Why are the batteries in the microwave?: Use of semantic information under uncertainty in a search task

A major problem in human cognition is to understand how newly-acquired information and long-standing beliefs about the environment combine to make decisions and plan behaviors. Over-dependence on long-standing beliefs may be a significant source of suboptimal decision-making in unusual circumstances. While the contribution of long-standing beliefs about the environment to search in real-world scenes is well-studied, less is known about how new evidence informs search decisions, and it is unclear whether the two sources of information are used together optimally to guide search. The present study expanded on the literature on semantic guidance in visual search by modeling a Bayesian ideal observer’s use of long-standing semantic beliefs and recent experience in an active search task. The ability to adjust expectations to the task environment was simulated using the Bayesian ideal observer, and subjects’ performance was compared to ideal observers that depended on prior knowledge and recent experience to varying degrees. Target locations were either congruent with scene semantics, incongruent with what would be expected from scene semantics, or random. Half of the subjects were able to learn to search for the target in incongruent locations over repeated experimental sessions when it was optimal to do so. These results suggest that searchers can learn to prioritize recent experience over knowledge of scenes in a near-optimal fashion when it is beneficial to do so, as long as the evidence from recent experience was learnable.

人类认知领域中的一个重大问题在于，探究如何将新获取的信息与对环境长期形成的信念相结合，进而做出决策和规划行为。过度依赖长期形成的信念可能是非理想情境下决策不佳的重要来源。尽管关于环境长期信念在现实场景搜索中的贡献已得到充分研究，但对于新证据如何影响搜索决策的了解尚显不足，且尚不明确这两种信息来源是否能够被优化地结合以指导搜索。本研究在视觉搜索语义引导文献的基础上进行了扩展，通过模拟贝叶斯理想观察者在主动搜索任务中运用长期语义信念和近期经验，对这一现象进行了建模。利用贝叶斯理想观察者模拟了对任务环境适应性的调整能力，并将受试者的表现与不同程度依赖先验知识和近期经验的理想观察者进行了比较。目标位置要么与场景语义一致，要么与场景语义预期不符，或者完全随机。在重复的实验过程中，一半的受试者能够学会在最优情况下在不一致的位置搜索目标。这些结果表明，搜索者可以在有益于这么做的情况下，学会以近乎理想的方式优先考虑近期经验而非场景知识。只要近期经验的证据是可学习的。