Distributional learning on Mechanical Turk and effects of attentional shifts

@inproceedings{Moeng2017DistributionalLO,
  title={Distributional learning on Mechanical Turk and effects of attentional shifts},
  author={Emily Moeng},
  year={2017}
}
This study seeks to determine whether distributional learning can be replicated on an online platform like Mechanical Turk. In doing so, factors that may affect distributional learning, such as level of attention, participant age, and stimuli, are explored. It is found that even distributional learning, which requires making fine phonetic distinctions, can be replicated on Mechanical Turk, and that attention may nullify the effect of distributional learning. 

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