CHAPTER TWO Models of Word Reading : What Have We Learned?

      Mark S. Seidenberg, Molly Farry‐Thorn, and Jason D. Zevin

      In reading, much depends on word recognition. Words are the elements out of which expressions are composed. They act as hubs that index the many types of information used in comprehending and producing language, whether written, spoken, or signed (Seidenberg, 2017). Words encode information about their meanings and senses, their internal structure (principally syllables, morphemes, phonemes), and their grammatical functions (e.g., noun, verb). They also carry information about the linguistic contexts in which they occur: for example the verb put refers to a particular action that occurs with an agent, object, and location, whereas carry only requires the agent and object (MacDonald et al., 1994). Knowledge of a word also includes information about language use: how often words occur and co‐occur with other words, given what is in the world and what we choose to communicate about (Clark, 2015). This statistical information is encoded as people acquire and use language (Seidenberg & MacDonald, 2018). Gaining the ability to read and understand words quickly and accurately is the great leap into literacy, but one that is challenging for many children.

Visual word recognition has been the focus of an enormous amount of research because of its complexity and importance, and because most of what is involved would otherwise be hidden from awareness (for reviews, see Rastle, 2016; Cohen‐Shikora & Balota, 2016). The goal of this research is to develop theories that explain its many aspects: the knowledge and processes that underlie word recognition; the linguistic, cognitive, and perceptual capacities recruited for the purpose; how the skill develops, the bases of individual differences, and how the brain makes it all happen, among other topics. Theories are often expressed as “models” that provide detailed accounts of important components of the word recognition system. Although the use of such models dates from the nineteenth century, progress was greatly accelerated by two developments from the 1970s–1980s. The first was Marshall and Newcombe’s (1973) formulation of what came to be known as the “dual‐route” model of reading (Coltheart, 1978). The model was an account of impairments in reading aloud observed in patients following brain injury; Coltheart and colleagues later applied it to unimpaired reading and learning to read. Much of the subsequent research in this area can be seen as following from this pioneering work. The second was the creation of a “connectionist” computational model of reading, again focused on reading aloud, by Seidenberg and McClelland (1989; hereafter SM89). This work was important because it challenged the core assumptions underlying the dual‐route approach and introduced a new theoretical framework for visual word recognition and other types of lexical processing, based on the PDP framework developed by Rumelhart et al. (1986). Coltheart and colleagues subsequently developed several computational models of the dual‐route theory, collectively known as the dual‐route cascade (DRC) model (Coltheart et al., 1993; Coltheart et al., 2001).

      An enormous amount has been learned since then. Visual word recognition is one of the great success stories in modern cognitive science and neuroscience. For much of this period, the existence of two competing theoretical approaches – dual‐route and connectionist – accelerated research progress. These theories provided frameworks for investigating numerous aspects of reading and greatly expanded the scope of research in English and other languages. The theories also stimulated the development of computational models of specific types of information (e.g., orthography, semantics) and related phenomena (e.g., morphology: Seidenberg & Gonnerman, 2000; Seidenberg & Plaut, 2014). Visual word recognition also became a domain in which to explore contrasting approaches to computational modeling of cognitive phenomena (Coltheart, 2005; Seidenberg & Plaut, 2006), and methods for studying brain structure and function (e.g., Cox et al., 2015; Woollams et al., this volume). Given the sustained interest in the topic over many years, visual word recognition represents an important case study illustrating what modern cognitive science and neuroscience has achieved.

      The purpose of this chapter is to provide a critical perspective on this long endeavor, focusing on the role of computational modeling. Computational models of cognition serve two essential, interacting functions. One is methodological. Modeling requires theoretical claims to be specified at a level that allows them to be implemented as working simulations. A theory’s validity