Thesis (Ph.D.) - Indiana University,Psychological and Brain Sciences/Cognitive Science, 2015We argue that taking a dynamic approach to the understanding of memory will lead to
advances that are not possible via other routes. To that end, we present a model of
recognition memory that specifies how memory retrieval and recognition decisions jointly
evolve over time and show that it is able to jointly predict accuracy, response time, and
speed-accuracy trade-off functions. The model affords insights into the effects of study
time, list length, and instructions. The model leads to a novel qualitative and quantitative
test of the source of word frequency effects in recognition, showing that the relatively high
distinctiveness of the features of low frequency words provide the best account. We also
show how the dynamic model can be extended to account for paradigms like associative
recognition and list discrimination, leading to another novel test of the presence of
recall-like processes. Associative recognition, list discrimination, recognition of similar
foils, and source exclusion are all better explained by the formation of a compound cue
rather than recall, although source memory is found to be better modeled by a recall
process
