Models of errors of omission in aphasic naming

Dell, Gary S.; Lawler, Elisa N.; Harris, Harlan D.; Gordon, Jean

doi:10.1080/02643290342000320

Cited by 114 publications

(94 citation statements)

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“…This finding is inconsistent with the account put forward by Newton and Barry, because dense semantic neighborhoods containing many semantic competitors should elicit fewer rather than more omissions (Bormann et al, 2008;Dell et al, 2004). Second, when participants were provided with cues as to the number of letters in a word, the number of correct retrievals increased significantly, and the probability of failure at Step 1 was significantly reduced.…”

Section: Discussioncontrasting

confidence: 55%

“…Their claim is compatible with Foygel and Dell's (2000) model of word production. In this model, omissions reflect a failure to activate sufficiently strongly any lexical representation (e.g., Dell, Lawler, Harris, & Gordon, 2004;Laine, Tikkala, & Juhola, 1998). Alternates reflect activation of the lexical representation of a different word from the target item, as a result of insufficient activation of the appropriate lexical representation relative to a competitor.…”

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confidence: 99%

“…Newton and Barry (1997) therefore believed that the problems with abstract words on the definition task occur during lexical access, because more semantic competitors are activated for abstract words. In a simulation using Foygel and Dell's (2000) model, Dell et al (2004) examined the effect of varying the number of semantic competitors on word production. The simulation showed that an additional semantic competitor made the model produce more semantic errors and fewer omissions.…”

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Concreteness and word production

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Two experiments are reported that investigated the effect of concreteness on the ability to generate words to fit sentence contexts. When participants attempted to retrieve words from dictionary definitions in Experiment 1, abstract words were associated with more omissions and more alternates than were concrete words. These findings are consistent with the view that the semantic-lexical weights in the word production system are weaker for abstract than for concrete words. We found no evidence that greater competition from semantic neighbors was an additional reason why abstract words were harder to produce. Participants also reported more positive tip-of-the-tongue states (TOTs) when attempting to produce abstract words from their definitions, consistent with more phonological retrieval problems for abstract than for concrete words. In Experiment 2, participants attempted to generate words to fit into a sentence that described a specific event. The difference between the numbers of abstract and concrete words recalled was significantly smaller in the event condition than in the definition condition, and evidence no longer emerged of greater phonological retrieval failure for abstract words. Overall, the results are consistent with the view that the semantic-lexical weights, but not the lexicalphonological weights, are weaker for abstract than for concrete words in the word production system. Keywords Concreteness effect . Word production . Tip-of-the-tongue statesIn this study, we investigated the way in which rated concreteness influences the processes that are involved in word production. Experiments examining the effects on word production of variables such as frequency and age of acquisition have often employed tests of picture naming (e.g., Johnston, Dent, Humphreys, & Barry, 2010). Because it is not feasible to present abstract words in pictorial form, a few studies have instead used definition tasks to compare concrete and abstract word retrieval (Allen & Hulme, 2006;Hanley & Kay, 1997;Newton & Barry, 1997). Participants are presented with dictionary definitions of concrete and abstract words, and they attempt to recall the word that fits each definition. Allen and Hulme reported significantly better concrete than abstract word production on this task, even though their sets of concrete and abstract words were matched for frequency, morphemic complexity, age of acquisition, familiarity, neighborhood size, and word length.As Allen and Hulme (2006) pointed out, generating words from dictionary definitions involves several distinct processes. It first requires access to a target word's semantic representation in memory. In terms of a model of word production such as that put forward by Foygel and Dell (2000), activation of the word's lexical representation can then take place from its semantic representation, followed by activation of the word's phonological features. Gollan and Brown (2006) argued that the kinds of errors that participants make during word production make it possible to distinguis...

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Section: Discussioncontrasting

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Concreteness and word production

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“…(2000) two-step interactive account (TSIA; see also Dell, Lawler, Harris, & Gordon, 2004;Schwartz, Dell, Martin, Gahl, & Sobel, 2006). These are depicted on the right and left sides of Fig.…”

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confidence: 99%

“…Artificial case series were generated using simulations of (a) Foygel and Dell's (2000) TSIA, (b)a theory in which speech errors arise prior to the two steps of lexical access assumed in TSIA, and (c) Rapp and Goldrick's (2000) restricted interaction account, which differs from TSIA in the strength and nature of feedback. When the parameter-fitting procedure of Dell et al (2004) was then used to fit the TSIA to each of these artificial case series, and the degrees of fit were equivalent for all three artificial case series. Thus, with respect to overall response distributions, TSIA was able to fit data generated by a TSIA simulation just as well as data generated by simulations of distinct theoretical accounts.…”

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