Format

Send to

Choose Destination
Front Physiol. 2013 Jan 29;4:1. doi: 10.3389/fphys.2013.00001. eCollection 2013.

A fractal approach to dynamic inference and distribution analysis.

Author information

1
Department of Psychology, CAP Center for Cognition, Action, and Perception, University of Cincinnati Cincinnati, OH, USA.

Abstract

Event-distributions inform scientists about the variability and dispersion of repeated measurements. This dispersion can be understood from a complex systems perspective, and quantified in terms of fractal geometry. The key premise is that a distribution's shape reveals information about the governing dynamics of the system that gave rise to the distribution. Two categories of characteristic dynamics are distinguished: additive systems governed by component-dominant dynamics and multiplicative or interdependent systems governed by interaction-dominant dynamics. A logic by which systems governed by interaction-dominant dynamics are expected to yield mixtures of lognormal and inverse power-law samples is discussed. These mixtures are described by a so-called cocktail model of response times derived from human cognitive performances. The overarching goals of this article are twofold: First, to offer readers an introduction to this theoretical perspective and second, to offer an overview of the related statistical methods.

KEYWORDS:

cognitive performance; distribution analysis; dynamic systems; fractal analysis; response time distributions; scaling relations

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center