A mathematical framework for making decisions under uncertainty by separating meaningful information from random background variation. The model represents two distributions — signal and noise — and defines a decision threshold that trades off hit rates against false alarm rates. The observer's sensitivity (d') quantifies how well the distributions can be distinguished, independent of their response bias. The framework persists through formalized procedures (threshold setting, ROC analysis) and computational models applied across radar engineering, medical diagnostics, psychophysics, and machine learning classification. [formal: signal detection | substrate: mind|behavior|matter | horizon: a moment|hours|days | explicit: yes | epoch: 0.01]
Accepted ontology entry
signal-detection
A mathematical framework for making decisions under uncertainty by separating meaningful information from random background variation. The model represents two distributions — signal and noise — and defines a decision threshold that trades…
Definition
Why it is in scope
A human-made analytical framework for distinguishing meaningful patterns (signal) from random variation (noise) in observation or measurement. Built to persist through mathematical modeling of decision-making under uncertainty, applied in engineering, medicine, psychology, and all fields requiring threshold-based classification.
Names and aliases
- signal-detectionen · CANONICAL
Relations from this entry
- cmsfbq1i906w73vv33r9veel9DEPENDS_ON →
Signal detection operates by making a binary decision (signal present vs absent) under uncertainty. Decision theory provides the framework for optimal decisions under uncertainty — without it, signal detection has no decision criterion, no cost/benefit analysis for false positives vs false negatives. The removal test passes: remove decision theory and signal detection ceases to operate as a decision framework.
- cmr9uz3vv00elhcxfruyltnd4DEPENDS_ON →
Signal detection as a concept requires measurement: the fundamental task of distinguishing signal from noise depends on the ability to measure quantities. Without measurement, there is no way to operationalize signal vs. noise discrimination. Per Law 8 removal test: remove measurement from signal detection and it ceases to operate as a practice.
Relations to this entry
No accepted relations in this direction.
Record identity
- Created
- Aug 7, 2026, 5:05 AM UTC
- Content hash
- 29519d4f45a2028734a6371e2592fae572d7b8abb4aa24be7ad783d76a09efc3