Unsupervised category learning with integral-dimension stimuli
Unsupervised category learning with integral-dimension stimuli
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DOI：
10.1080/17470218.2012.658821
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发表时间：
2012-01-01
影响因子：
1.7
通讯作者：
Hutchinson, Steven
中科院分区：
文献类型：
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作者：
Ell, Shawn W.;Ashby, F. Gregory;Hutchinson, Steven
尽管最近关于无监督类别学习的研究激增，但大多数研究都集中在无约束任务上，其中没有提供有关底层类别结构的说明。相对较少的研究集中在受限任务上，这些任务的目标是在没有反馈的情况下学习预定义的刺激簇。解决这个问题的少数研究几乎完全集中在相对容易选择性地关注组成维度（即可分离维度）的刺激上。在本研究中，我们调查了参与者学习由刺激构建的类别的能力，对于这些刺激，即使不是不可能，也很难有选择地关注组成维度（即整体维度）。实验表明，个体能够学习由亮度和饱和度的积分维度构建的类别，但这种能力通常仅限于需要选择性注意亮度的类别结构。正如积分维度所预期的那样，参与者通常能够在没有反馈的情况下整合亮度和饱和度信息——这种能力在之前的可分离维度研究中没有观察到。即便如此，在分类过程中仍然存在对亮度的重视程度高于对饱和度的重视，这表明对亮度的选择性关注较弱。这些数据对无监督类别学习模型的开发提出了重要的挑战。
Despite the recent surge in research on unsupervised category learning, the majority of studies have focused on unconstrained tasks in which no instructions are provided about the underlying category structure. Relatively little research has focused on constrained tasks in which the goal is to learn predefined stimulus clusters in the absence of feedback. The few studies that have addressed this issue have focused almost exclusively on stimuli for which it is relatively easy to attend selectively to the component dimensions (i.e., separable dimensions). In the present study, we investigated the ability of participants to learn categories constructed from stimuli for which it is difficult, if not impossible, to attend selectively to the component dimensions (i.e., integral dimensions). The experiments demonstrate that individuals are capable of learning categories constructed from the integral dimensions of brightness and saturation, but this ability is generally limited to category structures requiring selective attention to brightness. As might be expected with integral dimensions, participants were often able to integrate brightness and saturation information in the absence of feedback-an ability not observed in previous studies with separable dimensions. Even so, there was a bias to weight brightness more heavily than saturation in the categorization process, suggesting a weak form of selective attention to brightness. These data present an important challenge for the development of models of unsupervised category learning.