A Practical Introduction


  • Frederick Kingdom, Department of Opthalmology, McGill Vision Research, Montreal, QC, Canada
  • Frederick Kingdom, Department of Opthalmology, McGill Vision Research, Montreal, QC, Canada
  • Nicolaas Prins, Department of Psychology, University of Mississippi, University, MS, USA
  • Nicolaas Prins, Department of Psychology, University of Mississippi, University, MS, USA

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.
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Book information

  • Published: November 2009
  • ISBN: 978-0-12-373656-7

Table of Contents



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


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



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


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



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



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



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


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



Quick Reference Guide