Contents, Part 1: TEXT
1. Introduction
Antecedents
Notation
Twofold partition as an initial step in understanding a complex process
Processes, measures, factors, and selective influence
Statistical issues
Process decomposition versus task comparison
Relevance of brain measurement to process decomposition
Organization of the paper
2. Pure measures: Definitions and inferential logic
Selective influence of factors on processes and their measures
Hypothesis, prediction, and inference for pure measures
Why all four properties are important
3. Composite measures: Definitions and inferential logic
Composite measures based on summation
-- Illustration: The Additive-Factor Method.
Composite measures based on multiplication
Factorial experiments
4. Introduction to three examples of inference based on derived pure measures
5. Isolation of a timing module in the rat (Example 1)
Two kinds of elaboration of the two-module analysis
Comments
6. Parallel neural modules revealed by the lateralized-readiness potential (Example 2)
Comments
7. Separation of sensory and decision processes by signal detection theory (Example 3)
Why has SDT failed in this respect?
An approximation to full modularity of sensory and decision processes when reinforcer ratio is controlled
Comments
8. Introduction to three examples of inference based on direct pure measures
9. Evidence from selective adaptation for modular spatial-frequency analyzers (Example 4)
Comments
10. Evidence from individual neurons for temporally-delimited (serial) neural modules (Example 5)
Estimation of stage durations
Relation of these findings to the demonstration of separate modifiability
Comments
11. Evidence from fMRI for modular neural processes implemented by anatomically delimited processors (Example 6)
Comments
12. Introduction to four examples of inference based on composite measures
13. Evidence from `probability summation' at threshold for modular spatial-frequency analyzers (Example 7)
Justification of a multiplicative combination rule for non-detection probability
Three tests of the joint hypothesis
Comments
14. Amplitude of the event-related potential as a composite measure, with the combination rule known (Example 8)
The additive-amplitude method
Application of the additive-amplitude method to word classification
Comments
15. Multiplicative combination rule for response rate (Example 9)
A plausible systematic deviation from additivity measured by multiplicative interaction of scaled factor levels
Comments
16. Reaction time as a composite measure: Selective effects of sleep deprivation (Example 10)
Comments
17. General Discussion
Contents, Part 2: APPENDICES, etc.
Appendix A1. Process decomposition versus task comparison
Introduction
Qualitative task comparison
-- Behavioral studies of memory.
-- Effects of sleep deprivation
-- Task-specific effects of localized brain lesions.
-- Task-specific effects on localized brain activation.
Quantitative task comparison: Subtraction and division methods
-- Donders' subtraction method.
-- Jacoby's division method.
-- Derived pure measures from subtraction and division methods.
Appendix A2. Considerations in the choice of factors
Unitary factors and qualitative process invariance
Manipulated versus selective factors
Inferential logic when hypotheses about modules specify the factors
Appendix A3. Composite measures, combination rules, and stochastic independence
Measures, combination rules, and plausibility
Multiplication as combination rule: Implications for data analysis of the zero-correlation requirement
Stochastic independence of process contributions as further evidence of modularity
Appendix A6. Serial neural modules revealed by the lateralized readiness potential
Appendix A7. Details of the analysis of brightness discrimination by pigeons
Support for the equal-variance Gaussian detection model: Details
Evaluation of an alternative measure of the decision process
Appendix A9. Pure measures: Factorial experiments with multiple-level factors in selective adaptation
Advantages of a factorial design.
Advantages of multiple factor levels.
Appendix A10. Inferring neural processing stages from single-unit recordings
Classification of neurons
Combining activation times over neurons
Effects of stimulus and response factors on stage durations
Appendix A11 fMRI and modular processes: Requirements and statistical issues
A.1 Requirements for a process-decomposition study
A.2 Statistical issues
Appendix A13 Fitting and testing one-channel and two-channel models of detection
Appendix A15 Numerical scaling of factor levels for multiplicative and hybrid combination rules
Appendix A16 Processing stages as modules
Multiplicative combination rule for the proportion of response omissions
What is a `stage'?
Additive effects of factors on mean reaction time: Alternative interpretations
GLOSSARY
ACKNOWLEDGEMENTS
REFERENCES