## Detection Theory: A User's GuideDetection Theory, Second Edition is an introduction to one of the most important tools for analysis of data where choices must be made and performance is not perfect. Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision making, then embraced by students of human memory. It has since been utilized in areas as diverse as animal behavior and X-ray diagnosis. |

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### Contents

Oneinterval discrimination | 5 |

Response bias | 31 |

The rating experiment and empirical ROCs | 58 |

Threshold theory and nonparametric analysis | 88 |

Discrimination with two or more | 117 |

Samedifferent designs | 141 |

Manyinterval designs | 162 |

Larger stimulus contexts | 181 |

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 |

Adaptive procedures | 311 |

Tables | 317 |

Software for detection theory | 357 |

Choices for the adaptive tester | 190 |

Criteria for evaluating adaptive methods | 201 |

Problems | 207 |

Tworesponse classification with a standard | 218 |

Glossary | 369 |

383 | |

397 | |

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### Common terms and phrases

2AFC alarms assumed average bias measures bias statistic calculated chapter Chart Choice Theory computed condition consider corresponding Creelman criterion location decision axis decision rule decision space designs differencing model dimension distance equal equation estimate example experimental false-alarm rate forced choice forced-choice frequency H and F high-threshold theory hit and false-alarm hit rate identification independent independent-observation model interval isobias curves likelihood ratio linear log-linear models log(a logistic distributions mAFC mean measure of sensitivity model cont nonparametric normal distribution number of trials observer observer's oddity one-interval P("different paradigms perceptual performance PEST predicted problem proportion correct psychometric function psychophysical random variable response bias ROC curve ROC space roving same-different sample saturated model sensitivity and bias sensitivity measure shown in Figure slope standard deviation statistic stim stimulus classes stimulus pair stimulus set strategy subjects Swets Table task tion unbiased underlying distributions variance yes-no experiment z-coordinates z-score z-transformation zero