How the statistics of sequential presentation influence the learning of structure.

作者: Devika Narain , Pascal Mamassian , Robert J van Beers , Jeroen BJ Smeets , Eli Brenner

DOI: 10.1371/JOURNAL.PONE.0062276

关键词: UncorrelatedDeep learningIndependent SamplingStimulus (physiology)Random walkBayes' theoremStatisticsArtificial intelligenceEmpirical assessmentBiologyLinear regression

摘要: 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 its parameters. This process represents structured problem. We are interested in empirical assessment some factors enable to such over time. More specifically, we look at how statistics presentation samples from given structure influence learning. Participants engage experimental task where they required predict timing target. At outset, oblivious existence relationship between position stimulus temporal response intercept it. Different groups participants either presented with Random Walk consecutive stimuli were correlated uncorrelated find structural implicit is only learned conditions independently drawn. leads us believe require rich independent sampling hidden structures

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