Multidimensional signal detection theory: Theoretical issues and empirical applications

Abstract

Signal detection theory (SDT) provides a powerful set of tools for modeling choice data in a wide variety of domains. Fundamental to SDT is the distinction between noisy evidence and deterministic response selection. Multidimensional signal detection theory (MDSDT, aka general recognition theory) extends the basic concepts of SDT so that they are applicable to more complex tasks and data. Building on the SDT foundation of noisy evidence and deterministic response selection, MDSDT adds multiple ways in which dimensions may (or may not) interact. The additional complexity of multiple dimensions both broadens the range of applications of these tools and presents unique challenges. I will discuss the origins of MDSDT, a number of empirical applications of MDSDT in speech and hearing sciences, and some recent theoretical developments, focusing on model mimicry and identifiability and multilevel extensions of the basic model.Signal detection theory (SDT) provides a powerful set of tools for modeling choice data in a wide variety of domains. Fundamental to SDT is the distinction between noisy evidence and deterministic response selection. Multidimensional signal detection theory (MDSDT, aka general recognition theory) extends the basic concepts of SDT so that they are applicable to more complex tasks and data. Building on the SDT foundation of noisy evidence and deterministic response selection, MDSDT adds multiple ways in which dimensions may (or may not) interact. The additional complexity of multiple dimensions both broadens the range of applications of these tools and presents unique challenges. I will discuss the origins of MDSDT, a number of empirical applications of MDSDT in speech and hearing sciences, and some recent theoretical developments, focusing on model mimicry and identifiability and multilevel extensions of the basic model.