On Dynamical Systems Theory in Quantitative Psychology and Cognition Science: A Fair Discrimination Between Deterministic and Statistical Counterparts Is Required
Adam Gadomski, Marcel Ausloos, and Tahlia Casey

TL;DR
This paper emphasizes the importance of distinguishing between deterministic and statistical models in dynamical systems theory applied to psychology and cognition, highlighting how data origins influence interpretation and treatment strategies.
Contribution
It advocates for careful consideration of data distributions and initial conditions to improve the accuracy of psychological and cognitive analyses within dynamical systems frameworks.
Findings
Statistical approaches can lead to discrepancies in data analysis.
Understanding data origins helps determine appropriate statistical models.
Behavioral patterns and memory effects are crucial for treatment strategies.
Abstract
The present communication addresses a set of observations, obeying both deterministic as well as statistical formal requirements, and serving to operate within the framework of the dynamical systems theory, with a certain emphasis placed on initial data. It is argued that statistical approaches can manifest themselves non unequivocally, leading to certain virtual discrepancies in psychological and/or cognitive data analyses, termed sometimes in literature as, questionable research practices. This communication points to the demand for a deep awareness of the data origins, which can indicate whether the exponential (Malthus type) or the algebraic (Pareto type) statistical distribution ought to be effectively considered in practical interpretation. This is also related to the question of how frequently patients behave in a specific way, and the significance of these behaviors in…
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Taxonomy
TopicsMental Health Research Topics · Ecosystem dynamics and resilience · Complex Systems and Time Series Analysis
