IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0195019.html
   My bibliography  Save this article

Investigating the development of causal inference by studying variability in 2- to 5-year-olds' behavior

Author

Listed:
  • Tessa J P van Schijndel
  • Kim Huijpen
  • Ingmar Visser
  • Maartje E J Raijmakers

Abstract

This study investigated the development of young children’s causal inference by studying variability in behavior. Two possible sources of variability, strategy use and accuracy in strategy execution, were discriminated and related to age. To this end, a relatively wide range of causal inference trials was administered to children of a relatively broad age range: 2- to 5-year-olds. Subsequently, individuals’ response patterns over trials were analyzed with a latent variable technique. The results showed that variability in children’s behavior could largely be explained by strategy use. Three different strategies were distinguished, and tentative interpretations suggest these could possibly be labeled as “rational”, “associative”, and “uncertainty avoidance” strategies. The strategies were found to be related to age, and this age-related strategy use better explained the variability in children’s behavior than age-related increase in accuracy of executing a single strategy. This study can be considered a first step in introducing a new, fruitful approach for investigating the development of causal inference.

Suggested Citation

  • Tessa J P van Schijndel & Kim Huijpen & Ingmar Visser & Maartje E J Raijmakers, 2018. "Investigating the development of causal inference by studying variability in 2- to 5-year-olds' behavior," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-15, April.
    • Handle: RePEc:plo:pone00:0195019
    DOI: 10.1371/journal.pone.0195019
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195019
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0195019&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0195019?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. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
    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. Dionne, Georges & Saissi-Hassani, Samir, 2016. "Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Working Papers 15-3, HEC Montreal, Canada Research Chair in Risk Management.
    2. Denis S Willett & Justin George & Nora S Willett & Lukasz L Stelinski & Stephen L Lapointe, 2016. "Machine Learning for Characterization of Insect Vector Feeding," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-14, November.
    3. Davey, Calum & Dirawo, Jeffrey & Mushati, Phillis & Magutshwa, Sitholubuhle & Hargreaves, James R. & Cowan, Frances M., 2019. "Mobility and sex work: why, where, when? A typology of female-sex-worker mobility in Zimbabwe," Social Science & Medicine, Elsevier, vol. 220(C), pages 322-330.
    4. Georges Dionne & Amir Saissi Hassani, 2015. "Endogenous Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Cahiers de recherche 1516, CIRPEE.
    5. Yi-Hsuan Lee & Alina Davier, 2013. "Monitoring Scale Scores over Time via Quality Control Charts, Model-Based Approaches, and Time Series Techniques," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 557-575, July.
    6. Rob Hayward & Jens Hölscher, 2017. "The Forward-Discount Puzzle in Central and Eastern Europe," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 59(4), pages 472-497, December.
    7. Andrew C. Meldrum & Oleg Sokolinskiy, 2023. "The Effects of Volatility on Liquidity in the Treasury Market," Finance and Economics Discussion Series 2023-028, Board of Governors of the Federal Reserve System (U.S.).
    8. Morteza Amini & Afarin Bayat & Reza Salehian, 2023. "hhsmm: an R package for hidden hybrid Markov/semi-Markov models," Computational Statistics, Springer, vol. 38(3), pages 1283-1335, September.
    9. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    10. Ahmet Akca & Ethem Çanakoğlu, 2021. "Adaptive stochastic risk estimation of firm operating profit," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 48(3), pages 463-504, September.
    11. Francesco Bartolucci & Fulvia Pennoni, 2024. "Book Review of Mixture and Hidden Markov Models with R by Visser & Speekenbrink," Psychometrika, Springer;The Psychometric Society, vol. 89(2), pages 741-743, June.
    12. Gerald C. Kane & Sam Ransbotham, 2016. "Research Note—Content and Collaboration: An Affiliation Network Approach to Information Quality in Online Peer Production Communities," Information Systems Research, INFORMS, vol. 27(2), pages 424-439, June.
    13. Melnykov, Volodymyr, 2016. "ClickClust: An R Package for Model-Based Clustering of Categorical Sequences," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i09).
    14. Ramya Raviram & Pedro P Rocha & Christian L Müller & Emily R Miraldi & Sana Badri & Yi Fu & Emily Swanzey & Charlotte Proudhon & Valentina Snetkova & Richard Bonneau & Jane A Skok, 2016. "4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
    15. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    16. Marco Sandri & Paola Zuccolotto & Marica Manisera, 2020. "Markov switching modelling of shooting performance variability and teammate interactions in basketball," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1337-1356, November.
    17. Jan Bulla & Roland Langrock & Antonello Maruotti, 2019. "Guest editor’s introduction to the special issue on “Hidden Markov Models: Theory and Applications”," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 63-66, August.
    18. Lolea Iulian Cornel & Stamule Simona, 2021. "Trading using Hidden Markov Models during COVID-19 turbulences," Management & Marketing, Sciendo, vol. 16(4), pages 334-351, December.
    19. Ingmar Visser & Maarten Speekenbrink, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 478-483, September.
    20. Reetam Majumder & Qing Ji & Nagaraj K. Neerchal, 2023. "Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 177-198, May.

    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:pone00:0195019. 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.