Inter-Trial Effects in Priming of Pop-Out: Comparison of Computational Updating Models

dc.authorid Gokce, Ahu/0000-0002-5032-7007
dc.authorid Shi, Zhuanghua/0000-0003-2388-6695
dc.authorid Allenmark, Fredrik/0000-0002-3127-4851
dc.authorid Muller, Hermann J./0000-0002-4774-5654
dc.authorwosid Gokce, Ahu/GRE-8084-2022
dc.authorwosid Shi, Zhuanghua/J-2236-2019
dc.authorwosid Allenmark, Fredrik/V-7301-2019
dc.authorwosid Allenmark, Fredrik/GQB-0575-2022
dc.contributor.author Allenmark, Fredrik
dc.contributor.author Gökçe, Ahu
dc.contributor.author Gokce, Ahu
dc.contributor.author Geyer, Thomas
dc.contributor.author Zinchenko, Artyom
dc.contributor.author Mueller, Hermann J.
dc.contributor.author Shi, Zhuanghua
dc.contributor.other Psychology
dc.date.accessioned 2023-10-19T15:12:16Z
dc.date.available 2023-10-19T15:12:16Z
dc.date.issued 2021
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, Turkey en_US
dc.description.abstract In 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.sponsorship German research foundation [DFG MU773/16-2, DFG SH166/3-2] en_US
dc.description.sponsorship This 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.citationcount 5
dc.identifier.doi 10.1371/journal.pcbi.1009332 en_US
dc.identifier.issn 1553-734X
dc.identifier.issn 1553-7358
dc.identifier.issue 9 en_US
dc.identifier.pmid 34478446 en_US
dc.identifier.scopus 2-s2.0-85114417465 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1371/journal.pcbi.1009332
dc.identifier.uri https://hdl.handle.net/20.500.12469/5394
dc.identifier.volume 17 en_US
dc.identifier.wos WOS:000724181600003 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Public Library Science en_US
dc.relation.ispartof Plos Computational Biology en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 8
dc.subject Short-Term-Memory En_Us
dc.subject Visual-Search En_Us
dc.subject Feature Targets En_Us
dc.subject Reaction-Time En_Us
dc.subject Attention En_Us
dc.subject Repetition En_Us
dc.subject Dimension En_Us
dc.subject Stimulus En_Us
dc.subject Capture En_Us
dc.subject Retrieval En_Us
dc.subject Short-Term-Memory
dc.subject Visual-Search
dc.subject Feature Targets
dc.subject Reaction-Time
dc.subject Attention
dc.subject Repetition
dc.subject Dimension
dc.subject Stimulus
dc.subject Capture
dc.subject Retrieval
dc.title Inter-Trial Effects in Priming of Pop-Out: Comparison of Computational Updating Models en_US
dc.type Article en_US
dc.wos.citedbyCount 9
dspace.entity.type Publication
relation.isAuthorOfPublication 87ce03f1-4fee-4079-a431-0811f59885ad
relation.isAuthorOfPublication.latestForDiscovery 87ce03f1-4fee-4079-a431-0811f59885ad
relation.isOrgUnitOfPublication 9390486a-b1dc-46cf-ad5f-31415f0c8b95
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