Object Learning Improves Feature Extraction but Does Not Improve Feature Selection
Object Learning Improves Feature Extraction but Does Not Improve Feature Selection
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DOI：
10.1371/journal.pone.0051325
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发表时间：
2012-12-12
期刊：
影响因子：
3.7
通讯作者：
Schrater, Paul
中科院分区：
文献类型：
--
作者：
Holm, Linus;Engel, Stephen;Schrater, Paul
只要看一眼你拥挤的办公桌，就足以找到你最喜欢的杯子。但是找到一个不熟悉的物体需要更多的努力。这种学习对象识别性能的优势至少有两个可能的来源。对于熟悉的物体，观察者可能：1）选择更多信息的图像位置来固定他们的眼睛，或者2）从给定的眼睛固定中提取更多信息。为了测试这些可能性，我们让观察者定位嵌入在密集显示的随机轮廓碎片中的碎片物体。8名参与者在600张图片中搜索物体，同时记录他们的眼球运动，每天三次。随着受试者接受物体训练，表现有所改善：在3次训练中，寻找物体所需的注视次数减少了64%。一个理想的观察者模型，包括碎片混淆的措施被用来计算从一个单一的固定可用的信息。将人类表现与模型进行比较表明，在每次眼睛注视时的信息提取显著增加，其量大致等于功能视野增加100%后提取的额外信息。另一方面，固定位置的选择并没有随着实践而改善。
A single glance at your crowded desk is enough to locate your favorite cup. But finding an unfamiliar object requires more effort. This superiority in recognition performance for learned objects has at least two possible sources. For familiar objects observers might: 1) select more informative image locations upon which to fixate their eyes, or 2) extract more information from a given eye fixation. To test these possibilities, we had observers localize fragmented objects embedded in dense displays of random contour fragments. Eight participants searched for objects in 600 images while their eye movements were recorded in three daily sessions. Performance improved as subjects trained with the objects: The number of fixations required to find an object decreased by 64% across the 3 sessions. An ideal observer model that included measures of fragment confusability was used to calculate the information available from a single fixation. Comparing human performance to the model suggested that across sessions information extraction at each eye fixation increased markedly, by an amount roughly equal to the extra information that would be extracted following a 100% increase in functional field of view. Selection of fixation locations, on the other hand, did not improve with practice.