Discrete-state versus continuous models of the confidence-accuracy relationship in recognition memory

Abstract

The relationship between confidence and accuracy in recognition memory is important in real-world settings (e.g., eyewitness identification) and is also important to understand at a theoretical level. Signal detection theory assumes that recognition decisions are based on continuous underlying memory signals and therefore inherently predicts that the relationship between confidence and accuracy will be continuous. Almost invariably, the empirical data accord with this prediction. Threshold models instead assume that recognition decisions are based on discrete-state memory signals. As a result, these models do not inherently predict a continuous confidence-accuracy relationship. However, they can accommodate that result by adding hypothetical mapping relationships between discrete states and the confidence rating scale. These mapping relationships are thought to arise from a variety of factors, including demand characteristics (e.g., instructing participants to distribute their responses across the confidence scale). However, until such possibilities are experimentally investigated in the context of a recognition memory experiment, there is no sense in which threshold models adequately explain confidence ratings at a theoretical level. Here, we tested whether demand characteristics might account for the mapping relationships required by threshold models and found that confidence was continuously related to accuracy (almost identically so) both in the presence of strong experimenter demands and in their absence. We conclude that confidence ratings likely reflect the strength of a continuous underlying memory signal, not an attempt to use the confidence scale in a manner that accords with the perceived expectations of the experimenter.

Keywords: Recognition memory; Signal detection theory; Threshold theory.