Detection Theory: A User's Guide |
Contents
Sensitivity | 7 |
The decision space | 19 |
The provenance of detection theory | 25 |
Response bias | 31 |
The rating experiment and empirical ROCS | 58 |
31 | 83 |
Threshold theory and nonparametric analysis | 88 |
Discrimination with two or more | 117 |
Problems | 207 |
Tworesponse classification with a standard | 218 |
44 | 225 |
Multidimensional detection theory | 233 |
Statistics and detection theory | 267 |
Elements of probability and statistics | 291 |
Logarithms and exponentials | 303 |
Flowcharts to sensitivity and bias | 305 |
33 | 139 |
Samedifferent designs | 141 |
37 | 157 |
Manyinterval designs | 162 |
42 | 173 |
Larger stimulus contexts | 181 |
Choices for the adaptive tester | 190 |
Criteria for evaluating adaptive methods | 201 |
Adaptive procedures | 311 |
Tables | 317 |
Software for detection theory | 357 |
Glossary | 369 |
References | 383 |
397 | |
Common terms and phrases
2AFC adaptive analysis applied assumed average axis bias calculated called chapter Choice Theory classification compared comparison computed condition consider constant corresponding criterion curve decision space depends described designs detection theory differencing dimension discrimination discussed distance distributions effects equal equation estimate example experiment experimental false-alarm rate Figure fixed four function hit rate identification implied increases independent intensity interval leads less likelihood ratio mean measure methods normal observer obtained oddity pair paradigms parameter perception performance possible predicted presented probability problem procedure proportion correct relation relative response roving rule S₁ S₂ same-different sample sensitivity shown shows single slope standard deviation statistic step stimulus strategy subjects Table task threshold tion transformation trials true unbiased underlying units values variable variance varies yes-no yields