As a function of the amount of evidence in a detector mechanism (e.g., a neural mechanism for visual processing), we can distinguish a probability distribution for cases when the signal is...

Signal detection theory provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty. The general approach of signal detection theory has …

Signal detection theory (SDT) sprouted from World War II research on radar into a probability-based theory in the early 1950s. It specifies the optimal observation and decision processes …

Jul 13, 2021 · Here I will describe the conceptual framework underlying the theory. SDT can be generalized to apply to any diagnostic system, a term that applies whenever a formal set of …

Signal Detection Theory (often abridged as sdt) is used to analyze data coming from experiments where the task is to categorize ambiguous stimuli which can be generated either by a known …

Aug 25, 2023 · Signal Detection Theory (SDT) is an informative model for understanding how humans make decisions based on sensory input. This vital concept helps comprehend why …

Signal detection theory (SDT) is used to analyze data coming from experiments in which the task is to categorize ambiguous stimuli that can either be generated by a known process (in which …

Signal detection theory provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty. The general approach of signal detection theory has …

Welcome to d′etectable, an interactive introduction to Signal Detection Theory (SDT). This site uses manipulable diagrams, dynamic tables, and live equations to explain the motivation, …

Signal detection theory and Bayes' theorem have been proposed as suitable frameworks to address these questions (Locke and Robinson, 2021; Huys et al., 2021). ...