作者: Devika Narain , Pascal Mamassian , Robert J van Beers , Jeroen BJ Smeets , Eli Brenner
DOI: 10.1371/JOURNAL.PONE.0062276
关键词: Uncorrelated 、 Deep learning 、 Independent Sampling 、 Stimulus (physiology) 、 Random walk 、 Bayes' theorem 、 Statistics 、 Artificial intelligence 、 Empirical assessment 、 Biology 、 Linear 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