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Computational modeling of the negative priming effect based on inhibition patterns and working memory

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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Title
Computational modeling of the negative priming effect based on inhibition patterns and working memory
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00166
Pubmed ID
Authors

Dongil Chung, Amir Raz, Jaewon Lee, Jaeseung Jeong

Abstract

Negative priming (NP), slowing down of the response for target stimuli that have been previously exposed, but ignored, has been reported in multiple psychological paradigms including the Stroop task. Although NP likely results from the interplay of selective attention, episodic memory retrieval, working memory, and inhibition mechanisms, a comprehensive theoretical account of NP is currently unavailable. This lacuna may result from the complexity of stimuli combinations in NP. Thus, we aimed to investigate the presence of different degrees of the NP effect according to prime-probe combinations within a classic Stroop task. We recorded reaction times (RTs) from 66 healthy participants during Stroop task performance and examined three different NP subtypes, defined according to the type of the Stroop probe in prime-probe pairs. Our findings show significant RT differences among NP subtypes that are putatively due to the presence of differential disinhibition, i.e., release from inhibition. Among the several potential origins for differential subtypes of NP, we investigated the involvement of selective attention and/or working memory using a parallel distributed processing (PDP) model (employing selective attention only) and a modified PDP model with working memory (PDP-WM, employing both selective attention and working memory). Our findings demonstrate that, unlike the conventional PDP model, the PDP-WM successfully simulates different levels of NP effects that closely follow the behavioral data. This outcome suggests that working memory engages in the re-accumulation of the evidence for target response and induces differential NP effects. Our computational model complements earlier efforts and may pave the road to further insights into an integrated theoretical account of complex NP effects.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Researcher 5 15%
Student > Master 5 15%
Student > Bachelor 3 9%
Professor 3 9%
Other 5 15%
Unknown 6 18%
Readers by discipline Count As %
Psychology 14 42%
Neuroscience 4 12%
Medicine and Dentistry 3 9%
Engineering 2 6%
Agricultural and Biological Sciences 1 3%
Other 2 6%
Unknown 7 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 December 2013.
All research outputs
#17,286,645
of 25,374,917 outputs
Outputs from Frontiers in Computational Neuroscience
#919
of 1,463 outputs
Outputs of similar age
#193,613
of 289,007 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#76
of 139 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,463 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 289,007 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.