Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

Recognition memory models posit that performance is impaired as the similarity between the probe cue and the contents of memory is increased (global similarity). Global similarity predictions have been commonly tested using category length designs, in which the number of items from a common taxonomic or associative category is manipulated. Prior work has demonstrated that increases in the length of associative categories show clear detriments on performance, but that result is found only inconsistently for taxonomic categories. In this work, we explored global similarity predictions using representations from the BEAGLE model (Jones & Mewhort, 2007). BEAGLE’s two types of word representations, item and order vectors, exhibit similarity relations that resemble relations among associative and taxonomic category members, respectively. Global similarity among item and order vectors was regressed onto drift rates in the diffusion decision model (DDM: Ratcliff, 1978), which simultaneously accounts for both response times and accuracy. We implemented this model in a hiearchical Bayesian framework across seven datasets with lists composed of unrelated words. Results indicated clear deficits due to global similarity among item vectors, suggesting that lists of unrelated words exhibit semantic structure that impairs performance. However, there were relatively small influences of global similarity among the order vectors. These results are consistent with prior work suggesting associative similarity causes stronger performance impairments than taxonomic similarity.