Recently, complexity scientists have suggested understanding complex systems from an information processing perspective [1–4], and the natural way to do so is to think of information processing pathways as functions transforming inputs to outputs. By combining a small number of building blocks in different ways, very many functions can be obtained [5]. In this talk, I will explain the connection between information processing by complex systems and recurrent activity sequences in their dynamics, and argue that an understanding of information processing pathways in terms of these dynamical motifs is important for designing effective interventions. I then describe two recently completed studies, where recurrent activity sequences (or dynamical motifs) were identified and tested for statistical significance. In the first study [6], we video recorded 37 shared book reading (SBR) sessions, and thereafter annotated each of these sessions for 26 activities (reading the book, comments and questions, management talk by the teacher, and responses from the children). For all SBR sessions, the annotations consisted of sequences of one activity followed by another (transitions). We tested the empirical data against a null model where activities occur randomly, to identify 34 transitions that occur more frequently than by chance, and visualize these transitions in the form of a static network. We then chose six significant transitions, and tested their extensions against the same null model to identify statistically significant length-3 sequences. We repeated this extension procedure to obtain length-4, length-5, and longer sequences until no further statistically significant extensions can be found. Finally, we organized the longest significant sequences into five families of dynamical motifs, and discuss their implications on the effectiveness of SBR. In the second study [7], we manually annotated 184 recorded lectures from the Nanyang Technological University for 17 activities, before visualizing the transitions between these activities in the form of a static network. With a modification in our statistical procedure, we tested sequences of lengths up to 8 for significance, to explore the phenomenon of adaptation in the dynamical motifs.
References
[1] Haken, H. Information and Self-Organization: A Macroscopic Approach to Complex Systems. Springer, 2006.
[2] Nicolis, G. and Nicolis, C. Foundations of Complex systems: Emergence, Information and Prediction. World Scientific, 2012.
[3] Tkačik, G. and Bialek, W. Information processing in living systems. Annual Review of Condensed Matter Physics, vol. 7, pp. 89-117, 2016.
[4] Weisbuch, G. Complex Systems Dynamics. CRC Press, 2018.
[5] Holland, J.H. Signals and Boundaries: Building Blocks for Complex Adaptive Systems. MIT Press, 2012.
[6] Sun, H., Toh, W., Cheong, S.A., Dickinson, D., Verspoor, M. Associations Among Teacher Questions and Comments and Kindergarten Children’s Acquisition of Mandarin Vocabulary. Under review for Modern Language Journal.
[7] Cheong, S.A. et al. Identifying long recurrent sequences of pedagogical activities in video recordings of university lectures. In preparation.