Set size manipulations reveal the boundary conditions of perceptual ensemble learning
Set size manipulations reveal the boundary conditions of perceptual ensemble learning
复制标题
DOI：
10.1016/j.visres.2017.08.003
复制
发表时间：
2017-11-01
期刊：
影响因子：
1.8
通讯作者：
Kristjansson, Arni
中科院分区：
文献类型：
--
作者：
Chetverikov, Audrey;Campana, Gianluca;Kristjansson, Arni
最近的证据表明，观察者能够以惊人的效率掌握环境中特征变化的模式。在视觉搜索任务中，所有干扰物都是从一定分布中随机抽取的，而不是都是均匀的，观察者能够学习到干扰物集的高度复杂的统计特性。仅经过几次试验（学习阶段），就可以学习分布均值、方差和（至关重要的）形状的统计特性，并且这些表示会影响随后测试阶段的搜索（Chetverikov, Campana, & Kristjansson, 2016）。为了评估这种分布学习的局限性，我们在两个实验中通过在学习阶段操纵集合大小来改变观察者对潜在分心物分布的可用信息。我们发现鲁棒分布学习只发生在大的集合大小上。我们还使用集合大小来评估学习分布属性是否使搜索更有效。结果揭示了学习发生所需的最小信息，从而描绘了环境中统计变化学习的边界条件。然而，分布学习对搜索效率的好处仍然不清楚。
Recent evidence suggests that observers can grasp patterns of feature variations in the environment with surprising efficiency. During visual search tasks where all distractors are randomly drawn from a certain distribution rather than all being homogeneous, observers are capable of learning highly complex statistical properties of distractor sets. After only a few trials (learning phase), the statistical properties of distributions mean, variance and crucially, shape - can be learned, and these representations affect search during a subsequent test phase (Chetverikov, Campana, & Kristjansson, 2016). To assess the limits of such distribution learning, we varied the information available to observers about the underlying distractor distributions by manipulating set size during the learning phase in two experiments. We found that robust distribution learning only occurred for large set sizes. We also used set size to assess whether the learning of distribution properties makes search more efficient. The results reveal how a certain minimum of information is required for learning to occur, thereby delineating the boundary conditions of learning of statistical variation in the environment. However, the benefits of distribution learning for search efficiency remain unclear.