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首页> 外文期刊>Journal of vision >Classification images reveal changes in the encoding of newly learned face dimensions
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Classification images reveal changes in the encoding of newly learned face dimensions

机译:分类图像揭示了新学习的面部尺寸的编码方式的变化

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A body of research suggests that learning to categorize objects along a new dimension changes the perceptual representation of such dimension, increasing its discriminability and its separability from other dimensions. However, little is known about exactly how the internal representations of individual objects change during such dimension learning. Here, we trained twenty participants to categorize faces that varied along two morphing dimensions. One of the morphing dimensions was relevant to the categorization task and the other was irrelevant. We used classification images to estimate the internal templates used by participants to identify four faces varying along the category-relevant and category-irrelevant dimensions, both before categorization training and after categorization training. The obtained classification images provide estimates of the exact stimulus information used by the participants to identify the faces at each stage. Thus, examination and comparison of the obtained classification images allowed us to determine exactly how the internal representation of these faces changed as a result of categorization training. We defined two ways in which the representation of the category-relevant dimension could have changed as a result of categorization training. First, the internal templates of two faces having opposite values in the category-relevant dimension could become negatively correlated, a result that has been found with some familiar face dimensions. Our results suggest that categorization training had an effect in this direction, but the effect was not significant. Second, the internal templates of two faces having the same value in the category-relevant dimension, but different values in the category-irrelevant dimension could become more similar, which would explain previously-observed increases in dimensional separability after categorization training. Our results show a robust effect of categorization training in this direction.
机译:大量研究表明,学习沿新维度对对象进行分类会改变该维度的感知表示,从而增加其可辨性和与其他维度的可分离性。但是,对于这种尺寸学习过程中各个对象的内部表示如何精确变化知之甚少。在这里,我们培训了二十名参与者,以对沿两个变形维度变化的面孔进行分类。变形维度之一与分类任务相关,而另一个则不相关。我们使用分类图像来估计参与者使用的内部模板,以识别在分类训练之前和分类训练之后,沿着与类别相关和与类别无关的维度变化的四个面孔。所获得的分类图像提供了参与者在每个阶段用来识别面部的确切刺激信息的估计。因此,对获得的分类图像的检查和比较使我们能够准确地确定这些面部的内部表示作为分类训练结果的变化。我们定义了两种方法,通过分类训练可以改变类别相关维度的表示形式。首先,在类别相关维度中具有相反值的两个面孔的内部模板可能变得负相关,这是在一些熟悉的面孔维度中发现的。我们的结果表明,分类训练在此方向上有效果,但效果并不显着。其次,在类别相关维度中具有相同值但在类别无关维度中具有不同值的两个面部的内部模板可能变得更加相似,这可以解释先前观察到的分类训练后维度可分离性的增加。我们的结果显示了在这个方向上分类训练的强大效果。

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