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Psychophysics : a practical introduction /
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 mi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Elsevier Academic Press,
2016.
|
| Edición: | Second edition. |
| Colección: | Elsevier science & technology books
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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<U+0017>
- 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<U+0017>
- 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.
- 6.3.4.2 d2 for Biased 2AFC
- 6.3.4.3 Pcmax for Biased 2AFC
- 6.3.5 Calculation of d2 for Same-Different Tasks
- 6.3.5.1 d2 for a 2AFC Same-Different
- 6.3.5.2 d2 for a 1AFC Same-Different: Differencing Model
- 6.3.5.3 d2 for a 1AFC Same-Different: Independent Observation Model
- 6.3.6 Calculation of d2 for Match-to-Sample Tasks
- 6.3.6.1 Independent Observation Model
- 6.3.6.2 Differencing Model
- 6.3.7 Calculation of d2 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<U+0017>
- 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.
- 8
- Scaling Methods<U+0017>
- 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<U+0017>
- 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.