Theory and data interactions in complex thinking

几个世纪以来，来自不同学科的学者一直在争论人们思考和推理数据的最佳（或逻辑）方式。矛盾的是，现在有大量的经验证据表明，人们在评估数据时没有逻辑推理，而且天生就有偏见。例如，如果数据与他们的信念和期望一致，而不是不一致，人们更有可能接受数据是真实的。这些偏见的根源是一个持续争论的话题，也是我提出的研究计划的主要焦点。具体来说，我的建议涉及两个关键目标。首先，推理中的偏见有注意基础吗？例如，人们是否更倾向于关注与他们的信念一致的数据，而忽略与他们的信念不一致的数据？第二，在明显不同的领域（如因果推理和演绎推理）的偏差是相似或不同的潜在机制的结果吗？我的研究计划的核心是寻求涉及各种实验方法（例如行为，眼动追踪和脑成像方法）的研究结果的融合。通过使用多种收敛方法，提出的研究计划将提供推理中信念偏差的机制的综合说明。此外，通过检查跨多个推理领域的信念偏差的性质，我将获得必要的数据，以开发一个更全面的推理和决策的一般模型。我的长期目标是，我的研究项目产生的数据将在认知实验室之外有深远的应用，为医疗决策、法律决策和科学推理等领域提供信息。例如，了解偏见的潜在机制可能对如何在偏见阻碍最佳决策时最大限度地减少这种偏见的影响具有重要意义（有关这些应用的进一步示例，请参阅本提案表格100第二部分）。

For centuries, scholars from a variety of academic disciplines have debated the optimal (or logical) way for people to think and reason about data. Paradoxically, there is now abundant empirical evidence that suggests that people do not reasoning logically and are inherently biased when evaluating data. For example, people are more likely accept data as real if it is consistent rather than inconsistent with their beliefs and expectations. The locus of these biases, a topic of continuing debate, forms the primary focus of my proposed research program. Specifically, there are two key objectives addressed by my proposal. First, do biases in reasoning have an attentional basis? For example, are people more inclined to attend to data that are consistent with their beliefs and ignore data that are inconsistent with their beliefs? Second, are the biases in apparently disparate domains (e.g. causal reasoning and deductive reasoning) the result of similar or different underlying mechanisms? At the heart of my research program is the quest for converging results across studies involving various experimental methodologies (e.g. behavioural, eye tracking, and brain imaging methodologies). Through the use of multiple converging methodologies, the proposed research program will provide a comprehensive account of the mechanisms underlying belief biases in reasoning. In addition, by examining the nature of belief biases across multiple reasoning domains, I will acquire the data necessary to develop a more comprehensive general model of reasoning and decision-making. It is my long range goal that the data generated from my research program will have far reaching applications beyond the cognitive laboratory by informing such fields as medical decision making, legal decision making, and scientific reasoning. For example, an understanding of the mechanisms underlying biases may have significant implications for how one can hope to minimize the influence of such biases when they hinder optimal decision-making (for further examples of these applications see Form 100 part II of this proposal).