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Do Salient Features Overshadow Learning of Other Features in Category Learning?

机译:显着特征是否使类别学习中其他特征的学习蒙上阴影?

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摘要

Hundreds of associative learning experiments have examined how animals learn to predict an aversive outcome, such as a shock, loud sound, or puff of air in the eye. In this study, we reversed this pattern and examined the role of an aversive stimulus, shock, as a feature of a complex stimulus composed of several features, rather than as an outcome. In particular, we used a category learning paradigm in which multiple features predicted category membership and asked whether a salient, aversive feature would reduce learning of other category features through cue competition. Three experiments compared a condition in which one category had among its six features a painful “sting” (shock) and the other category a distinctive sound (the critical features) to a control condition in which the sting and sound were represented by much less salient (and not aversive) visual depictions. Subjects learned the categories and then were tested on their knowledge of all six features as predictors of the category label. Surprisingly, the experiments consistently found that the salient, aversive critical features did not reduce learning of other features relative to the control. Bayesian statistics gave positive evidence for this null result. Equally surprisingly, in a fourth experiment, a nonaversive salient feature (brightly colored patterns) increased learning of other features compared to the control. We explain the results in terms of attentional strategies that may apply in a category learning context.
机译:数以百计的联想学习实验检查了动物如何学会预测令人反感的结局,例如震惊,响亮的声音或眼中的空气。在这项研究中,我们扭转了这种模式,并研究了厌恶性刺激(休克)作为由多个特征组成的复杂刺激的特征而不是结果的作用。特别是,我们使用了类别学习范式,其中多个功能预测类别成员资格,并询问显着的厌恶功能是否会通过提示竞争减少其他类别功能的学习。三个实验比较了一个条件,其中一个类别的六个特征中有一个痛苦的“刺痛”(电击),另一个类别具有独特的声音(关键特征)与一个控制条件,在该控制条件中,刺痛和声音的显着性要低得多(而非厌恶)视觉描绘。受试者学习了类别,然后对其作为类别标签预测指标的所有六个特征的知识进行了测试。令人惊讶的是,实验一致地发现,显着的,厌恶的关键特征并未减少相对于对照的其他特征的学习。贝叶斯统计为这一无效结果提供了积极的证据。同样令人惊讶的是,在第四个实验中,与对照相比,非平均显着特征(明亮的彩色图案)增加了其他特征的学习。我们根据可能适用于类别学习环境的注意力策略来解释结果。

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