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首页> 外文期刊>Journal of vision >Whata??s Feedback Got To Do With It? Examining Learning Rate and Generalization in Cross-scene Statistical Learning With and Without Feedback
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Whata??s Feedback Got To Do With It? Examining Learning Rate and Generalization in Cross-scene Statistical Learning With and Without Feedback

机译:Whata的反馈与此有关吗?在有和没有反馈的情况下,检查跨场景统计学习中的学习率和泛化

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Most models of learning predict that the presence of feedback facilitates the rate of learning, but can inhibit generalization by emphasizing attested exemplars rather than underlying rules. In a task where participants can learn statistical information without feedback (statistical learning), we examined what additional role feedback plays in learning and generalization. Following Shohamy and Wagner (2008), participants were taught arbitrary face-scene associations. On every trial, participants saw simultaneous presentation of a face and two scenes. After selecting one of the two scenes in a fixed response interval, participants were either given feedback (N=16, picture of a thumbs-up or -down) or not (N=19). Unbeknownst to participants, every two faces (Face1, Face2) were reciprocally paired with two scenes (Scene1, Scene2) creating a family of 4 overlapping associations (e.g. Face1a??Scene1, Face1a??Scene2, Face2a??Scene1, Face2a??Scene2). During training (4 blocks), participants were exposed to 3 of the 4 possible associations for each of the 16 families (trained associations). At test, feedback was always withheld to test for trained associations and untrained associations (generalization associations). There were robust and nearly identical learning rates for trained associations across feedback conditions. Both groups readily generalized but with marginally more generalization in the feedback condition (p 0.1). We find no significant difference in the frequency of non-learners across groups (defined as participants who fail to exhibit significant learning in any training block, feedback: N=2, no-feedback: N=6). Overall, these results show that learning of scene-pair associations and generalization to unseen, overlapping associations is minimally affected by the presence of feedback. In contrast to previous models, these results suggest that in the presence of salient statistical information, feedback may not modulate the time-course and outcomes of learning. Future work will examine how individual differences in the exposure phase affect learning rates and the factors that predict non-learners.
机译:大多数学习模型都预测反馈的存在会促进学习速度,但会通过强调经过证明的范例而不是基本规则来抑制泛化。在一项任务中,参与者可以在没有反馈的情况下学习统计信息(统计学习),我们研究了反馈在学习和归纳中所起的其他作用。按照Shohamy和Wagner(2008)的要求,向学员们讲授任意的面部场景关联。在每次试验中,参与者都看到了一张脸和两个场景的同时呈现。在以固定的响应间隔选择两个场景之一之后,向参与者提供反馈(N = 16,竖起大拇指或朝下的图片)或没有反馈(N = 19)。参与者不知道,每两个脸(Face1,Face2)与两个场景(Scene1,Scene2)相互配对,从而创建了一个由4个重叠关联组成的族(例如,Face1a ?? Scene1,Face1a ?? Scene2,Face2a ?? Scene1,Face2a ??)场景2)。在培训期间(4个街区),参加者与16个家庭中的每个有4个可能的协会(受训协会)接触了3个。在测试中,总是保留反馈以测试训练有素的协会和未训练的协会(一般化协会)。在各种反馈条件下,经过训练的关联具有强大且几乎相同的学习率。两组很容易泛化,但反馈条件泛化程度略高(p <0.1)。我们发现非学习者在各组之间的频率没有显着差异(定义为在任何训练块中均未表现出显着学习的参与者,反馈:N = 2,无反馈:N = 6)。总体而言,这些结果表明,场景对关联的学习和对看不见的重叠关联的泛化受反馈的影响最小。与以前的模型相比,这些结果表明,在存在重要的统计信息的情况下,反馈可能不会调节学习的时间过程和结果。未来的工作将研究暴露阶段的个体差异如何影响学习率以及预测非学习者的因素。

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