The Handbook of Communication Science and Biology

Edited by Kory Floyd, Rene Weber

Copyright Year 2020

ISBN 9780815376736
Published May 21, 2020 by Routledge
524 Pages 23 B/W Illustration

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.

Null Hypothesis Significance Testing in Communication Research

We consider theoretical and methodological issues associated with null hypothesis significance testing (NHST) and offer a  practical guide for NHST.


Effect & Equivalence Testing – SPSS Custom Dialogs

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.


Neusrel – Nonlinear Structural Equation Modeling

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.