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

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...

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Autores principales:	Fredrik Allenmark, Ahu Gokce, Thomas Geyer, Artyom Zinchenko, Hermann J Müller, Zhuanghua Shi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/3a1502d6466e43049f4dc64c6df441fd
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spelling	oai:doaj.org-article:3a1502d6466e43049f4dc64c6df441fd2021-12-02T19:57:51ZInter-trial effects in priming of pop-out: Comparison of computational updating models.1553-734X1553-735810.1371/journal.pcbi.1009332https://doaj.org/article/3a1502d6466e43049f4dc64c6df441fd2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009332https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In 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.Fredrik AllenmarkAhu GokceThomas GeyerArtyom ZinchenkoHermann J MüllerZhuanghua ShiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009332 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Fredrik Allenmark Ahu Gokce Thomas Geyer Artyom Zinchenko Hermann J Müller Zhuanghua Shi Inter-trial effects in priming of pop-out: Comparison of computational updating models.
description	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.
format	article
author	Fredrik Allenmark Ahu Gokce Thomas Geyer Artyom Zinchenko Hermann J Müller Zhuanghua Shi
author_facet	Fredrik Allenmark Ahu Gokce Thomas Geyer Artyom Zinchenko Hermann J Müller Zhuanghua Shi
author_sort	Fredrik Allenmark
title	Inter-trial effects in priming of pop-out: Comparison of computational updating models.
title_short	Inter-trial effects in priming of pop-out: Comparison of computational updating models.
title_full	Inter-trial effects in priming of pop-out: Comparison of computational updating models.
title_fullStr	Inter-trial effects in priming of pop-out: Comparison of computational updating models.
title_full_unstemmed	Inter-trial effects in priming of pop-out: Comparison of computational updating models.
title_sort	inter-trial effects in priming of pop-out: comparison of computational updating models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3a1502d6466e43049f4dc64c6df441fd
work_keys_str_mv	AT fredrikallenmark intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels AT ahugokce intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels AT thomasgeyer intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels AT artyomzinchenko intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels AT hermannjmuller intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels AT zhuanghuashi intertrialeffectsinprimingofpopoutcomparisonofcomputationalupdatingmodels
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Inter-trial effects in priming of pop-out: Comparison of computational updating models.

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