Figure 2.1 Theory development and evaluation using computational models. Theoretical frameworks are used to develop theories of particular phenomena. Models that implement core parts of the theory are intended to simulate target data. Model performance feeds back on theory development and generates new hypotheses and empirical tests.

      With the benefit of 30‐some years of hindsight we can ask: Did computational models of reading yield the expected benefits? Did they indeed provide a basis for assessing competing theories? Did they yield new theoretical insights? In short, given the promise of the approach and several decades of modeling research, what have we learned?

      Like many others, we think that computational modeling proved to be an invaluable tool in both methodological and theoretical respects. Taken as a method for testing theories, attempts to implement models based on the dual‐route theory revealed apparently intractable limitations of the approach. Researchers were unable to implement models that reproduced basic behavioral phenomena concerning the pronunciation of regular and irregular words and nonwords that the dual‐route theory was developed to explain.

Models based on the connectionist framework reproduced these effects, as well as additional phenomena that were not predicted by the dual‐route theory and were not simulated correctly within it. That approach is limited by the core assumption that pronunciations are either rule‐governed or exceptions. This dichotomy overlooks the fact that spelling‐sound correspondences exhibit varying degrees of consistency (Table 2.1). Regular (rule‐governed) words and exceptions occupy different points on this consistency continuum. Importantly, this account also predicts that words and nonwords can exhibit intermediate degrees of consistency. Consistency effects have been observed in numerous studies dating from Glushko (1979). Connectionist models could reproduce regularity, consistency, and other effects because they encode spelling‐sound correspondences as statistical dependencies rather than as rules and exceptions. The connectionist models also advanced theorizing by showing how concepts and computational mechanisms from the PDP framework could provide new insights about complex behavior.

Table 2.1 Regularity versus Consistency: What’s the Difference?

      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