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      Why Does Dual-Tasking Hamper Implicit Sequence Learning?

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          Abstract

          Research on the limitations of dual-tasking might profit from using setups with a predictable sequence of stimuli and responses and assessing the acquisition of this sequence. Detrimental effects of dual-tasking on implicit sequence learning in the serial reaction time task (SRTT; Nissen & Bullemer, 1987) – when paired with an uncorrelated task – have been attributed to participants’ lack of separating the streams of events in either task. Assuming that co-occurring events are automatically integrated, we reasoned that participants could need to first learn which events co-occur, before they can acquire sequence knowledge. In the training phase, we paired an 8-element visual-manual SRTT with an auditory-vocal task. Afterwards, we tested under single-tasking conditions whether SRTT sequence knowledge had been acquired. By applying different variants of probabilistic SRTT-tone pairings across three experiments, we tested what type of predictive relationship was needed to preserve sequence learning. In Experiment 1, where half of the SRTT-elements were paired to 100% with one specific tone and the other half randomly, only the fixedly paired elements were learned. Yet, no sequence learning was found when each of the eight SRTT-elements was paired with tone identity in a 75%–25% ratio (Experiment 2). Sequence learning was, however, intact when the 75%–25% ratio was applied to the four SRTT target locations instead (Experiment 3). The results suggest that participants (when lacking a separation of the task representations while dual-tasking) can learn a sequence inherent in one of two tasks to the extent that across-task contingencies can be learned first.

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          Attentional requirements of learning: Evidence from performance measures

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            Using confidence intervals in within-subject designs.

            We argue that to best comprehend many data sets, plotting judiciously selected sample statistics with associated confidence intervals can usefully supplement, or even replace, standard hypothesis-testing procedures. We note that most social science statistics textbooks limit discussion of confidence intervals to their use in between-subject designs. Our central purpose in this article is to describe how to compute an analogous confidence interval that can be used in within-subject designs. This confidence interval rests on the reasoning that because between-subject variance typically plays no role in statistical analyses of within-subject designs, it can legitimately be ignored; hence, an appropriate confidence interval can be based on the standard within-subject error term-that is, on the variability due to the subject × condition interaction. Computation of such a confidence interval is simple and is embodied in Equation 2 on p. 482 of this article. This confidence interval has two useful properties. First, it is based on the same error term as is the corresponding analysis of variance, and hence leads to comparable conclusions. Second, it is related by a known factor (√2) to a confidence interval of the difference between sample means; accordingly, it can be used to infer the faith one can put in some pattern of sample means as a reflection of the underlying pattern of population means. These two properties correspond to analogous properties of the more widely used between-subject confidence interval.
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              Using Bayes to get the most out of non-significant results

              No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.
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                Author and article information

                Contributors
                Journal
                J Cogn
                J Cogn
                2514-4820
                Journal of Cognition
                Ubiquity Press
                2514-4820
                07 January 2021
                2021
                : 4
                : 1
                : 1
                Affiliations
                [1 ]Department of Psychology, University of Bremen, Hochschulring 18, 28359 Bremen, DE
                [2 ]Department of Psychology, FernUniversität in Hagen, Universitätsstr. 33, 58084 Hagen, DE
                [3 ]Department of Psychology, University of Cologne, Richard-Strauss-Str. 2, 50931 Köln, DE
                Author notes
                CORRESPONDING AUTHOR: Eva Röttger Department of Psychology, University of Bremen, Hochschulring 18, 28359 Bremen, DE eva.roettger@ 123456uni-bremen.de
                Author information
                http://orcid.org/0000-0003-0794-3274
                http://orcid.org/0000-0001-9363-2581
                http://orcid.org/0000-0002-8576-5330
                http://orcid.org/0000-0001-7293-3166
                Article
                10.5334/joc.136
                7792471
                33506167
                8685ef4c-5b87-4ad5-b22a-a523f93a2b59
                Copyright: © 2021 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 January 2020
                : 15 October 2020
                Funding
                Funded by: German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), doi open-funder-registry10.13039/open_funder_registry10.13039/501100001659;
                Award ID: HA 5447/11-1
                Award ID: GA 2246/1-1
                This research was supported by grants within the Priority Program, SPP 1772 from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). Grants HA 5447/11-1 (Hilde Haider) and GA 2246/1-1 (Robert Gaschler).
                Categories
                Research Article

                implicit sequence learning,multitasking,prediction
                implicit sequence learning, multitasking, prediction

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