The Handbook of Communication Science and Biology
Edited by Kory Floyd, Rene Weber
Copyright Year 2020

Edited by Kory Floyd, Rene Weber
Copyright Year 2020
A Synchronization Theory of Flow
The Synchronization Theory of Flow offers a neurological explanation for flow experiences. In this view, flow is a discrete, energetically optimized, and gratifying experience resulting from the synchronization of attentional and reward networks under conditions of a balance between challenge and skill.
The Neurophysiological Perspective in Mass Communication Research
The neurophysiological perspective argues for a paradigm shift to a new way of thinking about mass communication that goes beyond the nomothetic deductive models of the past and embraces current scientfic ontology and epistemology.
Flow Theory: Advances in Experimental Manipulation & Measurement
An experimental study manipulates level of challenge in a video game and makes a case for the use of secondary task response times as a continuous, unobtrusive measure of flow.
We consider theoretical and methodological issues associated with null hypothesis significance testing (NHST) and offer a practical guide for NHST.
Although equivalence testing is needed when a researcher’s goal is to support the null hypothesis (i.e., no substantial effect), equivalence tests are virtually unknown and unused in communication research. We provide the rationale for and theoretical background of effect- and equivalence testing. SPSS custom dialogs are provided to assist the research community in conducting tests of statistical effects and statistical equivalence. Find out more at Effect & Equivalence Testing under Service.
The Media Neuroscience Lab is a scientific collaborator of Neusrel Causal Analytics. This collaboration seeks to advance nonlinear structural equation modeling methods by incorporating machine learning techniques (e.g., neural networks). This statistical approach is of particular interest to the lab as it expands the researcher’s toolbox when analyzing brain imaging data and other complex datasets. Find out more at Neusrel Causal Analytics.