2016
DOI: 10.3389/fpsyg.2016.01034
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Semantic Neighborhood Effects for Abstract versus Concrete Words

Abstract: Studies show that semantic effects may be task-specific, and thus, that semantic representations are flexible and dynamic. Such findings are critical to the development of a comprehensive theory of semantic processing in visual word recognition, which should arguably account for how semantic effects may vary by task. It has been suggested that semantic effects are more directly examined using tasks that explicitly require meaning processing relative to those for which meaning processing is not necessary (e.g.,… Show more

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Cited by 27 publications
(25 citation statements)
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References 71 publications
(101 reference statements)
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“…The WINDSORS measure of SND has been recently tested in some psycholinguistic tasks. For instance, Danguecan and Buchanan (2016) found an inhibitory effect from near neighbours in a lexical decision task; words from dense semantic spaces, or high SND words, were processed slower than words from sparse semantic spaces, or low SND words. MacDonald (2013) replicated the inhibitory effect in both young (18-25 years old) and older (60-80 years old) adults.…”
Section: Methodsmentioning
confidence: 99%
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“…The WINDSORS measure of SND has been recently tested in some psycholinguistic tasks. For instance, Danguecan and Buchanan (2016) found an inhibitory effect from near neighbours in a lexical decision task; words from dense semantic spaces, or high SND words, were processed slower than words from sparse semantic spaces, or low SND words. MacDonald (2013) replicated the inhibitory effect in both young (18-25 years old) and older (60-80 years old) adults.…”
Section: Methodsmentioning
confidence: 99%
“…Thus the experimental conditions were abstract topic-concrete vehicle, high SND (abstract-high SND); abstract-topic-concrete vehicle, low SND (abstract-low SND); concrete topicconcrete vehicle, high SND (concrete-high SND); and concrete topic-concrete vehicle, low SND (concrete-low SND). Some of the metaphors were borrowed or modified by others' work (Danguecan & Buchanan, 2016;Katz, Paivio, Marschark, & Clark, 1988;Xu, 2010). However, it was difficult to find or create many metaphors which would fit with our conditions; consequently, only 12 items were used per condition.…”
Section: Methodsmentioning
confidence: 99%
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“…For example, the WINDSORS model of semantic space (Durda and Buchanan 2008) is a variant of HAL that aims to prevent artificially dense neighborhoods for high-frequency words. Dangeucan and Buchanan (2016) used the WINDSORS model to estimate semantic neighborhood densities for both concrete and abstract words and compared high- and low-density words for several behavioral tasks. The authors found that semantic neighborhood density effects were task-specific, such that high neighborhood density influenced reaction times (RTs) during a lexical decision (LD) task and go/no-go LD task, but not more semantic tasks such as sentence relatedness.…”
Section: Introductionmentioning
confidence: 99%
“…However, the stimuli used by Dangeucan & Buchanan (2016) were not balanced for any quantitative measure of concreteness across high and low neighborhood density levels. An analysis of their stimuli using a comprehensive set of concreteness ratings (Brysbaert, Warriner, & Kuperman 2014) reveals a significant interaction between the Concrete/Abstract and High/Low Density conditions (F[1] = 6.54, p = 0.012) such that although high- and low-density concrete words were balanced for concreteness, abstract words were not.…”
Section: Introductionmentioning
confidence: 99%