Are there mental lexicons? The role of semantics in lexical decision - PubMed (original) (raw)
Are there mental lexicons? The role of semantics in lexical decision
Katia Dilkina et al. Brain Res. 2010.
Abstract
What is the underlying representation of lexical knowledge? How do we know whether a given string of letters is a word, whereas another string of letters is not? There are two competing models of lexical processing in the literature. The first proposes that we rely on mental lexicons. The second claims there are no mental lexicons; we identify certain items as words based on semantic knowledge. Thus, the former approach - the multiple-systems view - posits that lexical and semantic processing are subserved by separate systems, whereas the latter approach - the single-system view - holds that the two are interdependent. Semantic dementia patients, who have a cross-modal semantic impairment, show an accompanying and related lexical deficit. These findings support the single-system approach. However, a report of an SD patient whose impairment on lexical decision was not related to his semantic deficits in item-specific ways has presented a challenge to this view. If the two types of processing rely on a common system, then shouldn't damage impair the same items on all tasks? We present a single-system model of lexical and semantic processing, where there are no lexicons, and performance on lexical decision involves the activation of semantic representations. We show how, when these representations are damaged, accuracy on semantic and lexical tasks falls off together, but not necessarily on the same set of items. These findings are congruent with the patient data. We provide an explicit explanation of this pattern of results in our model, by defining and measuring the effects of two orthogonal factors - spelling consistency and concept consistency.
Copyright © 2010 Elsevier B.V. All rights reserved.
Figures
Figure 1
Architecture of the connectionist model of semantic and lexical processing: dashed lines signify connections with large hard-coded positive weights from the units in the control layer to each of the six processing layers; solid lines signify connections with small randomly-initiated weights, which are learned over the course of training; arrows indicate direction of connectivity.
Figure 2
Lexical decision performance on W>NW pairs and W
Figure 3
Effectof spelling typicality in the two pathways involved in lexical decision. NOTE: log odds of 0 marks chance level.
Figure 4
Correlations between lexical decision and other semantic and lexical tasks performed by the network.
Figure 5
Effects of spelling consistency and concept consistency on the seven tasks in the model. Effect = performance on consistent items – performance on inconsistent items. (5a) main effect of spelling consistency; (5b) main effect of concept consistency; (5c) interaction between spelling consistency and concept consistency. (*** p<.0005; ** p<.005; * p<.05)
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