Inter-trial effects in priming of pop-out: Comparison of computational updating models

dc.authoridGokce, Ahu/0000-0002-5032-7007
dc.authoridShi, Zhuanghua/0000-0003-2388-6695
dc.authoridAllenmark, Fredrik/0000-0002-3127-4851
dc.authoridMuller, Hermann J./0000-0002-4774-5654
dc.authorwosidGokce, Ahu/GRE-8084-2022
dc.authorwosidShi, Zhuanghua/J-2236-2019
dc.authorwosidAllenmark, Fredrik/V-7301-2019
dc.authorwosidAllenmark, Fredrik/GQB-0575-2022
dc.contributor.authorGökçe, Ahu
dc.contributor.authorGokce, Ahu
dc.contributor.authorGeyer, Thomas
dc.contributor.authorZinchenko, Artyom
dc.contributor.authorMueller, Hermann J.
dc.contributor.authorShi, Zhuanghua
dc.date.accessioned2023-10-19T15:12:16Z
dc.date.available2023-10-19T15:12:16Z
dc.date.issued2021
dc.department-temp[Allenmark, Fredrik; Geyer, Thomas; Zinchenko, Artyom; Mueller, Hermann J.; Shi, Zhuanghua] Ludwig Maximilians Univ Munchen, Dept Psychol, Munich, Germany; [Gokce, Ahu] Kadir Has Univ, Dept Psychol, Istanbul, Turkeyen_US
dc.description.abstractIn visual search tasks, repeating features or the position of the target results in faster response times. Such inter-trial 'priming' effects occur not just for repetitions from the immediately preceding trial but also from trials further back. A paradigm known to produce particularly long-lasting inter-trial effects-of the target-defining feature, target position, and response (feature)-is the 'priming of pop-out' (PoP) paradigm, which typically uses sparse search displays and random swapping across trials of target- and distractor-defining features. However, the mechanisms underlying these inter-trial effects are still not well understood. To address this, we applied a modeling framework combining an evidence accumulation (EA) model with different computational updating rules of the model parameters (i.e., the drift rate and starting point of EA) for different aspects of stimulus history, to data from a (previously published) PoP study that had revealed significant inter-trial effects from several trials back for repetitions of the target color, the target position, and (response-critical) target feature. By performing a systematic model comparison, we aimed to determine which EA model parameter and which updating rule for that parameter best accounts for each inter-trial effect and the associated n-back temporal profile. We found that, in general, our modeling framework could accurately predict the n-back temporal profiles. Further, target color- and position-based inter-trial effects were best understood as arising from redistribution of a limited-capacity weight resource which determines the EA rate. In contrast, response-based inter-trial effects were best explained by a bias of the starting point towards the response associated with a previous target; this bias appeared largely tied to the position of the target. These findings elucidate how our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance. Author summary In many perceptual tasks, performance is faster and more accurate when critical stimulus attributes are repeated from trial to trial compared to when they change. Priming of pop-out (PoP), visual search with sparse search displays and random swapping of the target feature between trials, is a paradigm in which such inter-trial effects can be traced back over several recent trial episodes. While many studies have explored PoP paradigms, the mechanisms underlying priming of the search-critical target feature, the target position, and the response-critical information are not yet fully understood. Here, we addressed this question by applying evidence accumulation (EA) decision models to the data from a previously published PoP study. The modeling framework combines evidence accumulation with Bayesian updating of the model parameters. Comparison of (> 1000) different combinations of decision models and updating rules revealed that the featural and positional priming effects were best explained by assuming that attentional weight resources are dynamically redistributed based on the recent history of target color and position, whereas response decisions are biased based on the recent history of the response-critical property of targets occuring at a particular (and nearby) position(s). These findings confirm that our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance.en_US
dc.description.sponsorshipGerman research foundation [DFG MU773/16-2, DFG SH166/3-2]en_US
dc.description.sponsorshipThis work was supported by German research foundation DFG MU773/16-2 (http://gepris.dfg.de/gepris/projekt/277137374) to HJM and DFG SH166/3-2 to ZS (http://gepris.dfg.de/gepris/projekt/277161151).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.citation5
dc.identifier.doi10.1371/journal.pcbi.1009332en_US
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.issue9en_US
dc.identifier.pmid34478446en_US
dc.identifier.scopus2-s2.0-85114417465en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1371/journal.pcbi.1009332
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5394
dc.identifier.volume17en_US
dc.identifier.wosWOS:000724181600003en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Computational Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectShort-Term-MemoryEn_Us
dc.subjectVisual-SearchEn_Us
dc.subjectFeature TargetsEn_Us
dc.subjectReaction-TimeEn_Us
dc.subjectAttentionEn_Us
dc.subjectRepetitionEn_Us
dc.subjectDimensionEn_Us
dc.subjectStimulusEn_Us
dc.subjectCaptureEn_Us
dc.subjectRetrievalEn_Us
dc.subjectShort-Term-Memory
dc.subjectVisual-Search
dc.subjectFeature Targets
dc.subjectReaction-Time
dc.subjectAttention
dc.subjectRepetition
dc.subjectDimension
dc.subjectStimulus
dc.subjectCapture
dc.subjectRetrieval
dc.titleInter-trial effects in priming of pop-out: Comparison of computational updating modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication87ce03f1-4fee-4079-a431-0811f59885ad
relation.isAuthorOfPublication.latestForDiscovery87ce03f1-4fee-4079-a431-0811f59885ad

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