The language of accurate recognition memory

Abstract

The natural language accompanying recognition judgments is a largely untapped though potentially rich source of information about the kinds of processing that may support recognition memory. The current report illustrates a series of methods using machine learning and receiver operating characteristics (ROCs) to examine whether the language participants use to justify their 'old' and 'new' recognition decisions (viz., memory justifications) predicts accuracy. The findings demonstrate that the natural language of observers conveys the accuracy of 'old' (hits versus false alarms) but not 'new' (misses versus correct rejections) decisions. The classifier trained on this language was considerably more predictive of accuracy than the initial speed of the decisions, generalized to the justification language of two independent experiments using different procedures, and appeared sensitive to the presence versus absence of recollective experiences in the observer's reports. We conclude by considering extensions of the approach to several basic and applied areas, and, more broadly, to identifying the explicit bases (if any) of classification decisions in general.

Keywords: Language content analysis; Machine learning; Receiver operating characterstics; Recognition memory; Recollection.