Nielsen Kristina J, Logothetis Nikos K, Rainer Gregor
Max-Planck Institute for Biological Cybernetics, Tübingen, Germany.
Curr Biol. 2006 Apr 18;16(8):814-20. doi: 10.1016/j.cub.2006.03.027.
By learning to discriminate among visual stimuli, human observers can become experts at specific visual tasks. The same is true for Rhesus monkeys, the major animal model of human visual perception. Here, we systematically compare how humans and monkeys solve a simple visual task. We trained humans and monkeys to discriminate between the members of small natural-image sets. We employed the "Bubbles" procedure to determine the stimulus features used by the observers. On average, monkeys used image features drawn from a diagnostic region covering about 7% +/- 2% of the images. Humans were able to use image features drawn from a much larger diagnostic region covering on average 51% +/- 4% of the images. Similarly for the two species, however, about 2% of the image needed to be visible within the diagnostic region on any individual trial for correct performance. We characterize the low-level image properties of the diagnostic regions and discuss individual differences among the monkeys. Our results reveal that monkeys base their behavior on confined image patches and essentially ignore a large fraction of the visual input, whereas humans are able to gather visual information with greater flexibility from large image regions.
通过学习区分视觉刺激，人类观察者可以成为特定视觉任务的专家。对于恒河猴（人类视觉感知的主要动物模型）来说也是如此。在这里，我们系统地比较了人类和猴子如何解决一个简单的视觉任务。我们训练人类和猴子区分小的自然图像集的成员。我们采用“气泡”程序来确定观察者使用的刺激特征。平均而言，猴子使用的图像特征来自覆盖约7%±2%图像的诊断区域。人类能够使用来自大得多的诊断区域的图像特征，平均覆盖51%±4%的图像。然而，对于这两个物种来说，在任何一次单独试验中，诊断区域内大约2%的图像需要可见才能正确执行任务。我们描述了诊断区域的低级图像属性，并讨论了猴子之间的个体差异。我们的结果表明，猴子的行为基于有限的图像块，基本上忽略了大部分视觉输入，而人类能够更灵活地从大图像区域收集视觉信息。
