In this special issue devoted to speaking across communication subfields, we introduce a domain general explanatory framework that integrates biological explanation with communication science and organizes our field around a shared explanatory empirical model. Specifically, we draw on David Marr’s classical framework, which subdivides the explanation of human behavior into three levels: computation (why), algorithm (what), and implementation (how). Prior theorizing and research in communication has primarily addressed Marr’s computational level (why), but has less frequently investigated algorithmic (what) or implementation (how all communication phenomena emerge from and rely on biological processes) explanations. Here, we introduce Marr’s framework and apply it to three research domains in communication science—audience research, persuasion, and social comparisons—to demonstrate what a unifying framework for explaining communication across the levels of why, what, and how can look like, and how Marr’s framework speaks to and receives input from all subfields of communication inquiry.
(LC4MP) aims to understand message processing dynamics. Despite 20 years of research, no meta-analysis has assessed LC4MP effects. We conducted a meta-analysis of the model to examine three theoretical research domains in the LC4MP: cognitive load, motivation, and memory. Results from 142 articles and 683 effects demonstrate that pooled effect sizes for research domain range from r = .314-.398. Effect sizes vary by measurement modality with self-report resulting in the largest pooled effect size, followed by behavioral, and finally psychophysiological measures. We did not detect evidence of publication bias. These findings offer meta-analytic support for LC4MP research domains and are discussed in terms of falsifiability, predictive and explanatory power.
Flow is thought to occur when both task difficulty and individual ability are high. Flow experiences are highly rewarding and are associated with well-being. Importantly, media use can be a source of flow. Communication scholars have a long history of theoretical inquiry into how flow biases media selection, how different media content results in flow, and how flow influences media processing and effects. However, the neurobiological basis of flow during media use is not well understood, limiting our explanatory capacity to specify how media contribute to flow or well-being. Here, we show that flow is associated with a flexible and modular brain-network topology, which may offer an explanation for why flow is simultaneously perceived as high-control and effortless, even when the task difficulty is high. Our study tests core predictions derived from synchronization theory, and our results provide qualified support for the theory while also suggesting important theoretical updates.
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