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  Projects in Psychological Foundations of Causal Judgment and Human Data-Mining
  The Bayesian Network Lens


  Research :: Psychological Foundations of Causal Judgment and Human Data-Mining

Many fields of human endeavour require reaching conclusions with small data samples. Unlike traditional data mining algorithms, humans make inferences on small samples, forget the data, and then change their conclusions as new data are obtained. Humans thus learn across many different data sets with small memory requirements.

IHMC researchers use a variety of techniques to study human data-mining. Modeling of dynamical, low-memory associative learning defines limits on learning procedures. Systems in which complex functions and causes underlie observed variables are analyzed statistically. Characterization and testing of algorithms for learning causal relations contribute to the development of new algorithms. We also study strategies young children use in learning about cause and effect.

Understanding such processes is important not only for cognitive psychology but also for many circumstances where automated learning methods are needed, for example in space exploration where robotic explorers may have very limited memory capacities, and in any circumstance in which related data are collected in separate databases.