Putting it all together: a unified account of word recognition and reaction-time distributions

R. Ratcliff, P. Gomez, and G. McKoon (2004) suggested much of what goes on in lexical decision is attributable to decision processes and may not be particularly informative about word recognition. They proposed that lexical decision should be characterized by a decision process, taking the form of a drift-diffusion model (R. Ratcliff, 1978), that operates on the output of lexical model. The present article argues that the distinction between perception and decision making is unnecessary and that it is possible to give a unified account of both lexical processing and decision making. This claim is supported by formal arguments and reinforced by simulations showing how the Bayesian Reader model (D. Norris, 2006) can be extended to fit the data on reaction time distributions collected by Ratcliff, Gomez, and McKoon simply by adding extra sources of noise. The Bayesian Reader gives an integrated explanation of both word recognition and decision making, using fewer parameters than the diffusion model. It can be thought of as a Bayesian diffusion model, which subsumes Ratcliff's drift-diffusion model as a special case.