The Synergy of Passive and Active Learning Modes in Adaptive Perceptual Learning.

Everett Mettler, Austin S Phillips, Christine M Massey, Timothy Burke, Patrick Garrigan, Philip J Kellman
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Abstract

Adaptive learning systems that generate spacing intervals based on learner performance enhance learning efficiency and retention (Mettler, Massey & Kellman, 2016). Recent research in factual learning suggests that initial blocks of passive trials, where learners observe correct answers without overtly responding, produce greater learning than passive or active trials alone (Mettler, Massey, Burke, Garrigan & Kellman, 2018). Here we tested whether this passive + active advantage generalizes beyond factual learning to perceptual learning. Participants studied and classified images of butterfly genera using either: 1) Passive Only presentations, 2) Passive Initial Blocks followed by active, adaptive scheduling, 3) Passive Initial Category Exemplar followed by active, adaptive scheduling, or 4) Active Only learning. We found an advantage for combinations of active and passive presentations over Passive Only or Active Only presentations. Passive trials presented in initial blocks showed the best performance, paralleling earlier findings in factual learning. Combining active and passive learning produces greater learning gains than either alone, and these effects occur for diverse forms of learning, including perceptual learning.

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被动和主动学习模式在适应性知觉学习中的协同作用。
基于学习者表现产生间隔的自适应学习系统提高了学习效率和记忆力(Mettler, Massey & Kellman, 2016)。最近对事实学习的研究表明,在被动试验的初始阶段,学习者在没有明显反应的情况下观察正确答案,比单独的被动或主动试验产生更大的学习效果(Mettler, Massey, Burke, Garrigan & Kellman, 2018)。在这里,我们测试了这种被动+主动优势是否从事实学习推广到感知学习。参与者使用以下方法对蝴蝶属图像进行研究和分类:1)被动呈现;2)被动初始块然后主动自适应调度;3)被动初始类别范例然后主动自适应调度;或4)主动仅学习。我们发现主动和被动结合的演示比被动或主动的演示更有优势。在最初的模块中进行的被动试验显示出最好的表现,与之前在事实学习方面的发现相似。主动学习和被动学习相结合比单独学习产生更大的学习收益,这些影响发生在各种形式的学习中,包括感知学习。
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