What shape are the neural response functions underlying opponent coding in face space? A psychophysical investigation
What shape are the neural response functions underlying opponent coding in face space? A psychophysical investigation
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DOI：
10.1016/j.visres.2009.11.016
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发表时间：
2010-02-08
期刊：
影响因子：
1.8
通讯作者：
Edwards, Mark
中科院分区：
文献类型：
--
作者：
Susilo, Tirta;McKone, Elinor;Edwards, Mark
最近的证据表明，面部空间使用双池对手编码表示面部身份信息。这里我们要问的是，这种编码背后的单调神经响应函数的形状是线性的（即，面部空间编码所有大小相等的物理变化，具有相同的灵敏度）还是非线性的（例如，面部空间在平均面部周围显示出更大的编码灵敏度）。使用适应后效和两两辨别任务，我们的面部属性眼高和嘴高的结果显示出线性形状：包括远远超出正常范围的怪异面孔。我们讨论了线性编码如何解释先前文献中的一些结果，包括未能发现适应性增强了人脸识别，并提出了人脸空间可以保持远超出正常范围的值的详细编码的可能原因。我们还讨论了解释其他发现所需的特定非线性编码模型，并得出结论，人脸空间似乎使用了线性和非线性表示的混合。2009爱思唯尔有限公司版权所有。
Recent evidence has shown that face space represents facial identity information using two-pool opponent coding. Here we ask whether the shape of the monotonic neural response functions underlying such coding is linear (i.e. face space codes all equal-sized physical changes with equal sensitivity) or nonlinear (e.g. face space shows greater coding sensitivity around the average face). Using adaptation aftereffects and pairwise discrimination tasks, our results for face attributes of eye height and mouth height demonstrate linear shape: including for bizarre faces far outside the normal range. We discuss how linear coding explains some results in the previous literature, including failures to find that adaptation enhances face discrimination, and suggest possible reasons why face space can maintain detailed coding of values far outside the normal range. We also discuss specific nonlinear coding models needed to explain other findings, and conclude face space appears to use a mixture of linear and nonlinear representations. (C) 2009 Elsevier Ltd. All rights reserved.