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。讽刺的是，现在有大量的经验证据表明，人们在评估数据时不会进行逻辑推理，并且天生就有偏见。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为真实的。这些偏见的轨迹，一个持续争论的话题，形成了我提出的研究计划的主要焦点。具体而言，我的建议涉及两个关键目标。首先，推理中的偏见是否有注意力基础？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）的偏见是相似或不同的潜在机制的结果？在我的研究计划的核心是寻求跨研究融合的结果，涉及各种实验方法（例如行为，眼动跟踪和大脑成像方法）。通过使用多种融合的方法，拟议的研究计划将提供一个全面的解释推理中的信念偏见的机制。此外，通过研究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，从我的研究计划中产生的数据将通过告知医疗决策，法律的决策和科学推理等领域而具有超越认知实验室的深远应用。例如，了解偏见背后的机制可能对人们如何希望在这些偏见阻碍最佳决策时将其影响降至最低具有重要意义（关于这些应用的进一步例子，见本提案的表格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).