M Casarrubea1, M S Magnusson2, M T Anguera3, G K Jonsson2, M Castañer4, A Santangelo5, M Palacino6, S Aiello6, F Faulisi6, G Raso6, S Puigarnau4, O Camerino4, G Di Giovanni7, G Crescimanno6. 1. Laboratory of Behavioral Physiology, Department of Experimental Biomedicine and Clinical Neurosciences (Bio.Ne.C.), Human Physiology Section "Giuseppe Pagano", University of Palermo, Italy. Electronic address: maurizio.casarrubea@unipa.it. 2. Human Behavior Laboratory, University of Iceland, Reykjavik, Iceland. 3. Faculty of Psychology, Institute of Neurosciences, University of Barcelona, Spain. 4. INEFC (National Institute of Physical Education of Catalonia) and IRBLLEIDA (Lleida Institute for Biomedical Research Dr. Pifarré Foundation), University of Lleida, Spain. 5. Laboratory of Behavioral Physiology, Department of Experimental Biomedicine and Clinical Neurosciences (Bio.Ne.C.), Human Physiology Section "Giuseppe Pagano", University of Palermo, Italy; Psychiatric Unit, Department of Health Sciences, University of Florence, Florence, Italy. 6. Laboratory of Behavioral Physiology, Department of Experimental Biomedicine and Clinical Neurosciences (Bio.Ne.C.), Human Physiology Section "Giuseppe Pagano", University of Palermo, Italy. 7. Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, University of Malta, Msida, Malta.
Abstract
BACKGROUND: The behaviour of all living beings consists of hidden patterns in time; consequently, its nature and its underlying dynamics are intrinsically difficult to be perceived and detected by the unaided observer. METHOD: Such a scientific challenge calls for improved means of detection, data handling and analysis. By using a powerful and versatile technique known as T-pattern detection and analysis (TPA) it is possible to unveil hidden relationships among the behavioural events in time. RESULTS: TPA is demonstrated to be a solid and versatile tool to study the deep structure of behaviour in different experimental contexts, both in human and non human subjects. CONCLUSION: This review deepens and extends contents recently published by adding new concepts and examples concerning the applications of TPA in the study of behaviour both in human and non-human subjects.
BACKGROUND: The behaviour of all living beings consists of hidden patterns in time; consequently, its nature and its underlying dynamics are intrinsically difficult to be perceived and detected by the unaided observer. METHOD: Such a scientific challenge calls for improved means of detection, data handling and analysis. By using a powerful and versatile technique known as T-pattern detection and analysis (TPA) it is possible to unveil hidden relationships among the behavioural events in time. RESULTS:TPA is demonstrated to be a solid and versatile tool to study the deep structure of behaviour in different experimental contexts, both in human and non human subjects. CONCLUSION: This review deepens and extends contents recently published by adding new concepts and examples concerning the applications of TPA in the study of behaviour both in human and non-human subjects.
Authors: Maurizio Casarrubea; Stefania Aiello; Giuseppe Di Giovanni; Andrea Santangelo; Manfredi Palacino; Giuseppe Crescimanno Journal: Front Psychol Date: 2019-04-24
Authors: Fabrizio Grieco; Briana J Bernstein; Barbara Biemans; Lior Bikovski; C Joseph Burnett; Jesse D Cushman; Elsbeth A van Dam; Sydney A Fry; Bar Richmond-Hacham; Judith R Homberg; Martien J H Kas; Helmut W Kessels; Bastijn Koopmans; Michael J Krashes; Vaishnav Krishnan; Sreemathi Logan; Maarten Loos; Katharine E McCann; Qendresa Parduzi; Chaim G Pick; Thomas D Prevot; Gernot Riedel; Lianne Robinson; Mina Sadighi; August B Smit; William Sonntag; Reinko F Roelofs; Ruud A J Tegelenbosch; Lucas P J J Noldus Journal: Front Behav Neurosci Date: 2021-09-24 Impact factor: 3.617
Authors: Pere Lavega-Burgués; Rafael A Luchoro-Parrilla; Jorge Serna; Cristòfol Salas-Santandreu; Pablo Aires-Araujo; Rosa Rodríguez-Arregi; Verónica Muñoz-Arroyave; Assumpta Ensenyat; Sabrine Damian-Silva; Leonardo Machado; Queralt Prat; Unai Sáez de Ocáriz; Aaron Rillo-Albert; David Martín-Martínez; Miguel Pic Journal: Front Psychol Date: 2020-07-07