Theory and data interactions in complex thinking

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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).