Learning where to look for a hidden target
Learning where to look for a hidden target
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DOI：
10.1073/pnas.1301216110
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发表时间：
2013-06-18
影响因子：
11.1
通讯作者：
Sejnowski, Terrence J.
中科院分区：
文献类型：
--
作者：
Chukoskie, Leanne;Snider, Joseph;Sejnowski, Terrence J.
生存取决于成功地觅食，进化选择了不同物种的不同行为。人类搜寻不仅是为了食物，也是为了信息。我们每天决定看哪里超过17万次，大约每清醒的一秒钟三次。这些扫视眼球运动的频率掩盖了每个人选择背后的复杂性。经验因素会影响到看哪里的选择，并且可以被调用以在上下文和任务适当的方式快速重定向凝视。然而，人们对个体如何在当前的背景和任务下学会引导他们的目光知之甚少。我们设计了一个任务，参与者搜索一个新的场景中的目标，其位置是随机绘制的每个试验从一个固定的先验分布。目标在空白屏幕上是不可见的，当参与者注视隐藏的目标位置时，他们会得到奖励。在几次试验中，参与者通过查看先前奖励的位置附近并避开先前未奖励的位置，迅速找到隐藏的目标。学习轨迹的特征在于一个简单的重复学习（RL）模型，该模型维护并不断更新位置的奖励地图。RL模型进一步预测了对最近经验的敏感性，这些预测得到了数据的证实。参与者和RL模型的渐近性能接近理想观测器理论所描述的最优性能。这两个互补的解释水平表明，在一个新的环境中的经验如何驱动人类的视觉搜索，并可能扩展到其他形式的搜索，如动物觅食。
Survival depends on successfully foraging for food, for which evolution has selected diverse behaviors in different species. Humans forage not only for food, but also for information. We decide where to look over 170,000 times per day, approximately three times per wakeful second. The frequency of these saccadic eye movements belies the complexity underlying each individual choice. Experience factors into the choice of where to look and can be invoked to rapidly redirect gaze in a context and task-appropriate manner. However, remarkably little is known about how individuals learn to direct their gaze given the current context and task. We designed a task in which participants search a novel scene for a target whose location was drawn stochastically on each trial from a fixed prior distribution. The target was invisible on a blank screen, and the participants were rewarded when they fixated the hidden target location. In just a few trials, participants rapidly found the hidden targets by looking near previously rewarded locations and avoiding previously unrewarded locations. Learning trajectories were well characterized by a simple reinforcement-learning (RL) model that maintained and continually updated a reward map of locations. The RL model made further predictions concerning sensitivity to recent experience that were confirmed by the data. The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans and may extend to other forms of search such as animal foraging.