Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Energy and Personality: A Bridge between Physics and Psychology

Version 1 : Received: 14 May 2021 / Approved: 17 May 2021 / Online: 17 May 2021 (07:56:34 CEST)

A peer-reviewed article of this Preprint also exists.

Caselles, A.; Micó, J.C.; Amigó, S. Energy and Personality: A Bridge between Physics and Psychology. Mathematics 2021, 9, 1339. Caselles, A.; Micó, J.C.; Amigó, S. Energy and Personality: A Bridge between Physics and Psychology. Mathematics 2021, 9, 1339.


The objective of this paper is to present a mathematical formalism that states a bridge between Physics and Psychology, concretely between analytical dynamics and personality theory in order to open new insights in this theory. In this formalism energy plays a central role. First, the short-term personality dynamics can be measured by the General Factor of Personality (GFP) response to an arbitrary stimulus. This GFP dynamical response is modelled by a stimulus-response model: an integro-differential equation. The bridge between Physics and Psychology is provided when the stimulus-response model can be formulated as a linear second order differential equation and, subsequently, reformulated as a Newtonian equation. This bridge is strengthened when the Newtonian equation is derived from a minimum action principle, obtaining the current Lagrangian and Hamiltonian functions. However, the Hamiltonian is a non-conserved energy. Then, some changes provide a conserved Hamiltonian function: the Ermakov-Lewis energy. This energy is presented, as well as the GFP dynamical response that can be derived from it. An application case is presented: an experimental design in which 28 individuals consumed 26.51 g of alcohol. This experiment provides an ordinal scale for the Ermakov-Lewis energies that predicts the effect of a single dose of alcohol.


personality dynamics; general factor of personality; stimulus-response model; minimum action principle; Hamiltonian; Ermakov-Lewis energy


Computer Science and Mathematics, Algebra and Number Theory

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