Words are the building blocks of language, and visual word recognition is a crucial prerequisite for skilled reading. Before we can pronounce a word or understand what it means, we have to first recognize it (i.e., the visually presented word makes contact with its underlying mental representation). Although several tasks have been developed to tap word recognition performance, researchers have primarily relied on lexical decision (classifying letter strings as words or nonwords), speeded pronunciation (reading a word or nonword aloud), and semantic classification (e.g., classifying a word as animate or inanimate). Despite the apparent ease of visual word recognition, the processes that support the mapping of spelling-to-sound and spelling-to-meaning are far from perfectly understood and remain the object of active investigations. Beyond shedding light on reading, literacy, and language development, the visual word recognition literature has helped inform our understanding of other cognitive domains (e.g., pattern recognition, attention, memory), while propelling advances in computational modeling and cognitive neuroscience. Because words can be coded and analyzed at multiple levels (e.g., orthography, phonology, semantics), much of empirical research has explored the functional relationships between orthographic, phonological, and semantic variables and word recognition performance across lexical processing tasks. In addition to studying the recognition of isolated words, there is a rich literature examining how different prime contexts influence the processing of subsequently presented words. Such primes can be orthographically, phonologically, semantically, or morphologically related to targets and are either visible or masked (i.e., presented so briefly that conscious perception is minimized). Turning to methodology, although the classical factorial design continues to dominate word recognition research, an increasing amount of work has been leveraging on the megastudy approach, whereby researchers examine word recognition performance for large sets of words, which are defined by the language rather than by the experimenter. Collectively, the basic findings from the isolated and primed visual word recognition performance have been used to develop and constrain increasingly powerful computational models of word recognition and task performance. Moving forward, the visual word recognition literature is likely to be increasingly characterized by studies that rely on powerful analytical tools (e.g., linear mixed effects analyses, analysis of response time distributions) and which give more consideration to the role of individual differences. Finally, in light of space constraints, this article focuses on references that deal with how visually presented English words are recognized. There is an important and growing literature that explores the lexical processing of other alphabetic (e.g., Spanish, French, German) and nonalphabetic (e.g., Chinese, Korean) languages and the interplay between languages in the multilingual lexicon.
Connectionism and the role of morphology in visual word recognition
