When you search repeatedly for a set of items among very similar distractors, does that make you more efficient in locating the targets? To address this, we had observers search for two categories of targets among the same set of distractors across trials. Visual and conceptual similarity of the stimuli were validated with a multidimensional scaling analysis, and separately using a deep neural network model. After a few blocks of visual search trials, the distractor set was replaced. In three experiments, we manipulated the level of discriminability between the targets and distractors before and after the distractors were replaced. Our results suggest that in the presence of repeated distractors, observers generally become more efficient. However, the difficulty of the search task does impact how efficient people are when the distractor set is replaced. Specifically, when the training is easy, people are more impaired in a difficult transfer test. We attribute this effect to the precision of the target template generated during training. In particular, a coarse target template is created when the target and distractors are easy to discriminate. These coarse target templates do not transfer well in a context with new distractors. This suggests that learning with more distinct targets and distractors can result in lower performance when context changes, but observers recover from this effect quickly (within a block of search trials).

当您在非常相似的干扰项中反复搜索一组项目时，这是否会让您更有效地定位目标？为了解决这个问题，我们让观察者在试验中的同一组干扰因素中寻找两类目标。通过多维尺度分析并分别使用深度神经网络模型来验证刺激的视觉和概念相似性。经过几次视觉搜索试验后，干扰器组被更换。在三个实验中，我们在更换干扰物之前和之后操纵了目标和干扰物之间的区分度。我们的结果表明，在存在重复干扰因素的情况下，观察者通常会变得更有效率。然而，当干扰物组被替换时，搜索任务的难度确实会影响人们的效率。具体来说，当训练很容易时，人们在困难的转移测试中受到的损害更大。我们将这种效果归因于训练期间生成的目标模板的精度。特别是，当目标和干扰物易于区分时，会创建粗目标模板。这些粗略的目标模板在有新干扰因素的环境中不能很好地转移。这表明，当上下文发生变化时，使用更明确的目标和干扰因素进行学习可能会导致性能下降，但观察者很快就会从这种影响中恢复过来（在搜索试验的范围内）。