The Science of Reading. Группа авторов

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these ideas (e.g., Strong et al., 2011; Snowling et al., 2019). Further, it has been proposed that automatized naming problems (e.g., & Wolf, 2011), when added to a phonological deficit, produce a “double deficit” (Wolf & Bowers, 1999) and Ziegler et al. (2019) concluded that most children show phonological deficits while also showing weaknesses in nonphonological tasks, especially letter detection. Although there are potentially multiple causes of disruption to the word‐identification system, the phonological deficit hypothesis is supported by extensive evidence and is now the standard theory. Indeed, a phonological deficit is part of the definition of dyslexia provided by the International Dyslexia Association (https://dyslexiaida.org/definition‐of‐dyslexia/).

      It should be contentious to consider a subsystem of reading called “reading comprehension.” If learning to read words unlocks the resources of spoken language comprehension, then anything special about reading ends at word identification. “The Simple View of Reading” (Gough & Tunmer, 1986; Hoover & Gough, 1990) expresses this assumption and continues to accumulate evidence (Catts, 2018; Hjetland et al., 2020; Lonigan et al., 2018; Nation, 2019). Moreover, reading comprehension builds on spoken language experience. Preschool measures of oral language predict school‐entry indicators of word level skills that predict later comprehension skills (Hulme et al., 2015).

      Nevertheless, comprehension is a distinctive subsystem of reading, even if it derives from general language comprehension. Moreover, excluding reading comprehension as part of reading would ignore the largest body of research on skilled comprehension. Much of what is known about language comprehension – including such basic aspects as sentence comprehension – comes from reading research (see Liversedge et al., this volume).

Schematic illustration of the comprehension system of the Reading Systems Framework.

       From global top‐down structures to actual comprehension

      Text comprehension results from word‐by‐word, phrase‐by‐phrase, and sentence‐by‐sentence processes that are challenging to study. So, research started at the other end – where global structures could be seen as shaping local word and sentence processes. Early artificial intelligence (AI) systems started with global organizers for restricted situational comprehension (Schank & Abelson, 1977). Similarly, approaches within psychology and education also emphasized situated conceptual structures or schemata (Anderson & Pearson, 1984). Evidence for global top‐down guidance came from studies showing that a nearly incomprehensible text could be understood with a helpful title (Bransford & Johnson, 1972) and that a text lacking referential specificity could be understood as being about either music or card playing depending on whether the reader was a student in music education or physical education (Anderson et al., 1977).

      Other approaches focused on more generalized mental structures (e.g., story grammars, Mandler & Johnson, 1977; Stein & Glenn, 1979) that guide narrative comprehension. Trabasso and colleagues (1984) argued that people seek causality in reading stories and showed that causal expectations predict how readers understand sentences (Trabasso & Suh, 1993). In Reading Systems Framework terms, these approaches focus on the general knowledge component and largely ignore comprehension processes. They provide demonstrations of global influence without dealing with the nuts and bolts of comprehension.

       Text comprehension from the bottom up

      Global influences continued to be emphasized in constructivist theories that assume readers are driven to construct coherence and search for meaning (Graesser et al., 1994). Top‐down influences were elaborated more specifically as mental structures to guide the reader’s construction of coherence, for example, dimensions of time, space, and causality in the Event‐Indexing Model (Zwaan et al., 1995). The Landscape model combined the automatic bottom‐up processes of memory‐based models with the top‐down influences of constructionist theories (van den Broek et al., 2005; van den Broek et al., 1999). In this model, a coherent mental representation emerges from both text and external knowledge activation patterns that increase and diminish over the course of reading a text. Comprehension results from the mixing of automatic passive processes with reader‐initiated strategic processes determined by the reader’s standard for coherence and goals in a particular reading situation (van den Broek et al., 1995; van den Broek & Helder, 2017; see van den Broek & Kendeou, this volume).

       The situation model: Knowledge and inferences

      Text comprehension results in memories at multiple levels, two at minimum: the text surface and the mental‐model (Johnson‐Laird, 1983). Research generally has followed the three‐way distinction of van Dijk and Kintsch (1983): surface level, text‐base, and situation model. This three‐way distinction adds a level of language‐based text meanings (propositions) intermediate between clauses/sentences and situated meanings.

      Critical in the situation model are inferences that require knowledge from both the text and the reader’s general knowledge. Bridging inferences are often required to make a text coherent (see O’Brien et al., 2015). For example, in reading “The bright sun lit the field. Alfred’s snowman melted,” one maintains coherence by inferring that the sun’s heat caused the snow to melt (Singer et al. 1992). When related knowledge triggers elaborative inferences, which are not required for coherence, comprehension becomes referentially richer and more interpretative, although unwarranted inferences can lead to inaccuracies. Successful comprehension

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