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The semantic Stroop effect: An ex-Gaussian analysis

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Title
The semantic Stroop effect: An ex-Gaussian analysis
Published in
Psychonomic Bulletin & Review, February 2016
DOI 10.3758/s13423-016-1014-9
Pubmed ID
Authors

Darcy White, Evan F. Risko, Derek Besner

Abstract

Previous analyses of the standard Stroop effect (which typically uses color words that form part of the response set) have documented effects on mean reaction times in hundreds of experiments in the literature. Less well known is the fact that ex-Gaussian analyses reveal that such effects are seen in (a) the mean of the normal distribution (mu), as well as in (b) the standard deviation of the normal distribution (sigma) and (c) the tail (tau). No ex-Gaussian analysis exists in the literature with respect to the semantically based Stroop effect (which contrasts incongruent color-associated words with, e.g., neutral controls). In the present experiments, we investigated whether the semantically based Stroop effect is also seen in the three ex-Gaussian parameters. Replicating previous reports, color naming was slower when the color was carried by an irrelevant (but incongruent) color-associated word (e.g., sky, tomato) than when the control items consisted of neutral words (e.g., keg, palace) in each of four experiments. An ex-Gaussian analysis revealed that this semantically based Stroop effect was restricted to the arithmetic mean and mu; no semantic Stroop effect was observed in tau. These data are consistent with the views (1) that there is a clear difference in the source of the semantic Stroop effect, as compared to the standard Stroop effect (evidenced by the presence vs. absence of an effect on tau), and (2) that interference associated with response competition on incongruent trials in tau is absent in the semantic Stroop effect.

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The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Ph. D. Student 7 17%
Student > Bachelor 4 10%
Student > Master 4 10%
Student > Doctoral Student 2 5%
Other 3 7%
Unknown 13 32%
Readers by discipline Count As %
Psychology 19 46%
Computer Science 3 7%
Arts and Humanities 2 5%
Neuroscience 2 5%
Social Sciences 1 2%
Other 1 2%
Unknown 13 32%