University Library
  • Login
A gateway to Melbourne's research publications
Minerva Access is the University's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience.
View Item 
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne School of Psychological Sciences
  • Melbourne School of Psychological Sciences - Research Publications
  • View Item
  • Minerva Access
  • Medicine, Dentistry & Health Sciences
  • Melbourne School of Psychological Sciences
  • Melbourne School of Psychological Sciences - Research Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

    Inferring an Observer's Prediction Strategy in Sequence Learning Experiments

    Thumbnail
    Download
    Published version (644.3Kb)

    Citations
    Altmetric
    Author
    Uppal, A; Ferdinand, V; Marzen, S
    Date
    2020-08-01
    Source Title
    Entropy: international and interdisciplinary journal of entropy and information studies
    Publisher
    MDPI
    University of Melbourne Author/s
    Ferdinand, Vanessa
    Affiliation
    Melbourne School of Psychological Sciences
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Uppal, A., Ferdinand, V. & Marzen, S. (2020). Inferring an Observer's Prediction Strategy in Sequence Learning Experiments. ENTROPY, 22 (8), https://doi.org/10.3390/e22080896.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/252571
    DOI
    10.3390/e22080896
    Abstract
    Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer's prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the model's parameter values unless we have access to several "clones" of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer's prediction strategy in an experimental or observational setting.

    Export Reference in RIS Format     

    Endnote

    • Click on "Export Reference in RIS Format" and choose "open with... Endnote".

    Refworks

    • Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References


    Collections
    • Minerva Elements Records [53102]
    • Melbourne School of Psychological Sciences - Research Publications [1220]
    Minerva AccessDepositing Your Work (for University of Melbourne Staff and Students)NewsFAQs

    BrowseCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    My AccountLoginRegister
    StatisticsMost Popular ItemsStatistics by CountryMost Popular Authors