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    Investigating the effects of ongoing-task bias on prospective memory

    78533.pdf (1.535Mb)
    Access Status
    Open access
    Authors
    Strickland, Luke
    Loft, Shayne
    Heathcote, Andrew
    Date
    2020
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Strickland, L. and Loft, S. and Heathcote, A. 2020. Investigating the effects of ongoing-task bias on prospective memory. Quarterly Journal of Experimental Psychology.
    Source Title
    Quarterly Journal of Experimental Psychology
    DOI
    10.1177/1747021820914915
    ISSN
    1747-0218
    Faculty
    Faculty of Business and Law
    School
    Future of Work Institute
    Remarks

    © Experimental Psychology Society 2020

    URI
    http://hdl.handle.net/20.500.11937/78449
    Collection
    • Curtin Research Publications
    Abstract

    Event-based prospective memory (PM) refers to the cognitive processes required to perform a planned action upon encountering a future event. Event-based PM studies engage participants in an ongoing task (e.g., lexical decision-making) with an instruction to make an alternative PM response to certain items (e.g., items containing “tor”). The Prospective Memory Decision Control (PMDC) model, which provides a quantitative process account of ongoing-task and PM decisions, proposes that PM and ongoing-task processes compete in a race to threshold. We use PMDC to test whether, as proposed by the Delay Theory of PM costs, PM can be improved by biasing decision-making against a specific ongoing-task choice, so that the PM process is more likely to win the race. We manipulated bias in a lexical decision task with an accompanying PM intention. In one condition, a bias was induced against deciding items were words, and in another, a bias was induced against deciding items were non-words. The bias manipulation had little effect on PM accuracy but did affect the types of ongoing-task responses made on missed PM trials. PMDC fit the observed data well and verified that the bias manipulation had the intended effect on ongoing-task processes. Furthermore, although simulations from PMDC could produce an improvement in PM accuracy due to ongoing-task bias, this required implausible parameter values. These results illustrate the importance of understanding event-based PM in terms of a comprehensive model of the processes that interact to determine all aspects of task performance.

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