T-Pattern Detection and Analysis (TPA) With THEMETM: A Mixed Methods Approach

Started in the early 1970’s, this work was inspired by social interaction analysis based on direct observation and careful coding of behaviors according to a list of behavioral (mostly ethological) categories. Especially the ethological work of N. Tinbergen, K. Lorenz and K. von Frisch, for which they shared a Nobel Prize in 1973 in Medicine or Physiology, but also H. Montagner’s ethological analyses of interactions in social insects and children. S. Duncan’s psychological and linguistic research on turn taking in human interactions provided great inspiration and so did Chomsky’s work on syntactic structure and Skinner’s probabilistic real-time functional analysis and their consequent debate. A hypothesis concerning numerous kinds of temporal and spatial natural and especially biological structure, the T-pattern is a hierarchical self-similar fractal-like structure recurring with significant translational symmetry on a single discrete dimension, initially real-time. It also points to profound self-similarity across many levels of biological spatio-temporal organization as it seems characteristic of molecular structure such as genes and a multitude of recurrent motives on DNA and its 3D generalization corresponds to (3D) folded proteins. Developed initially to facilitate empirical analysis, the T-pattern and its detection algorithms were first presented in A.I. (Magnusson, 1981) and in Applied Statistics (Magnusson, 1983) through the THEME (3k Fortran IV) software using an evolution algorithm. It is now over 300k lines of code, runs under Windows and recently uses parallel processing for increased speed. This has allowed abundant detection of hidden structure in numerous kinds of biological phenomena at highly varied scales from human behavior at time scales of days (Hirschenhauser, 2002, 2005) to interactions of many individual neurons simultaneously registered at a temporal resolution of 10-6 s in neuronal networks in rat brains to ongoing work on T-patterns in DNA molecules at a spatial nano scale. T-pattern detection and analysis (TPA) thus mixes qualitative and quantitative analyses as T-patterns themselves are artificial categories composed of recurring coding categories with special real-scale statistical relations between their instances. After their detection T-patterns are thus analyzed much as other behavioral categories.