This paper presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision and semantic categorization, human readers behave as optimal Bayesian decision-makers. This leads to the development of a computational model of word recognition, the Bayesian Reader. The Bayesian Reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word-frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model, and the way the model predicts different patterns of results in different tasks, follow entirely from the assumption that human readers approximate optimal Bayesian decision-makers.

To minimize drug name confusion errors, regulators, drug companies, and clinicians need tools that help them predict which names are most likely to be involved in confusions.Two experiments, carried out in the United States, examined the effects of stimulus frequency (i.e., how frequently a target name is prescribed), neighborhood frequency (i.e., how frequently prescribed are the "neighbors" of the target name), and neighborhood density (how many names are within a fixed distance of the target name) on the probability of pharmacists making an error in a visual perceptual identification task.In both experiments, the task was to correctly identify a series of blurry drug names after a 3 s presentation on a computer monitor.In the first experiment, 45 pharmacists viewed 160 typewritten names, incorrectly identifying 60.6% of them. Random effects regression revealed a significant beneficial effect of stimulus frequency and a detrimental effect of neighborhood density.Significant two-way interactions were observed between stimulus frequency and neighborhood density and neighborhood frequency and neighborhood density.In the second experiment, 37 pharmacists viewed 156 handwritten drug names, incorrectly identifying 45.7%. Random effects regression revealed significant main effects of stimulus frequency and neighborhood density.These were contained within a significant three-way interaction: The interaction between stimulus frequency and neighborhood density was present at high but not low neighborhood frequency.Objectively measurable frequency and neighborhood characteristics have predictable effects on errors in pharmacists' visual perception.Organizations that coin and evaluate drug names, as well as hospitals, pharmacies, and health systems, should consider these characte...

In reading, it is often assumed that words are recognized sufficiently quickly, accurately, and unambiguously that downstream processes may proceed with perfect information about word identity. For example, word predictability is believed to affect early reading time measures, yet a word’s predictability cannot be calculated without knowledge of the word’s identity. We argue that such information is not, in general, available to the language processing system, and that it proceeds with only probabilistic information about word identity. We predict therefore that what have been analyzed previously as predictability effects must instead be based on noisy estimates of word predictability that are influenced by the predictability of visually similar words (neighbors). We test this prediction by building a Bayesian model of visual word recognition, using it to compute the ‘average neighborhood surprisal ’ of words in a corpus, and testing the ability of this novel measure to explain human reading time data.