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A Bayesian explanation of masked priming.
NORRIS, D., Kinoshita, S., & VAN CASTEREN, M.
Paper presented at the The 4th International Conference on Memory (ICOM4), Sydney.
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We present a number of simulations of masked priming using an extended version of the Bayesian Reader model (Norris, in press). The central assumption of the simulations is that readers accumulate evidence from the input to continuously revise the posterior probabilities of letters or words. If readers fail to appreciate that the prime and target correspond to different objects, the prime and target effectively provide independent sources of information which are combined in making decisions about the target's identity or lexical status. The simulations investigate the consequences of basing the model on alternative representations and decision rules, and show that the model can give a principled account of the basic empirical results on masked priming.