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Visual search for rare targets: Distracter tuning as a mechanism for learning from repeated target-absent searches

机译:视觉搜索稀有目标:将干扰因素调整为从重复的无目标搜索中学习的机制

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In the typical visual search experiment, participants search for targets that are present on half of the trials and absent on the other half. However, many real-world tasks involve targets that are present only occasionally. Given this, it is important to know how people deal with the problem of finding targets they have little experience with. One possibility is that they develop an awareness of the degree to which they have effectively completed a search through complex target-absent scenes. To test this hypothesis, we had participants complete two relatively long search tasks in which only a minority of trials included targets. Stimuli were cluttered real-world scenes, and targets were defined by category. We examined participants' ability to terminate search on the target-absent scenes based on an accurate assessment of scene difficulty. Scene difficulty was estimated by computing the mean correct-trial response time (RT) for each of the target-absent scenes across all participants. These group RTs were then correlated with each participants' individual correct-trial RTs for the same stimuli to assess the degree to which a given participant's search-termination times were correlated with those of the group. These correlations successfully predicted participants' target-detection success in both experiments. These experiments suggest that an integral part of visual search is the need to calibrate search behaviour to scenes of varying levels of complexity even when no targets are present.
机译:在典型的视觉搜索实验中,参与者搜索出现在一半试验中而另一半没有的目标。但是,许多现实世界中的任务都涉及仅偶尔出现的目标。鉴于此,重要的是要知道人们如何处理寻找经验不足的目标的问题。一种可能是,他们意识到在复杂的目标缺失场景中有效完成搜索的程度。为了检验该假设,我们让参与者完成了两个相对较长的搜索任务,其中只有少数试验包含了目标。刺激是混乱的现实世界场景,目标是按类别定义的。我们基于对场景难度的准确评估,研究了参与者在目标缺失场景上终止搜索的能力。通过计算所有参与者中每个目标缺失场景的平均正确审判响应时间(RT)来估计场景难度。然后将这些小组RT与每个参与者针对相同刺激的正确试验RT相关联,以评估给定参与者的搜索终止时间与该组的搜索终止时间相关的程度。这些相关性成功地预测了两个实验中参与者的目标检测成功。这些实验表明,即使没有目标存在,也需要将搜索行为校准到复杂程度不同的场景,这是视觉搜索的重要组成部分。

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