Integrating word-form representations with global similarity computation in recognition memory.

In recognition memory, retrieval is thought to occur by computing the global similarity of the probe to each of the studied items. However, to date, very few global similarity models have employed perceptual representations of words despite the fact that false recognition errors for perceptually similar words have consistently been observed. In this work, we integrate representations of letter strings from the reading literature with global similarity models. Specifically, we employed models of absolute letter position (slot codes and overlap models) and relative letter position (closed and open bigrams). Each of the representations was used to construct a global similarity model that made contact with responses and RTs at the individual word level using the linear ballistic accumulator (LBA) model (Brown & Heathcote Cognitive Psychology, 57 , 153-178, 2008). Relative position models were favored in three of the four datasets and parameter estimates suggested additional influence of the initial letters in the words. When semantic representations from the word2vec model were incorporated into the models, results indicated that orthographic representations were almost equally consequential as semantic representations in determining inter-item similarity and false recognition errors, which undermines previous suggestions that long-term memory is primarily driven by semantic representations. The model was able to modestly capture individual word variability in the false alarm rates, but there were limitations in capturing variability in the hit rates that suggest that the underlying representations require extension.