Principle Investigator: Daniel Rasmussen

Raven's Progressive Matrices is one of the most widely used tests of general intelligence. It has been found to be both domain independent and highly correlated with other measures of intellectual ability [Marshalek et al., 1983]. In the RPM subjects are presented with a 3x3 matrix, where each cell-- except for the blank cell in the bottom right--contains multiple features. The task is to determine, given eight possibilities, which answer belongs in the blank cell. Subjects accomplish this by finding the rules that govern the features in each row or column. Once these rules have been found, they can be applied to the last row/column to determine which features will complete the rule in the blank cell. This task requires a complex array of cognitive abilities, and so represents an interesting challenge for understanding human intelligence.

There are many theories as to how subjects go about solving these matrices. Over the years researchers have focused on topics including the di fferent types of rules (Carpenter et al. [1990], DeShon et al. [1995]), error types (Babcock [2002]), the importance of early visual processing (Meo et al. [2007]), working memory (Kyllonen and Christal [1990]), and executive functions (Unsworth and Engle [2005]).

There are two significant tasks which stand out as necessary to flesh out this research. The first is developing a working model that combines these theories to recreate human performance. This is the proverbial proof in the pudding; if our theories about how subjects solve the RPM are correct, then we should be able to use those theories to solve the RPM. The second gap in the RPM literature is an explanation of how subjects create new rules. What is missing is that crucial intermediate step; we have theories on how visual information is collected in RPM situations, and given a set of rules we have theories on how they are used to solve the matrix, but we are lacking in theories that explain how we make that move from visual information to rule.