q-Deformed Brownian Motion

Brownian motion may be embedded in the Fock space of bosonic free field in one dimension.Extending this correspondence to a family of creation and annihilation operators satisfying a q-deformed algebra, the notion of q-deformation is carried from the algebra to the domain of stochastic processes.The properties of q-deformed Brownian motion, in particular its non-Gaussian nature and cumulant structure,are established.Comment: 6 page

Symbolic Sequences and Tsallis Entropy

We address this work to investigate symbolic sequences with long-range correlations by using computational simulation. We analyze sequences with two, three and four symbols that could be repeated $l$ times, with the probability distribution $p(l)\propto 1/ l^{\mu}$. For these sequences, we verified that the usual entropy increases more slowly when the symbols are correlated and the Tsallis entropy exhibits, for a suitable choice of $q$, a linear behavior. We also study the chain as a random walk-like process and observe a nonusual diffusive behavior depending on the values of the parameter $\mu$.Comment: Published in the Brazilian Journal of Physic

Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies.

Scholars across domains in psychology, physiology, and neuroscience have long been interested in the study of shared physiological experiences between people. Recent technological and analytic advances allow researchers to examine new questions about how shared physiological experiences occur. Yet comprehensive guides that address the theoretical, methodological, and analytic components of studying these processes are lacking. The goal of this article is to provide such a guide. We begin by addressing basic theoretical issues in the study of shared physiological states by presenting five guiding theoretical principles for making psychological inferences from physiological influence-the extent to which one dyad member's physiology predicts the other dyad member's physiology at a future time point. Second, keeping theoretical and conceptual concerns at the forefront, we outline considerations and recommendations for designing, implementing, and analyzing dyadic psychophysiological studies. In so doing, we discuss the different types of physiological measures one could use to address different theoretical questions. Third, we provide three illustrative examples in which we estimate physiological influence, using the stability and influence model. We conclude by providing detail about power analyses for the model and by comparing the strengths and limitations of this model with preexisting models. (PsycINFO Database Record (c) 2018 APA, all rights reserved)