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

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dc.contributor.authorChung, Dongilen
dc.contributor.authorRaz, Amiren
dc.contributor.authorLee, Jaewonen
dc.contributor.authorJeong, Jaeseungen
dc.date.accessioned2019-06-03T21:02:52Zen
dc.date.available2019-06-03T21:02:52Zen
dc.date.issued2013-11-19en
dc.description.abstractNegative 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.en
dc.description.sponsorshipThis work was supported by CHUNG MoonSoul Research Center for BioInformation and BioElectronics (CMSC) in KAIST, the Korea Science and Engineering Foundation (KOSEF), and the National Research Foundation of Korea (NRF) grants funded by the Korea government (MOST) (No. R01-2007-000-21094-0, No. M10644000028-06N4400-02810, and No. NRF-2006-2005399) (Jaeseung Jeong).en
dc.format.extent12 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fncom.2013.00166en
dc.identifier.urihttp://hdl.handle.net/10919/89700en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectnegative primingen
dc.subjectdisinhibitionen
dc.subjectStroop tasken
dc.subjectworking memoryen
dc.subjectparallel distributed processingen
dc.titleComputational modeling of the negative priming effect based on inhibition patterns and working memoryen
dc.title.serialFrontiers in Computational Neuroscienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Destination Area: Adaptive Brain and Behavior (ABB)