Computing the Meanings of Words in Reading: Cooperative Division of Labor Between Visual and Phonological Processes.

Harm, Michael W.; Seidenberg, Mark S.

doi:10.1037/0033-295x.111.3.662

Cited by 966 publications

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“…The results are also compatible with connectionist models that contain no word units, but instead compute print-to-sound mappings through interactive activation of non-lexical orthographic, phonological, and semantic units (Harm and Seidenberg, 2004;Plaut et al, 1996;Seidenberg and McClelland, 1989). Like the parallel dual-route model, the triangle model postulates activation of all parts of the reading system by both Irregular and Regular words.…”

Section: The Dtrianglet Modelsupporting

confidence: 72%

“…After a set of optimal connection weights has been learned, both irregular words and nonwords are pronounced using the same network. The first version of this model used a single pathway mapping orthography directly to phonology (Seidenberg and McClelland, 1989), but subsequent elaborations have added a layer of semantic units not only to assist with pronunciation of irregular words but also to more accurately account for dissociations between nonword and irregular word performance observed in patients with acquired dyslexia (Harm and Seidenberg, 2004;Plaut and Shallice, 1993;Plaut et al, 1996). This version has become known as the dtriangle modelT in reference to the triangular architecture linking orthographic, phonological, and semantic units.…”

Section: Introductionmentioning

confidence: 99%

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Some neurophysiological constraints on models of word naming

et al. 2005

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The pronunciation of irregular words in deep orthographies like English cannot be specified by simple rules. On the other hand, the fact that novel letter strings can be pronounced seems to imply the existence of such rules. These facts motivate dual-route models of word naming, which postulate separate lexical (whole-word) and non-lexical (rulebased) mechanisms for accessing phonology. We used fMRI during oral naming of irregular words, regular words, and nonwords, to test this theory against a competing single-mechanism account known as the triangle model, which proposes that all words are handled by a single system containing distributed orthographic, phonological, and semantic codes rather than word codes. Two versions of the dual-route model were distinguished: an dexclusiveT version in which activation of one processing route predominates over the other, and a dparallelT version in which both routes are equally activated by all words. The fMRI results provide no support for the exclusive dual-route model. Several frontal, insular, anterior cingulate, and parietal regions showed responses that increased with naming difficulty (nonword > irregular word > regular word) and were correlated with response time, but there was no activation consistent with the predicted response of a nonlexical, rule-based mechanism (i.e., nonword > regular word > irregular word). Several regions, including the angular gyrus and dorsal prefrontal cortex bilaterally, left ventromedial temporal lobe, and posterior cingulate gyrus, were activated more by words than nonwords, but these dlexical routeT regions were equally active for irregular and regular words. The results are compatible with both the parallel dual-route model and the triangle model. dLexical routeT regions also showed effects of word imageability. Together with previous imaging studies using semantic task contrasts, the imageability effects are consistent with semantic processing in these brain regions, suggesting that word naming is partly semantically-mediated. D 2005 Elsevier Inc. All rights reserved. Keywords: Word naming; Dual-route model; Triangle model IntroductionThe correspondence between spoken and written forms of a language is not always systematic. While in some alphabetic orthographies the sound of a word can be worked out using rules of pronunciation, in most, there are varying degrees of irregularity in the mapping between print and sound. In English, for example, Bernard Shaw pointed out that the word ''fish'' could be written ghoti if one were mischievous enough to borrow the spelling for /f/ from rough, the spelling of /I/ from women, and the spelling of /sh/ from nation. Words like colonel and yacht are only some of the more extreme examples of such irregularity of pronunciation, which is pervasive in English and is seen in many of its more common words, including some, many, of, the, and word just used in this sentence.While the pronunciation of these dirregularT words would seem to be learned through rote memorization of the whole word, there is...

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Section: The Dtrianglet Modelsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Some neurophysiological constraints on models of word naming

et al. 2005

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“…It is possible that training on orthographic mappings also has emergent consequences for semantic processing that then give rise to increased sensitivity to semantic overlap. The empirical evidence suggests at most only a subtle effect of literacy on semantic processing (da Silva et al, 2004;Kosmidis et al, 2004), however previous computational modelling studies of reading acquisition demonstrate that differences in the orthographic transparency of a language can have implications for the distribution of labour between phonological, semantic and orthographic processing networks (Harm & Seidenberg, 2004;Yang et al, 2013). Without knowing the emergent consequences of the additional orthographic mapping performed by such networks, it is not possible to rule out the possibility that such training could result in a modulation of semantic competitor effects without requiring a reduction in cognitive efficiency to be implemented.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, previous models of reading acquisition have made important contributions demonstrating an influence of orthographic transparency on phonological processing (Harm & Seidenberg, 1999;Yang, McCandliss, Shu, & Zevin, 2009) and semantic processing (Harm & Seidenberg, 2004;Yang, Shu, McCandliss, & Zevin, 2013). However, such models have tended to be trained on prototypical phonological representations in which substantial phonological structure is embedded, and then the processing of the phonological structure itself is investigated as both the input and output system.…”

Section: Discussionmentioning

confidence: 99%

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Literacy effects on language and vision: Emergent effects from an amodal shared resource (ASR) computational model

Smith

Monaghan

Huettig

2014

Cognitive Psychology

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a b s t r a c tLearning to read and write requires an individual to connect additional orthographic representations to pre-existing mappings between phonological and semantic representations of words. Past empirical results suggest that the process of learning to read and write (at least in alphabetic languages) elicits changes in the language processing system, by either increasing the cognitive efficiency of mapping between representations associated with a word, or by changing the granularity of phonological processing of spoken language, or through a combination of both. Behavioural effects of literacy have typically been assessed in offline explicit tasks that have addressed only phonological processing. However, a recent eye tracking study compared high and low literate participants on effects of phonology and semantics in processing measured implicitly using eye movements. High literates' eye movements were more affected by phonological overlap in online speech than low literates, with only subtle differences observed in semantics. We determined whether these effects were due to cognitive efficiency and/or granularity of speech processing in a multimodal model of speech processing -the amodal shared resource model (ASR, Smith, Monaghan, & Huettig, 2013a,b). We found that cognitive efficiency in the model had only a marginal effect on semantic processing and did not affect performance for phonological processing, whereas fine-grained versus coarse-grained

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Connectionist Approaches to Reading

Plaut

The Science of Reading: A Handbook

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Reading is a highly complex task involving the rapid coordination of visual, phonological, semantic, and linguistic processes. Computational models have played a key role in the scientific study of reading. These models allow us to explore the implications of specific hypotheses concerning the representations and processes underlying reading acquisition and performance. A particular form of computational modeling, known as connectionist or neural network modeling, offers the further advantage of being explicit about how such mechanisms might be implemented in the brain.In connectionist models, cognitive processes take the form of cooperative and competitive interactions among large numbers of simple neuron-like processing units. Typically, each unit has a real-valued activity level, roughly analogous to the firing rate of a neuron. Unit interactions are governed by weighted connections that encode the longterm knowledge of the system and are learned gradually through experience. Units are often organized into layers or groups; the activity of some groups of units encode the input to the system; the resulting activity of other groups of units encodes the system's response to that input. For example, one group might encode the written form (orthography) of a word, another might encode its spoken form (phonology), and a third might encode its meaning (semantics; see figure 2.1). The patterns of activity of the remaining groups of units -sometimes termed "hidden" units -constitute learned, internal representations that mediate between inputs and outputs. In this way, the connectionist approach attempts to capture the essential computational properties of the vast ensembles of real neuronal elements found in the brain using simulations of smaller networks of more abstract units. By linking neural computation to behavior, the framework enables developmental, cognitive, and neurobiological issues to be addressed within a single, integrated formalism. One very important advantage of connectionist models is that they deal explicitly with learning. Though many of these models have focused predominantly on simulating aspects of adult, rather than children's, reading, many of the models do explicitly consider the process of learning (e.g.,

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Computing the Meanings of Words in Reading: Cooperative Division of Labor Between Visual and Phonological Processes.

Cited by 966 publications

References 135 publications

Some neurophysiological constraints on models of word naming

Some neurophysiological constraints on models of word naming

Literacy effects on language and vision: Emergent effects from an amodal shared resource (ASR) computational model

Connectionist Approaches to Reading

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