|
|
Type of Document Master's Thesis Author Spies, Jeffrey Robert URN etd-12142007-100853 Title Local sequence alignment as a method to detect temporal patterns in behavioral data Degree Master of Arts Department Psychology Advisory Committee
Advisor Name Title Steven M. Boker Committee Chair Scott E. Maxwell Committee Co-Chair Gregory R. Madey Committee Member Julia M. Braungart-Rieker Committee Member Keywords
- temporal dynamics
- time-series analysis
Date of Defense 2007-12-06 Availability unrestricted Abstract A time-series is a sequence of observations ordered by time. Often in the behavioral sciences, these observationsare instances of categorical variables and can be represented by a finite set of symbols, or an alphabet. In these
sequences, there may exist temporal patterns that are important in understanding the dynamics of behavior. However,
these patterns may be nontrivial, that is events in the patterns may be noncontiguous and therefore difficult to
detect by standard time-series analyses as these methods generally deal with understanding the structure of behavior
at a global level across the entirety of the series. In 1981, Temple Smith and Michael Waterman encountered a
similar issue in the field of molecular biology. They developed local sequence alignment as a means to discover
nontrivial patterns of similarity in long sequences of DNA and protein, each comprised of elements from an alphabet
of size four and twenty respectively. This project will describe methods of local sequence alignment as they exist
in the biological sciences and propose and implement analogous methods for use with temporal data.
Files
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higher-speed Access SpiesJR122007.pdf 781.23 Kb 00:03:37 00:01:51 00:01:37 00:00:48 00:00:04