General object recognition is specific: Evidence from novel and familiar objects
General object recognition is specific: Evidence from novel and familiar objects
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DOI：
10.1016/j.cognition.2017.05.019
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发表时间：
2017-09-01
期刊：
影响因子：
3.4
通讯作者：
Gauthier, Isabel
中科院分区：
文献类型：
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作者：
Richler, Jennifer J.;Wilmer, Jeremy B.;Gauthier, Isabel
在物体识别的测试中，个体差异通常与熟悉的类别（例如汽车、面孔、鞋子、鸟、蘑菇）适度但不平凡地相关。从理论上讲，这些相关性可以反映全球的非特定机制，如一般智力（IQ）或更具体的机制。在这里，我们介绍了两种不同的方法来有效地捕捉类别一般的性能变化，一个使用新的对象，一个使用熟悉的对象。在每种情况下，我们表明，类别一般的性能方差是无关的智商，从而涉及更具体的机制。第一种方法检查三个新开发的新颖的对象记忆测试（NOMTs）。我们预测，NOMT会表现出更多的共享，类别一般的方差比熟悉的对象记忆测试（FOMT），因为新的对象，不像熟悉的对象，缺乏类别特定的环境影响（例如，暴露于汽车杂志或植物学类）。这一预测是正确的，值得注意的是，NOMT之间的显著差异几乎都不能用智商来解释。此外，虽然NOMT与两个FOMT（人脸、汽车）之间存在显著相关性，但这些相关性小于NOMT之间的相关性，也不大于人脸和汽车测试本身之间的相关性，这表明NOMT捕捉到的类别一般方差不仅与智商相关，而且在某种程度上与人脸和汽车识别相关。第二种方法在多个FOMT中平均性能，我们预测这将通过平均类别特定因素来增加类别一般方差。这一预测成立，与NOMT一样，FOMT之间的共享方差几乎没有被IQ解释。总体而言，这些结果支持物体识别机制的存在，尽管是一般的，但相对于IQ是特定的，并且基本上与人脸和汽车识别分离。他们还将灵敏的，规范良好的NOMT添加到可用于研究对象识别的工具中。(C)2017爱思唯尔B.V.保留所有权利。
In tests of object recognition, individual differences typically correlate modestly but nontrivially across familiar categories (e.g. cars, faces, shoes, birds, mushrooms). In theory, these correlations could reflect either global, non-specific mechanisms, such as general intelligence (IQ), or more specific mechanisms. Here, we introduce two separate methods for effectively capturing category-general performance variation, one that uses novel objects and one that uses familiar objects. In each case, we show that category-general performance variance is unrelated to IQ thereby implicating more specific mechanisms. The first approach examines three newly developed novel object memory tests (NOMTs). We predicted that NOMTs would exhibit more shared, category-general variance than familiar object memory tests (FOMTs) because novel objects, unlike familiar objects, lack category-specific environmental influences (e.g. exposure to car magazines or botany classes). This prediction held, and remarkably, virtually none of the substantial shared variance among NOMTs was explained by IQ. Also, while NOMTs correlated nontrivially with two FOMTs (faces, cars), these correlations were smaller than among NOMTs and no larger than between the face and car tests themselves, suggesting that the category-general variance captured by NOMTs is specific not only relative to IQ but also, to some degree, relative to both face and car recognition. The second approach averaged performance across multiple FOMTs, which we predicted would increase category-general variance by averaging out category-specific factors. This prediction held, and as with NOMTs, virtually none of the shared variance among FOMTs was explained by IQ Overall, these results support the existence of object recognition mechanisms that, though category-general, are specific relative to IQ and substantially separable from face and car recognition. They also add sensitive, well-normed NOMTs to the tools available to study object recognition. (C) 2017 Elsevier B.V. All rights reserved.