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Modeling implicit learning in a cross-modal audio-visual serial reaction time task
Taesler, Philipp1; Jablonowski, Julia1; Fu, Qiufang2; Rose, Michael1
Corresponding AuthorTaesler, Philipp(p.taesler@uke.de)
2019-05-01
Source PublicationCOGNITIVE SYSTEMS RESEARCH
ISSN1389-0417
Volume54Pages:154-164
AbstractThis study examined implicit learning in a cross-modal condition, where visual and auditory stimuli were presented in an alternating fashion. Each cross-modal transition occurred with a probability of 0.85, enabling participants to gain a reaction time benefit by learning the cross-modal predictive information between colors and tones. Motor responses were randomly remapped to ensure that pure perceptual learning took place. The effect for the implicit learning was extracted from the data by fitting five different models to the data, which was highly variable due to motor variability. To examine individual learning rates for stimulus types of different discriminability and modality, the models were fitted per stimulus type and individually for each participant. The model selection identified the model that included motor variability, surprise effects for deviants and a serial position for effect onset as the most explanatory (Akaike weight 0.87). Further, there was a significant global cross-modal implicit learning effect for predictable versus deviant transitions (40 ms reaction time difference, p < 0.004). The learning rates over time differed for both modality and the stimuli within modalities, although there was no correlation to global error rates or reaction time differences between the stimulus types. These results demonstrate a modeling method that is well suited to extract detailed information about the success of implicit learning from high variability data. It further shows a cross-modal implicit learning effect, which extends the understanding of the implicit learning system and highlights the possibility for information to be processed in a cross-modal representation without conscious processing. (C) 2018 Elsevier B.V. All rights reserved.
KeywordImplicit learning Cross-modal Modeling Serial reaction time task Audio-visual
DOI10.1016/j.cogsys.2018.10.002
Indexed BySCI
Language英语
Funding OrganizationGerman Research Foundation DFG Crossmodal Learning: Adaptivity, Prediction and Interaction
Funding ProjectGerman Research Foundation DFG Crossmodal Learning: Adaptivity, Prediction and Interaction[TRR 169] ; German Research Foundation DFG Crossmodal Learning: Adaptivity, Prediction and Interaction[TRR 169]
WOS Research AreaComputer Science ; Neurosciences & Neurology ; Psychology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences ; Psychology, Experimental
WOS IDWOS:000455740800012
PublisherELSEVIER SCIENCE BV
WOS KeywordMECHANISMS ; SEQUENCES ; SELECTION ; EXPLICIT
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27768
Collection脑与认知科学国家重点实验室
Corresponding AuthorTaesler, Philipp
Affiliation1.Univ Med Ctr Hamburg Eppendorf, Inst Syst Neurosci, Martinistr 52,Bldg W34,320b, Hamburg, Germany
2.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Taesler, Philipp,Jablonowski, Julia,Fu, Qiufang,et al. Modeling implicit learning in a cross-modal audio-visual serial reaction time task[J]. COGNITIVE SYSTEMS RESEARCH,2019,54:154-164.
APA Taesler, Philipp,Jablonowski, Julia,Fu, Qiufang,&Rose, Michael.(2019).Modeling implicit learning in a cross-modal audio-visual serial reaction time task.COGNITIVE SYSTEMS RESEARCH,54,154-164.
MLA Taesler, Philipp,et al."Modeling implicit learning in a cross-modal audio-visual serial reaction time task".COGNITIVE SYSTEMS RESEARCH 54(2019):154-164.
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