Reasoning about complex causal systems is central to everyday cognition, and yet our knowledge of such systems is often incoherent, in that individual (or small sets of) causal relationships are not integrated to form more comprehensive mental models. Two factors are hypothesized to have large roles in determining when, why, and how causal knowledge is integrated: (a) the use of correlational information to distinguish among multiple possible integrated models and (b) the use of fragmentary knowledge in reasoning. Two studies examine how adults use correlational information in integration. These studies distinguish between a hypothesis derived from Bayesian network theory and an alternative hypothesis in which learners interprete correlational information in a way that enables them to acquire incomplete or shallow models. Two further studies track the development of these abilities in young children. Finally, it is hypothesized that using fragmentary knowledge for different kinds of reasoning leads to different kinds of integrated models. This hypothesis is tested in two studies. ? ?

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
5F32HD049358-02
Application #
7478796
Study Section
Special Emphasis Panel (ZRG1-F12A (20))
Program Officer
Freund, Lisa S
Project Start
2006-07-01
Project End
2007-08-17
Budget Start
2007-07-01
Budget End
2007-08-17
Support Year
2
Fiscal Year
2007
Total Cost
$8,826
Indirect Cost
Name
Yale University
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520