The Science of Reading. Группа авторов

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СКАЧАТЬ These words are rule‐governed according to dual‐route model, but they have one or more irregular neighbors (in parentheses). Connectionist/Statistical Learning Approach Degrees of Spelling‐Sound Consistency: Low High Strange words Exceptions Reg Inconsistent Regular Words and nonwords exhibit varying degrees of spelling‐sound consistency. Regular, exception and inconsistent words occupy positions on this continuum, along with other intermediate cases. “Strange” words are oddballs like COLONEL and SPHINX. Locations on the continuum are approximate.

      It took many years of research within both approaches to arrive at these conclusions. Over time, successors to the DRC model discarded defining features of the approach in favor of networks incorporating distributed representations trained via weight‐adjusting learning procedures, the connectionist approach (e.g., Perry et al., 2007; Ziegler et al., 2014). This development reflects broader trends in cognitive science and neuroscience. Core PDP/connectionist ideas about distributed representations, quasiregularity, statistical learning, constraint satisfaction processing, and division of labor between components of the language system have been widely absorbed and continue to inform research (e.g., Chang et al., 2020; Chen et al., 2017; Gordon & Dell, 2003; Hoffman et al., 2015; Smith et al., 2021). This framework has proved particularly relevant to understanding the brain bases of reading, language, and visual cognition because the grain of the models is well matched to the grain of the data obtained using current neuroimaging methods (Cox et al., 2015). The computational models retain their relevance to understanding cognition and its brain bases even though they are simpler than deep learning networks that perform far more complex tasks, but are much harder to analyze and less closely tied to human experience (Joanisse & McClelland, 2015).

This chapter begins by showing that simulations presented as supporting the DRC model differed from the corresponding behavioral studies. The implemented models also exhibited other anomalous behaviors that were overlooked. Connectionist networks reproduce the behavioral effects but can also explain why they occur (Seidenberg & Plaut, 2006). Our discussion focuses on the major features of reading aloud, leaving aside many other important issues (the bases of individual differences and dyslexia, cross‐linguistic comparisons, brain bases of reading, and others) because of space limitations.

      We then examine “connectionist dual‐route models” (Perry et al., 2007, 2010; Ziegler et al., 2014). These hybrid models incorporate the major assumptions of the “triangle” framework but differ in one respect: They retain a second, lexical route. However, the phenomena this mechanism is intended to explain are explained in connectionist models that incorporate additional parts of the orthography➔phonology➔semantics triangle. The “lexical route” allows the authors to claim a degree of continuity with dual‐route models, but it is not required to explain any data. We close by considering the relevance of computational modeling for understanding how children learn to read. The dual‐route theory remains influential in areas where computational modeling results are not well known. These include reading acquisition and instruction, where research and pedagogy still focus on learning pronunciation rules and adding sight words to the lexicon, and in some areas of cognitive neuroscience (e.g., Bouhali et al., 2019). Modeling established the inadequacy of the dual‐route model, but because those results are not known, the approach retains its intuitive appeal. There are deep concerns about literacy levels in the United States, United Kingdom, and many other countries, and great interest in using the “science of reading” to improve instruction and outcomes (Seidenberg et al., 2020). A theory of visual word recognition could contribute to improved educational practices but only if the theory is correct and speaks to relevant issues about how children learn.

Reading Aloud: Accounting for Basic Phenomena

      The dual‐route theory is an account of reading aloud. The two routes refer to procedures thought to be necessary and sufficient for generating pronunciations from print. The procedures involve orthography and phonology but not semantics, which is only used in reading aloud as a compensatory strategy in acquired dyslexia (Coltheart, 2006). Pritchard et al. (2018) also incorporated semantics in their account of learning to read.

Seidenberg and McClelland (1989) introduced a general theoretical framework for computing lexical codes (orthography, phonology, semantics). It is called the “triangle” model for brevity, but also includes the contexts in which words occur. Ideally a model that processes words would be embedded in one that processes sequences of words in meaningful texts. Their implemented model was a network that computed pronunciations from print, using a procedure that applied to all types of letter strings. In later work Plaut et al. (1996) addressed the role of semantics in reading aloud, and the “division of labor” between components of the triangle (see also Seidenberg, 1992). Seidenberg and McClelland (1989) assessed whether the trained model could reproduce several well‐established findings concerning properties of words that affect skilled performance and reading acquisition. The methodology involved testing the model on the same words used in an experiment and observing whether the same pattern of results was obtained. This approach was subsequently adopted by Coltheart et al. (2001). Some of the phenomena (e.g., regularity effects) are predicted by both approaches; others (e.g., consistency effects) are particularly important because the theories make different predictions about them.

      Here, we focus on modeling results that illustrate four types of problems with DRC models:

       Simulations of “benchmark” studies that were said to reproduce an effect (e.g., frequency X regularity interaction) deviated from the behavioral findings in important ways.

       The models consistently missimulated the results of other studies of the same phenomena but these were not reported, creating a modeling version of a “file‐drawer problem” (Simmons et al., 2011).

       The models exhibited other anomalous behaviors that were not discussed.

       The models did not address prominent phenomena that contradict the approach.

Simulations of Behavioral Studies: What Did They Show?

       Regularity effects

      Early studies showed that even for skilled adult readers, exception words produce longer naming latencies than regular words (Baron & Strawson, 1976), termed the regularity effect. Seidenberg et al. (1984) discovered an important additional fact: regularity interacts with frequency. Whereas higher frequency regular and exception words are read equally rapidly (and accurately), lower frequency exception words take longer than lower frequency regular words. This is a well‐replicated effect. It occurs with other types of linguistic information (e.g., Juliano & Tanenhaus, 1994; Pearlmutter & MacDonald, 1995) and reflects a general fact about cognition: The impact of atypical structure can be overcome with sufficient experience, even as it continues to affect performance on less common forms.

      Seidenberg and McClelland (1989) simulated several studies that yielded this interaction, showing that it arises in a network in which repetitions of word and subword patterns create a continuum of spelling‐sound consistency; see Plaut et al. (1996) for a formal analysis and further predictions regarding the use of semantics. The DRC models were attempts to replicate these effects (Coltheart et al., 2001). Within this framework, the basic regularity effect occurs because both routes yield the same pronunciations for regular words, but conflicting pronunciations for exceptions, which slows processing. СКАЧАТЬ