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This chapter reports studies that examine a clear notion of categorization — how people choose names for simple artefacts — and three specific kinds of similarity — physical, functional, and overall similarity. Constructs are measured carefully. For naming, a straightforward, naturalistic naming task is used that does not limit participants' choices. For similarity, three different measures of the three kinds of similarity are examined: sorting, direct similarity judgement, and a measure based on feature listings of the objects. The studies are all intended to evaluate the degree to which the names of these objects can be explained by appealing to perceived similarity relations among the objects with the aim of uncovering the relation between naming and similarity.

This chapter first describes the tasks that are used to study how readers identify printed words (e.g., the lexical-decision task) and then reviews the key empirical findings related to skilled and impaired word identification (i.e., dyslexia). As explained, these findings have both motivated the development of computer models of word identification and been used to evaluate the explanatory adequacy of those models. The chapter then reviews several precursor theories and models of word identification that provide recurring metaphors (e.g., generating word pronunciations via analogy vs. the application of rules) in the development of later, more formally implemented word-identification models. The chapter reviews a large representative sample of these models in the order of their development, to show how the models have evolved in response to empirical research and the need to accommodate new findings (e.g., how the letters in words are perceived in their correct order). The chapter concludes with an explicit comparative analysis of the word-identification models and discussion of the findings that each model can and cannot explain.

Viewed from the perspective of psycholinguistics, words are fairly magical entities, representing the psychological level at which twenty-six meaningless letters coalesce into thousands of meaningful units. Many choose only to study word-recognition itself, modeling RT data gathered from lexical decision or naming tasks. Others choose to follow the linguistic pathways higher, studying how words are integrated into syntactic or semantic levels of discourse. In either circumstance, words are typically treated in a manner consistent with linguistic theory — as abstract, canonical units that may be recombined to create endless messages. Word recognition is appreciated for its stability across visual or auditory variations, and is theoretically likened to finding entries in a computer search or activating the proper node (or pattern) in a network.