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Within the connectionist triangle model of reading aloud, interaction between semantic and phonological representations occurs for all words but is particularly important for correct pronunciation of lower frequency exception words. This framework therefore predicts that (a) semantic dementia, which compromises semantic knowledge, should be accompanied by surface dyslexia, a frequency-modulated deficit in exception word reading, and (b) there should be a significant relationship between the severity of semantic degradation and the severity of surface dyslexia. The authors evaluated these claims with reference to 100 observations of reading data from 51 cases of semantic dementia. Surface dyslexia was rampant, and a simple composite semantic measure accounted for half of the variance in low-frequency exception word reading. Although in 3 cases initial testing revealed a moderate semantic impairment but normal exception word reading, all of these became surface dyslexic as their semantic knowledge deteriorated further. The connectionist account attributes such cases to premorbid individual variation in semantic reliance for accurate exception word reading. These results provide a striking demonstration of the association between semantic dementia and surface dyslexia, a phenomenon that the authors have dubbed SD-squared.

The connectionist triangle model of reading aloud proposes that semantic activation of phonology is particularly important for correct pronunciation of low-frequency exception words. Our consideration of this issue (Woollams, Lambon Ralph, Plant, & Patterson, 2007) reported computational simulations demonstrating that reduction and disruption of this semantic activation resulted in the marked deficit in low-frequency exception word reading that is characteristic of surface dyslexia. We then presented 100 observations of reading aloud from 51 patients with semantic dementia (SD) demonstrating a universal decline into surface dyslexia, a phenomenon we termed 'SD-squared.' Coltheart, Tree, and Saunders (2010) have more recently provided a simulation of the SD-squared data within the dual route cascaded (DRC) model, achieved by varying the amount of damage to components of the lexical and nonlexical pathways. Although they suggested that these simulations provide a closer fit to the SD patients' reading data than our own, we demonstrate here that this is not the case. Moreover, we argue that the connectionist triangle model account has substantially greater explanatory and predictive power than the DRC account. Keywords: surface dyslexia, semantic memory, reading aloud, frequency, regularity DOI: 10.1037/a0017641

Within the connectionist triangle model of reading aloud, interaction between semantic and phonological representations occurs for all words but is particularly important for correct pronunciation of lower frequency exception words. This framework therefore predicts that (a) semantic dementia, which compromises semantic knowledge, should be accompanied by surface dyslexia, a frequency-modulated deficit in exception word reading, and (b) there should be a significant relationship between the severity of semantic degradation and the severity of surface dyslexia. The authors evaluated these claims with reference to 100 observations of reading data from 51 cases of semantic dementia. Surface dyslexia was rampant, and a simple composite semantic measure accounted for half of the variance in low-frequency exception word reading. Although in 3 cases initial testing revealed a moderate semantic impairment but normal exception word reading, all of these became surface dyslexic as their semantic knowledge deteriorated further. The connectionist account attributes such cases to premorbid individual variation in semantic reliance for accurate exception word reading. These results provide a striking demonstration of the association between semantic dementia and surface dyslexia, a phenomenon that the authors have dubbed SD-squared. Keywords: surface dyslexia, semantic memory, reading aloud, frequency, regularity

Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual representations of objects and their verbal descriptions. To test the theory, they trained the model to associate names, verbal descriptions, and visual representations of objects. When its inputs and outputs are constructed to capture aspects of structure apparent in attribute-norming experiments, the model provides an intuitive account of semantic task performance. The authors then used the model to understand the structure of impaired performance in patients with selective and progressive impairments of conceptual knowledge. Data from 4 well-known semantic tasks revealed consistent patterns that find a ready explanation in the model. The relationship between the model and related theories of semantic representation is discussed.

Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition. [ABSTRACT FROM AUTHOR]