Psychophysics - 1st Edition - ISBN: 9780123736567, 9780080920221

Psychophysics

1st Edition

A Practical Introduction

Authors: Frederick Kingdom Frederick Kingdom Nicolaas Prins Nicolaas Prins
Hardcover ISBN: 9780123736567
eBook ISBN: 9780080920221
Imprint: Academic Press
Published Date: 13th November 2009
Page Count: 296
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Description


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




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

Researchers, graduate students, and post-doctorates in perception research in neuroscience, psychology, cognition; optometrists.

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




Details

No. of pages:
296
Language:
English
Copyright:
© Academic Press 2010
Published:
Imprint:
Academic Press
eBook ISBN:
9780080920221

About the Author

Frederick Kingdom

Fred Kingdom is a Professor at McGill University conducting research into a variety of aspects of visual perception. He studied and held research positions at Cambridge, London and Reading Universities before taking up his current position at McGill in 1990. He has published over 125 articles, and recently won an award for a new visual illusion.

Affiliations and Expertise

Department of Opthalmology, McGill Vision Research, Montreal, QC, Canada

Frederick Kingdom

Fred Kingdom is a Professor at McGill University conducting research into a variety of aspects of visual perception. He studied and held research positions at Cambridge, London and Reading Universities before taking up his current position at McGill in 1990. He has published over 125 articles, and recently won an award for a new visual illusion.

Affiliations and Expertise

Department of Opthalmology, McGill Vision Research, Montreal, QC, Canada

Nicolaas Prins

Nick Prins is an Associate Professor at the University of Mississippi specializing in a variety of aspects of visual perception and the use of statistical methods in the collection and analysis of psychophysical data. He has held research positions in Australia and Canada before taking up his current position at the University of Mississippi.

Affiliations and Expertise

Department of Psychology, University of Mississippi, University, MS, USA

Nicolaas Prins

Nick Prins is an Associate Professor at the University of Mississippi specializing in a variety of aspects of visual perception and the use of statistical methods in the collection and analysis of psychophysical data. He has held research positions in Australia and Canada before taking up his current position at the University of Mississippi.

Affiliations and Expertise

Department of Psychology, University of Mississippi, University, MS, USA