.1 The Psi Method 160
5.4.2 Termination Criteria and the Threshold and Slope Estimates 164
5.4.3 The Psi Method in Palamedes 165
5.4.4 Some Practical Tips 166
Exercises 167
References 167
CHAPTER 6. SIGNAL DETECTION MEASURES 170
6.1 Introduction 171
6.1.1 What is Signal Detection Theory (SDT)? 171
6.1.2 A Recap on Some Terminology: N, m and M 171
6.1.3 Why Measure d'? 173
6.2 Section A: Practice 175
6.2.1 Signal Detection Theory with Palamedes 175
6.2.2 Converting Pc to d' for Unbiased M-AFC Tasks 176
6.2.3 Measuring d' for 1AFC Tasks 177
6.2.4 Measuring d' for 2AFC Tasks with Observer Bias 180
6.2.5 Measuring d' for Same-Different Tasks 181
6.2.6 Measuring d' for Match-to-Sample Tasks 184
6.2.7 Measuring d' for M-AFC Oddity Tasks 185
6.2.8 Estimating Pc[sub(max)] with Observer Bias 186
6.2.9 Comparing d's and Pcs across Different Tasks 186
6.3 Section B: Theory 187
6.3.1 Relationship Between Z-scores and Probabilities 187
6.3.2 Calculation of d' for M-AFC 189
6.3.3 Calculation of d' and Measures of Bias for 1AFC Tasks 191
6.3.4 Calculation of d' for Unbiased and Biased 2AFC Tasks 195
6.3.5 Calculation of d' for Same-Different Tasks 198
6.3.6 Calculation of d' for Match-to-Sample Tasks 203
6.3.7 Calculation of d' for M-AFC Oddity Tasks 204
Further Reading 205
Exercises 205
References 206
CHAPTER 7. SCALING METHODS 208
7.1 Introduction 208
7.2 Section A: Practice 210
7.2.1 Maximum Likelihood Difference Scaling (MLDS) 210
7.3 Section B: Theory 217
7.3.1 How MLDS Works 217
7.3.2 Perceptual Scales and Internal Noise 219
7.3.3 Partition Scaling 222
Further Reading 224
Exercise 224
CHAPTER 8. MODEL COMPARISONS 226
8.1 Introduction 226
8.2 Section A: Statistical Inference 229
8.2.1 Standard Error Eyeballing 229
8.2.2 Model Comparisons 232
8.2.3 Other Model Comparisons 240
8.2.4 Goodness-of-Fit 243
8.2.5 More Than Two Conditions 246
8.3 Section B: Theory and Details 253
8.3.1 The Likelihood Ratio Test 253
8.3.2 Simple Example: Fairness of Coin 253
8.3.3 Composite Hypotheses 256
8.3.4 Specifying Models Using Contrasts 259
8.3.5 A Note on Failed Fits 266
8.4 Some Alternative Model Comparison Methods 267
8.4.1 Information Criteria: AIC and BIC 267
8.4.2 Bayes Factor and Posterior Odds 270
Further Reading 271
Exercises 271
References 272
QUICK REFERENCE GUIDE 274
ACRONYMS 290
INDEX 292
A 292
B 293
C 293
D 293
F 293
G 293
H 293
I 293
J 293
L 293
M 293
N 294
O 294
P 294
Q 295
R 295
S 295
T 296
U 296
W 296
Z 296
COLOR PLATES 297