Quantifying Stimulus Discriminability: A Comparison of Information Theory and Ideal Observer Analysis
| Autor: | Eric E. Thomson, William B. Kristan |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Signal Detection
Psychological Entropy Cognitive Neuroscience Models Neurological Information Theory Sensation Information theory Upper and lower bounds Discrimination Psychological Arts and Humanities (miscellaneous) Animals Humans Entropy (information theory) Nervous System Physiological Phenomena Ideal observer analysis Mathematics Observer Variation business.industry Conditional mutual information Specific-information Mutual information Probability Theory Artificial intelligence Variation of information business Algorithm Algorithms |
| Zdroj: | Neural Computation. 17:741-778 |
| ISSN: | 1530-888X 0899-7667 |
| Popis: | Performance in sensory discrimination tasks is commonly quantified using either information theory or ideal observer analysis. These two quantitative frameworks are often assumed to be equivalent. For example, higher mutual information is said to correspond to improved performance of an ideal observer in a stimulus estimation task. To the contrary, drawing on and extending previous results, we show that five information-theoretic quantities (entropy, response-conditional entropy, specific information, equivocation, and mutual information) violate this assumption. More positively, we show how these information measures can be used to calculate upper and lower bounds on ideal observer performance, and vice versa. The results show that the mathematical resources of ideal observer analysis are preferable to information theory for evaluating performance in a stimulus discrimination task. We also discuss the applicability of information theory to questions that ideal observer analysis cannot address. |
| Databáze: | OpenAIRE |
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