How the Statistics of Sequential Presentation Influence the Learning of Structure

D. Narain, P. Mamassian, R.J. van Beers, J.B.J. Smeets, E. Brenner

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    Abstract

    Recent work has shown that humans can learn or detect complex dependencies among variables. Even learning a simple dependency involves the identification of an underlying model and the learning of its parameters. This process represents learning a structured problem. We are interested in an empirical assessment of some of the factors that enable humans to learn such a dependency over time. More specifically, we look at how the statistics of the presentation of samples from a given structure influence learning. Participants engage in an experimental task where they are required to predict the timing of a target. At the outset, they are oblivious to the existence of a relationship between the position of a stimulus and the required temporal response to intercept it. Different groups of participants are either presented with a Random Walk where consecutive stimuli were correlated or with stimuli that were uncorrelated over time. We find that the structural relationship implicit in the task is only learned in the conditions where the stimuli are independently drawn. This leads us to believe that humans require rich and independent sampling to learn hidden structures among variables. © 2013 Narain et al.
    Original languageEnglish
    Article numbere62276
    Number of pages7
    JournalPLoS ONE
    Volume8
    Issue number4
    DOIs
    Publication statusPublished - 2013

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