Units of representation in visual word recognition.

Abstract

As you read these words, a complex sequence of processes are at work in your brain, identifying visual patterns (letters) that are mapped onto familiar units (words), the meanings of which are combined to allow comprehension. In this description, a mental dictionary or lexicon linking word forms (orthography) to word meanings (semantics) plays a central and critical role in the reading process. However, some fundamental questions concerning the functional and neural organization of the mental lexicon remain unanswered. How are words composed of more than one unit (such as darkness ) stored in the lexicon? Are they stored as whole forms, or are complex words broken down into their constituent parts or morphemes ( dark and ness )? In this issue of PNAS, Devlin et al. (1) describe a functional MRI (fMRI) study that makes significant progress in understanding the functional and neural architecture of the systems involved in accessing the meaning of written words, and provide evidence in support of a controversial approach to the processing of complex words. Traditional linguistic accounts of how complex words are stored propose that the mental lexicon is organized morphemically. Morphemic organization ensures that there is no redundancy in the representation of related words created by using either derivational (e.g., trusty, distrust, untrustworthy ) or inflectional ( jumps, jumped, jumping ) morphemes. These theoretical arguments have been supported by the results of psycholinguistic experiments. For instance, responses to a simple word ( hunt ) are speeded or primed by a prior presentation of a related word ( hunter ), suggesting that these words have shared entries in the mental lexicon (2). However, priming could also arise from shared meaning or shared orthographic form, and research has also demonstrated that priming effects for morphologically related word pairs ( hunter – hunt ) are distinct from priming effects for items that have an equivalent amount of overlap …