The HyGene Project



Hypothesis generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and lay people generate hypotheses to explain patterns of information (i.e., data) in the environment.

Our research has been focused on understanding the cognitive, motivational, and affective components of hypothesis generation and human judgment. The primary thesis underlying our research is that hypothesis generation processes serve as the lynchpin for understanding and interpreting information in the natural environment, for evaluating the probability of various hypotheses, and for searching for information in the environment to test hypotheses. Our research is directed at three levels of analysis:

  • What are the cognitive processes underlying how people generate, test, and evaluate diagnostic hypotheses?
  • Can we build artificial intelligent / decision support systems that capitalize on the power of Bayesian induction and the information inherent in natural language?
  • How can these models be scaled to serve as models of human induction in complex-dynamic tasks?