Academic Journal
Chaos Theory and Child Development: Quantifying Nonlinear Pathways of Growth.
| Τίτλος: | Chaos Theory and Child Development: Quantifying Nonlinear Pathways of Growth. |
|---|---|
| Συγγραφείς: | Ossmy O; Centre for Brain and Cognitive Development, Birkbeck, University of London.; Centre for Educational Neuroscience, Birkbeck, University of London.; School of Psychological Sciences, Birkbeck, University of London. |
| Πηγή: | Perspectives on psychological science : a journal of the Association for Psychological Science [Perspect Psychol Sci] 2026 Sep; Vol. 21 (5), pp. 548-560. Date of Electronic Publication: 2026 Jun 29. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Sage Country of Publication: United States NLM ID: 101274347 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1745-6924 (Electronic) Linking ISSN: 17456916 NLM ISO Abbreviation: Perspect Psychol Sci Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2010- : Thousand Oaks, CA : Sage Original Publication: [Washington, D.C.] : Association for Psychological Science, 2006- |
| Ιατρικοί όροι (MeSH): | Child Development*/physiology , Nonlinear Dynamics* , Psychological Theory*, Humans |
| Περίληψη: | Traditional developmental science has often described child growth as a sequence of stages or linear progressions, yet many phenomena-abrupt spurts and regressions, idiosyncratic pathways, and widening individual differences-resist linear accounts. This article proposes chaos theory as a framework for quantifying developmental trajectories. Chaos theory, which addresses how complex patterns emerge from simple rules in deterministic yet unpredictable ways, aligns with observations of sensitive developmental periods, emergent behaviors, and divergent outcomes. I situate chaos theory alongside dynamic systems theory, neuroconstructivism, and developmental-cascade models and clarify how chaos might add mathematical precision to established insights: Bifurcation analysis identifies tipping points at which behaviors reorganize; Lyapunov exponents quantify stability and sensitivity to small perturbations; state-space methods reconstruct attractor landscapes from dense time series; and complexity metrics discriminate structured variability from noise. These tools convert powerful metaphors-soft assembly, attractors, cascades-into testable hypotheses about when and why qualitative change occurs. Such a framework also motivates microgenetic and high-density longitudinal designs, computational modeling of phase transitions, and interventions conceived as targeted perturbations delivered near sensitive windows. Finally, I discuss why adopting a chaos framework can be advantageous compared with (or in concert with) traditional linear models. |
| Contributed Indexing: | Keywords: attractor landscapes; bifurcations; chaos theory; developmental cascades; dynamic systems; neuroconstructivism; nonlinear dynamics |
| Entry Date(s): | Date Created: 20260629 Date Completed: 20260916 Latest Revision: 20260918 |
| Update Code: | 20260919 |
| DOI: | 10.1177/17456916261451880 |
| PMID: | 42372270 |
| Βάση Δεδομένων: | MEDLINE |
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