IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0075163.html
   My bibliography  Save this article

A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns

Author

Listed:
  • Benjamin G Schultz
  • Catherine J Stevens
  • Peter E Keller
  • Barbara Tillmann

Abstract

Implicit learning (IL) occurs unconsciously and without intention. Perceptual fluency is the ease of processing elicited by previous exposure to a stimulus. It has been assumed that perceptual fluency is associated with IL. However, the role of perceptual fluency following IL has not been investigated in temporal pattern learning. Two experiments by Schultz, Stevens, Keller, and Tillmann demonstrated the IL of auditory temporal patterns using a serial reaction-time task and a generation task based on the process dissociation procedure. The generation task demonstrated that learning was implicit in both experiments via motor fluency, that is, the inability to suppress learned information. With the aim to disentangle conscious and unconscious processes, we analyze unreported recognition data associated with the Schultz et al. experiments using the sequence identification measurement model. The model assumes that perceptual fluency reflects unconscious processes and IL. For Experiment 1, the model indicated that conscious and unconscious processes contributed to recognition of temporal patterns, but that unconscious processes had a greater influence on recognition than conscious processes. In the model implementation of Experiment 2, there was equal contribution of conscious and unconscious processes in the recognition of temporal patterns. As Schultz et al. demonstrated IL in both experiments using a generation task, and the conditions reported here in Experiments 1 and 2 were identical, two explanations are offered for the discrepancy in model and behavioral results based on the two tasks: 1) perceptual fluency may not be necessary to infer IL, or 2) conscious control over implicitly learned information may vary as a function of perceptual fluency and motor fluency.

Suggested Citation

  • Benjamin G Schultz & Catherine J Stevens & Peter E Keller & Barbara Tillmann, 2013. "A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-1, September.
  • Handle: RePEc:plo:pone00:0075163
    DOI: 10.1371/journal.pone.0075163
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075163
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0075163&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0075163?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiwon Lee & Midam An & Yongku Kim & Jung-In Seo, 2021. "Optimal Allocation for Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(18), pages 1-10, September.
    2. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    3. Andreas Wienke & Anne M. Herskind & Kaare Christensen & Axel Skytthe & Anatoli I. Yashin, 2002. "The influence of smoking and BMI on heritability in susceptibility to coronary heart disease," MPIDR Working Papers WP-2002-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    4. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    5. Berry, Brian J.L. & Okulicz-Kozaryn, Adam, 2008. "Are there ENSO signals in the macroeconomy," Ecological Economics, Elsevier, vol. 64(3), pages 625-633, January.
    6. Nicos Nicolaou & Scott Shane, 2019. "Common genetic effects on risk-taking preferences and choices," Journal of Risk and Uncertainty, Springer, vol. 59(3), pages 261-279, December.
    7. Stephen Richards, 2010. "Author's response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 920-924, October.
    8. Ken B Hanscombe & Maciej Trzaskowski & Claire M A Haworth & Oliver S P Davis & Philip S Dale & Robert Plomin, 2012. "Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    9. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    10. Oh, Man-Suk, 2014. "Bayesian comparison of models with inequality and equality constraints," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 176-182.
    11. Satonori Nasu & Yu Ishibashi & Junichi Ikuta & Shingo Yamane & Ryuji Kobayashi, 2022. "Reliability and Validity of the Japanese Version of the Assessment of Readiness for Mobility Transition (ARMT-J) for Japanese Elderly," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    12. Bonaiuto, M. & Mosca, O. & Milani, A. & Ariccio, S. & Dessi, F. & Fornara, F., 2024. "Beliefs about technological and contextual features drive biofuels’ social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    13. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    14. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    15. Schreier, Alayna & Stenersen, Madeline R. & Strambler, Michael J. & Marshall, Tim & Bracey, Jeana & Kaufman, Joy S., 2023. "Needs of caregivers of youth enrolled in a statewide system of care: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 147(C).
    16. Daisuke Matsumoto & Fujio Inui & Chika Honda & Rie Tomizawa & Mikio Watanabe & Karri Silventoinen & Norio Sakai, 2020. "Heritability and Environmental Correlation of Phase Angle with Anthropometric Measurements: A Twin Study," IJERPH, MDPI, vol. 17(21), pages 1-10, October.
    17. Sanjay Gupta & Kushagra Sinha, 2022. "Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    18. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    19. Naiara Escalante Mateos & Eider Goñi Palacios & Arantza Fernández-Zabala & Iratxe Antonio-Agirre, 2020. "Internal Structure, Reliability and Invariance across Gender Using the Multidimensional School Climate Scale PACE-33," IJERPH, MDPI, vol. 17(13), pages 1-24, July.
    20. Anita Radman Peša & Mejra Festić, 2012. "Testing the "EU Announcement Effect" on Stock Market Indices and Macroeconomic Variables in Croatia Between 2000 and 2010," Prague Economic Papers, Prague University of Economics and Business, vol. 2012(4), pages 450-469.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0075163. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.