Visual word recognition
Visual word recognition is an integral aspect of reading. Although readers are able to recognize visually presented words with apparent ease, the processes that map orthography onto phonology and semantics are far from straightforward. In the present chapter, we discuss the cognitive processes that skilled readers use in order to recognize and pronounce individual words. After a historical overview of the broad theoretical developments in this rich field, we provide a description of methods and a selective review of the empirical literature, with an emphasis on how the recognition of an isolated word is modulated by its lexical- and semantic-level properties and by its context. Finally, we briefly consider some recent approaches and analytic tools in visual word recognition research, including megastudies, analysis of response time distributions, and the important role of individual differences.
Reading is a complex process that draws on a remarkable number of diverse perceptual and cognitive processes. In this review, I provide an overview of computational models of reading, focussing on models of visual word recognition–how we recognise individual words. Early computational models had ‘toy’ lexicons, could simulate only a narrow range of phenomena, and frequently had fundamental limitations, such as being able to handle only four-letter words. The most recent models can use realistic lexicons, can simulate data from a range of tasks, and can process words of different lengths. These models are the driving force behind much of the empirical work on reading. I discuss how the data have guided model development and, importantly, I also provide guidelines to help interpret and evaluate the contribution the models make to our understanding of how we read. Visual word recognition (VWR) research has been driven by various theoretical models such as functional architecture model and computational model.
Functional architecture model specifies components of the lexical processing system & the transition between them. These models are difficult to be disproved. But they lack specificity with regard to knowledge representations and processing mechanisms. Eg. Dual route model by Patterson, Marshall & Coltheart (1985)
Computational models use computer stimulation to explain VWR process but in deals with only parts of word recognition process and apply to limited vocabularies. They are also easy to be disproved. These models are however acutely detailed. Another way of explaining VWR in terms of connectionist model vs. lexical access model.
Lexical access model-In this recognizing a word involves successfully accessing its entry in the mental dictionary. But it doesn’t take into account the change in meaning of word with context.
Connectionist model-It explains VWR through distributed representations.It allows entities such as meanings or spellings or pronunciations to be encoded. In contrast to lexical access model, there are no units dedicated to representing individual words; rather, the units represent sub-lexical features (graphemes, phonems) each of which participates in many different words.
International Journal of Swarm Intelligence and Evolutionary Computation