This chapter presents two models of priming. The primary task under consideration is the identification of words presented visually at threshold. The first model, REMI (Retrieving Effectively from Memory, the ‘I’ stands for implicit), is a model for long-term priming in implicit memory. It explains repetition priming effects by assuming that during study of a word some contextual information is added to the corresponding lexical trace. This contextual information stored during the study task will tend to match the contextual information present during the test task, leading subjects to prefer studied words over non-studied words. The second model, ROUSE, is a model of short-term priming. ROUSE stands for Responding Optimally with Unknown Sources of Evidence, and it is able to explain an intricate pattern of results.

The original version of the counter model for perceptual identification (Ratcliff & McKoon, 1997) assumed that word frequency and prior study act solely to bias the identification process (i.e., subjects have a tendency to prefer high-frequency and studied low-frequency words, irrespective of the presented word). In a recent study, using a two-alternative forced-choice paradigm, we showed an enhanced discriminability effect for high-frequency and studied low-frequency words (Wagenmakers, Zeelenberg, & Raaijmakers, 2000). These results have led to a fundamental modification of the counter model: Prior study and high frequency not only result in bias, but presumably also result in a higher rate of feature extraction (i.e., better perception). We demonstrate that a criterion-shift model, assuming limited perceptual information extracted from the flash as well as a reduced distance to an identification threshold for high-frequency and studied low-frequency words, can also account for enhanced discriminability.