Open AccessThe structure of individual differences in Heterogeneous Stock mice across problem types and motivational systems
Author(s) -
Locurto C.,
Fortin E.,
Sullivan R.
Publication year - 2003
Publication title -
genes, brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.315
H-Index - 91
eISSN - 1601-183X
pISSN - 1601-1848
DOI - 10.1034/j.1601-183x.2003.00006.x
Subject(s) - principal component analysis , correlation , task (project management) , set (abstract data type) , working memory , eigenvalues and eigenvectors , matrix (chemical analysis) , cognitive psychology , mathematics , computer science , psychology , cognition , statistics , physics , geometry , management , materials science , quantum mechanics , neuroscience , economics , programming language , composite material
Sixty Heterogeneous Stock (HS) mice received a battery of six problem‐solving tasks and three control procedures. The problem‐solving tasks included Hebb‐Williams, a place learning task conducted in a plus maze, radial maze, a working memory test following the radial maze, a set of detour problems and a visual non‐matching to sample task. The control procedures consisted of land and water activity measures and a light‐dark test. The correlation matrix derived from these tasks did not exhibit positive manifold, that is, positive correlations across all problem‐solving tasks. Principal components analysis reduced the correlation matrix to four components with eigenvalues exceeding 1.0. Instead of the general factor solution common in the study of human problem‐solving, this component structure appeared more congenial to a modular interpretation, with the four components each explaining approximately the same magnitude of matrix variance.