Discovering Hidden Temporal Patterns in Behavior and Interaction

T-Pattern Detection and Analysis with THEME™

  • Magnus S. Magnusson
  • Judee K. Burgoon
  • Maurizio Casarrubea

Part of the Neuromethods book series (NM, volume 111)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Human Behavior

    1. Front Matter
      Pages 1-1
    2. Judee K. Burgoon, David Wilson, Michael Hass, Ryan Schuetzler
      Pages 37-62
    3. Valentino Zurloni, Barbara Diana, Massimiliano Elia, Luigi Anolli
      Pages 63-82
    4. Marta Castañer, Oleguer Camerino, M. Teresa Anguera, Gudberg K. Jonsson
      Pages 83-100
    5. Aaron S. Kemp, Mohammed R. Lenjavi, Paul E. Touchette, David Pincus, Magnus S. Magnusson, Curt A. Sandman
      Pages 101-124
    6. Diana Lynn Woods, Maria Yefimova, Haesook Kim, Linda R. Phillips
      Pages 125-142
    7. Anaïs Racca, Magnus S. Magnusson, César Ades, Claude Baudoin
      Pages 155-164
    8. Monika Suckfüll, Dagmar Unz
      Pages 165-181
    9. Michael Brill, Gudberg K. Jonsson, Magnus S. Magnusson, Frank Schwab
      Pages 183-193
    10. Liesbet Quaeghebeur, David McNeill
      Pages 195-213
  3. Animal and Neuronal Behavior (Non-human Behavior)

    1. Front Matter
      Pages 215-215
    2. Maurizio Casarrubea, Magnus S. Magnusson, Giuseppe Di Giovanni, Vincent Roy, Arnaud Arabo, Andrea Santangelo et al.
      Pages 217-235
    3. Charles C. Horn, Magnus S. Magnusson
      Pages 237-253
    4. Isabelle Baraud, Bertrand L. Deputte, Jean-Sébastien Pierre, Catherine Blois-Heulin
      Pages 255-277
    5. Alister U. Nicol, Anne Segonds-Pichon, Magnus S. Magnusson
      Pages 309-324

About this book

Introduction

A thorough look at the different research applications of temporal pattern detection and analysis using specially developed software, THEME (TM). The T-system is a model for discovering hidden recurring patterns in observable behavior and can be useful to researchers in neuroscience, psychology, biology, robotics, finance, medicine, and many other fields. This system forms the basis for the search algorithms in THEME (TM) , now in its 6th edition and available in both educational and fully commercial versions. Each chapter describes the methodology used and discusses the findings in detail, providing a highly useful primer for advanced students and researchers.    

Keywords

THEME (TM) behavior computational neuroscience t-pattern analysis temporal pattern

Editors and affiliations

  • Magnus S. Magnusson
    • 1
  • Judee K. Burgoon
    • 2
  • Maurizio Casarrubea
    • 3
  1. 1.University of IcelandReykjavikIceland
  2. 2.University of ArizonaTucson, AZUSA
  3. 3.Department of Bio.Ne.C.University of PalermoPalermoItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-3249-8
  • Copyright Information Springer Science+Business Media New York 2016
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-4939-3248-1
  • Online ISBN 978-1-4939-3249-8
  • Series Print ISSN 0893-2336
  • Series Online ISSN 1940-6045
  • About this book