View Item 
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UU Student Theses RepositoryBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

        Exploring Sampling Algorithms to explain Cognitive Characteristics in Random Number Sequences and a Time-estimation Task

        Thumbnail
        View/Open
        6371833_Eshelby_MSc.pdf (1.333Mb)
        Publication date
        2020
        Author
        Eshelby, V.E.
        Metadata
        Show full item record
        Summary
        Recently, sampling algorithms have demonstrated their ability in simulating ubiquitous cognitive characteristics, such as autocorrelations and levy-like distributions, found in foraging behaviours. However, this effect has been observed independently in individual tasks. Exploring the co-occurrence of such phenomena has yet to be investigated. This paper explores three main questions: Whether two foraging characteristics co-occur, whether cognitive load impacts said foraging characteristics and to what extent can sampling algorithms explain these cognitive characteristics. Seven participants were given two sequential tasks (Random number generation (RNGT) and a metronome tapping task) separately as well as together. The findings suggest that foraging does not co-occur; that autocorrelations are present in the tapping task but not RNGT and heavy tailed distributions are present in RNGT but not in the tapping task. Cognitive load only plays a role in the tapping task. Further analysis explored task-specific cognitive characteristics outlining potential strategies and patterns participants used (including randomness, run length, pattern type and jump length) and the findings were compared to four sampling algorithms: A Direct Sampler (DS), Markov Chain Monte Carlo (MCMC), Metropolis-Coupled Markov Chain Monte Carlo (MC3) and a No-U turn Hamiltonian Monte Carlo (HMC). The DS, MC3 and HMC were able to produce similar results to human behaviour but identifies that a hybrid approach of these three samplers might simulate the metrics produced by humans better.
        URI
        https://studenttheses.uu.nl/handle/20.500.12932/37083
        Collections
        • Theses
        Utrecht university logo