Testing the Foundations of Signal Detection Theory in Recognition Memory

Signal detection theory (SDT) plays a central role in the characterization of human judgments in a wide range of domains, most prominently in recognition memory. But despite its success, many of its fundamental properties are often misunderstood, especially when it comes to its testability. The present work examines five main properties that are characteristic of existing SDT models of recognition memory: (a) random-scale representation, (b) latent-variable independence, (c) likelihood-ratio monotonicity, (d) ROC function asymmetry, and (e) nonthreshold representation. In each case, we establish testable consequences and test them against data collected in the appropriately designed recognition-memory experiment. We also discuss the connection between yes-no, forced-choice, and ranking judgments. This connection introduces additional behavioral constraints and yields an alternative method of reconstructing yes-no ROC functions. Overall, the reported results provide a strong empirical foundation for SDT modeling in recognition memory.