IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v69y2018icp158-168.html
   My bibliography  Save this article

The characterization of attention resource capacity and its relationship with fluid reasoning intelligence: A multiple object tracking study

Author

Listed:
  • Tullo, Domenico
  • Faubert, Jocelyn
  • Bertone, Armando

Abstract

Multiple object-tracking (MOT) paradigms have the potential to highlight attention resource capacities. However, there is a dearth in research exploring the relationship between individual differences in MOT capability and higher-level cognition, such as intelligence. Previous research has demonstrated that manipulating task demands, or the task's cognitive load, can help describe this relationship. Therefore, we assessed the relationship between performance on a 3D-MOT task at different levels of cognitive load (average speed for tracking 1, 2, 3 and 4 target objects out of 8 total objects), and fluid reasoning intelligence measured by the Wechsler Abbreviated Scale of Intelligence-2nd edition (WASI-II). Also, we compared MOT performance between intellectual styles classified as: (i) low, medium or high fluid reasoning IQ, and (ii) fluid reasoning or verbal styles. As expected, speed scores decreased as target objects increased. This trend represents a proxy for attentional resource capacity as manipulations to both speed and target objects are able to highlight individual differences in available attentional resources. Furthermore, MOT capability at high load (4-targets) was the best predictor of fluid reasoning intelligence compared to lower loads (1–3 targets), and individuals with a fluid reasoning style and/or medium-high fluid reasoning intelligence outperformed individuals with a verbal style and low fluid reasoning IQ, respectively. These results describe the underlying commonalities between fluid reasoning intelligence and attention resource capacity, extending previous findings with working memory capacity. This study demonstrates that examining MOT as a measure of attention, rather than a phenomenon, can illustrate the potential to repurpose the use of this task to characterize attentional resource capacity.

Suggested Citation

  • Tullo, Domenico & Faubert, Jocelyn & Bertone, Armando, 2018. "The characterization of attention resource capacity and its relationship with fluid reasoning intelligence: A multiple object tracking study," Intelligence, Elsevier, vol. 69(C), pages 158-168.
  • Handle: RePEc:eee:intell:v:69:y:2018:i:c:p:158-168
    DOI: 10.1016/j.intell.2018.06.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289618300047
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.intell.2018.06.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Steven J. Luck & Edward K. Vogel, 1997. "The capacity of visual working memory for features and conjunctions," Nature, Nature, vol. 390(6657), pages 279-281, November.
    2. Lana M. Trick & Tahlia Perl & Naina Sethi, 2005. "Age-Related Differences in Multiple-Object Tracking," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 60(2), pages 102-105.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bruner, Emiliano & Colom, Roberto, 2022. "Can a Neandertal meditate? An evolutionary view of attention as a core component of general intelligence," Intelligence, Elsevier, vol. 93(C).

    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. Aki Kondo & Jun Saiki, 2012. "Feature-Specific Encoding Flexibility in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    2. Jifan Zhou & Jun Yin & Tong Chen & Xiaowei Ding & Zaifeng Gao & Mowei Shen, 2011. "Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    3. Li, Qian & Huang, Zhuowei (Joy) & Christianson, Kiel, 2016. "Visual attention toward tourism photographs with text: An eye-tracking study," Tourism Management, Elsevier, vol. 54(C), pages 243-258.
    4. Yuri A. Markov & Igor S. Utochkin, 2017. "The Effect of Object Distinctiveness on Object-Location Binding in Visual Working Memory," HSE Working papers WP BRP 79/PSY/2017, National Research University Higher School of Economics.
    5. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Ociepka, Michał & Kałamała, Patrycja & Chuderski, Adam, 2022. "High individual alpha frequency brains run fast, but it does not make them smart," Intelligence, Elsevier, vol. 92(C).
    7. repec:cup:judgdm:v:7:y:2012:i:3:p:254-267 is not listed on IDEAS
    8. Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2013. "No Evidence for an Item Limit in Change Detection," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    9. Bin Zhu & Stephanie A. Watts, 2010. "Visualization of Network Concepts: The Impact of Working Memory Capacity Differences," Information Systems Research, INFORMS, vol. 21(2), pages 327-344, June.
    10. D. Alexander Varakin & Jamie Hale, 2014. "Intentional Memory Instructions Direct Attention But Do Not Enhance Visual Memory," SAGE Open, , vol. 4(4), pages 21582440145, October.
    11. Haggar Cohen-Dallal & Isaac Fradkin & Yoni Pertzov, 2018. "Are stronger memories forgotten more slowly? No evidence that memory strength influences the rate of forgetting," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-18, July.
    12. Loic Matthey & Paul M Bays & Peter Dayan, 2015. "A Probabilistic Palimpsest Model of Visual Short-term Memory," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-34, January.
    13. Krieger, Florian & Zimmer, Hubert D. & Greiff, Samuel & Spinath, Frank M. & Becker, Nicolas, 2019. "Why are difficult figural matrices hard to solve? The role of selective encoding and working memory capacity," Intelligence, Elsevier, vol. 72(C), pages 35-48.
    14. Jochen Ranger & Jörg-Tobias Kuhn, 2013. "Analyzing Response Times in Tests With Rank Correlation Approaches," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 61-80, February.
    15. Jeanne Hagenbach & Rachel Kranton, 2023. "Competition, Cooperation, and Motivated Social Perceptions," Working Papers hal-03792554, HAL.
    16. Pais, Miguel Pessanha & Cabral, Henrique N., 2017. "Fish behaviour effects on the accuracy and precision of underwater visual census surveys. A virtual ecologist approach using an individual-based model," Ecological Modelling, Elsevier, vol. 346(C), pages 58-69.
    17. Eline R. Kupers & Insub Kim & Kalanit Grill-Spector, 2024. "Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    18. Juan Linde-Domingo & Bernhard Spitzer, 2024. "Geometry of visuospatial working memory information in miniature gaze patterns," Nature Human Behaviour, Nature, vol. 8(2), pages 336-348, February.
    19. Pahor, Anja & Jaušovec, Norbert, 2017. "Multifaceted pattern of neural efficiency in working memory capacity," Intelligence, Elsevier, vol. 65(C), pages 23-34.
    20. Hauke S Meyerhoff & Nina A Gehrer, 2017. "Visuo-perceptual capabilities predict sensitivity for coinciding auditory and visual transients in multi-element displays," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-11, September.
    21. Scharfen, Jana & Peters, Judith Marie & Holling, Heinz, 2018. "Retest effects in cognitive ability tests: A meta-analysis," Intelligence, Elsevier, vol. 67(C), pages 44-66.

    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:eee:intell:v:69:y:2018:i:c:p:158-168. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    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.