Psychophysics
A Practical Introduction
- 1st Edition - September 29, 2009
- Authors: Frederick A.A. Kingdom, Nicolaas Prins
- Language: English
Psychophysics: A Practical Application is a single-volume text that covers the rudimentary principles of psychophysical methods and the practical tools that are important for… Read more
Description
Description
Psychophysics: A Practical Application is a single-volume text that covers the rudimentary principles of psychophysical methods and the practical tools that are important for processing data from psychophysical experiments and tests. It makes complicated concepts and procedures understandable for beginners and non-experts in psychophysics. The book includes a wide array of analytical techniques, such as novel classification schemes for psychophysics experiments; new software packages for collecting and processing psychophysical data; practical tips for designing psychophysical experiments; and the advantages and disadvantages of the different psychophysical methods. The first chapters of the book present the fundamental concepts and terminology of psychophysics, and they familiarize readers with available psychophysical techniques. The remaining chapters discuss a series of topics, such as psychometric functions, adaptive procedures, signal detection measures, scaling methods, and statistical model comparisons. The book serves as an invaluable source of information about psychophysics for researchers and optometrists, as well as for psychology and neuroscience students, on both the graduate and undergraduate level.
Key features
Key features
- Large variety of analytical methods explained for the non-expert
- Novel classification scheme for psychophysics experiments
- New software package for collecting and analyzing psychophysical data
- Pros and cons of different psychophysical procedures
- Practical tips for designing psychophysical experiments
Readership
Readership
Researchers, graduate students, and post-doctorates in perception research in neuroscience, psychology, cognition; optometrists.
Table of contents
Table of contents
Preface
Acknowledgements
About the Authors
1. Introduction and Aims
1.1 What is Psychophysics?
1.2 Aims of the Book
1.3 Organization of the Book
1.4 Introducing Palamedes
1.4.1 Organization of Palamedes
1.4.2 Functions and Demonstration Programs in Palamedes
1.4.3 Error Messages in Palamedes
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 "Objective" versus "Subjective"
2.3.3 "Type 1" versus "Type 2"
2.3.4 "Performance" versus "Appearance"
2.3.5 "Forced-choice" versus "Non-forced-choice"
2.3.6 "Criterion-free" versus "Criterion-dependent"
2.3.7 "Detection" versus "Discrimination"
2.3.8 "Threshold" versus "Suprathreshold"
2.4 Classification Scheme
Further Reading
Exercises
References
3. Varieties of Psychophysical Procedure
3.1 Introduction
3.2 Performance-Based Procedures
3.2.1 Thresholds
3.2.2 Non-threshold Tasks Procedures
3.3 Appearance-Based Procedures
3.3.1 Matching
3.3.2 Scaling
3.4 Further Design Details
3.4.1 Method of Constant Stimuli
3.4.2 Adaptive Procedures
3.4.3 Timing of Stimulus Presentation
Further Reading
References
4. Psychometric Functions
4.1 Introduction
4.2 Section A: Practice
4.2.1 Overview of the Psychometric Function
4.2.2 Number of Trials and Stimulus Levels
4.2.3 Types and Choice of Function
4.2.4 Methods for Fitting Psychometric Functions
4.2.5 Estimating the Errors
4.2.6 Estimating the Goodness-of-Fit
4.2.7 Putting it All Together
4.3 Section B: Theory and Details
4.3.1 Psychometric Function Theories
4.3.2 Details of Function Types
4.3.3 Methods for Fitting Psychometric Functions
Further Reading
Exercises
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 Up/Down Methods in Palamedes
5.2.7 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 Running Fit Methods in Palamedes
5.3.5 Some Practical Tips
5.4 Psi Method
5.4.1 The Psi Method
5.4.2 Termination Criteria and the Threshold and Slope Estimates
5.4.3 The Psi Method in Palamedes
5.4.4 Some Practical Tips
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 Signal Detection Theory with Palamedes
6.2.2 Converting Pc to d’ for Unbiased M-AFC Tasks
6.2.3 Measuring d’ for 1AFC Tasks
6.2.4 Measuring d’ for 2AFC Tasks with Observer Bias
6.2.5 Measuring d’ for Same-Different Tasks
6.2.6 Measuring d’ for Match-to-Sample Tasks
6.2.7 Measuring d’ for M -AFC Oddity Tasks
6.2.8 Estimating Pcmax with Observer Bias
6.2.9 Comparing d’s and Pcs across Different Tasks
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
6.3.4 Calculation of d’ for Unbiased and Biased 2AFC Tasks
6.3.5 Calculation of d’ for Same-Different Tasks
6.3.6 Calculation of d’ for Match-to-Sample Tasks
6.3.7 Calculation of d for M -AFC Oddity Tasks
Further Reading
Exercises
References
7. Scaling Methods
7.1 Introduction
7.2 Section A: Practice
7 .2.1 Maximum Likelihood Difference Scaling (MLDS)
7.3 Section B: Theory
7.3.1 How MLDS Works
7.3.2 Perceptual Scales and Internal Noise
7.3.3 Partition Scaling
Further Reading
Exercise
8. Model Comparisons
8.1 Introduction
8.2 Section A: Statistical Inference
8.2.1 Standard Error Eyeballing
8.2.2 Model Comparisons
8.2.3 Other Model Comparisons
8.2.4 Goodness-of-Fit
8.2.5 More Than Two Conditions
8.3 Section B: Theory and Details
8.3.1 The Likelihood Ratio Test
8.3.2 Simple Example: Fairness of Coin
8.3.3 Composite Hypotheses
8.3.4 Specifying Models Using Contrasts
8.3.5 A Note on Failed Fits
8.4 Some Alternative Model Comparison Methods
8.4.1 Information Criteria: AIC and BIC
8.4.2 Bayes Factor and Posterior Odds
Further Reading
Exercises
References
Quick Reference Guide
Acronyms
Index
Product details
Product details
- Edition: 1
- Published: September 29, 2009
- Language: English
About the authors
About the authors
FK
Frederick A.A. Kingdom
Frederick A. A. Kingdom, PhD, is Professor Emeritus in the Department of Ophthalmology at McGill University. His research focuses on visual perception, encompassing a broad range of topics in human vision and sensory processing. Before joining McGill University in 1990, he studied and held research positions at the Universities of Cambridge, London, and Reading. His work has contributed to the understanding of visual perception across a range of topics, including visual illusions. In 2007, he received first prize in the Neural Correlate Society’s Best Visual Illusion Contest for the Leaning Tower Illusion.
Affiliations and expertise
Professor Emeritus, Department of Ophthalmology, McGill Vision Research, Montreal, QC, CanadaNP
Nicolaas Prins
Nicolaas Prins, PhD, is Associate Professor at the University of Mississippi. His research focuses on visual perception and the application of statistical methods to the design, collection, and analysis of psychophysical data. Before joining the University of Mississippi, he held research positions in Australia and Canada. His work integrates vision science with quantitative methods for psychophysical research.
Affiliations and expertise
Associate Professor, Department of Psychology, University of Mississippi, University, MS, USA