Rapid long lasting learning in a collinear edge-detection task.
Rapid long lasting learning in a collinear edge-detection task.
复制标题
共线边缘检测任务中的快速持久学习。
作者：
Bush,EliotC;Shimojo,Shinsuke;Allman,JohnM
我们已经开发了一个检测任务，其中受试者确定一对共线边缘的多边形领域。我们的六个主题中有五个在这个任务中表现出显著的快速学习。四个在一天和一周后显示出保留的证据。在几个迁移测试中，我们发现干扰物的中断会导致表现的显著下降。这些结果是一致的模型，其中共线的目标最初产生的显着信号太弱，不能可靠地检测到干扰的噪声。随着实验的进行，视觉系统学会抑制干扰信号，从而实现更可靠的检测。
We have developed a detection task in which subjects identify a pair of collinear edges in a field of polygons. Five of our six subjects showed significant, rapid learning at this task. Four showed evidence of retention a day and a week later. In several transfer tests, we found that disruption of the distractors produced a significant drop-off in performance. These results are consistent with a model in which collinear targets initially produce a salience signal too weak to be reliably detected over the noise of the distractors. As the experiment proceeds, the visual system learns to dampen the distractor signals, allowing for more reliable detection.