Marr’s Tri-Level Framework Integrates Biological Explanation Across Communication Subfields

Abstract

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.

Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science

“…Second, and this is a less conventional and less explored idea, the framework can also be applied to noncognitive or nonindividual capacities of relevance to social, developmental, and evolutionary psychology and more. Preliminary explorations into computational-level analyses of noncognitive or nonindividual capacities can be found in work by Krafft and Griffiths (2018) on distributed social processes, Huskey et al (2020) on communication processes, Rich et al (2020) on natural and/or cultural-evolution processes, and van Rooij (2012) on self-organized processes.…”

Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns

“…Two important yet unresolved questions in the persuasion neuroscience literature are: (1) what are the relevant structure-function mappings in persuasive message processing, and (2) how selective are these mappings to the putative processes of interest ( Huskey et al, 2020 ). On one hand, a small handful of structures including the vMPFC and VS have been strongly implicated in persuasive message processing as well as in message tailoring ( Tompson et al, 2015 ; Falk and Scholz, 2018 ) and neural activity in these structures has been repeatedly shown to predict persuasive message outcomes above and beyond what is capable with more traditional measures ( Falk et al, 2012 , 2016 ; Pegors et al, 2017 ; Cooper et al, 2018 ; Doré et al, 2019 ).…”

Integrating Media Selection and Media Effects Using Decision Theory

“…Formal models exist at the algorithmic level, acting as "recipes'' that translate inputs into outputs in view of the computational and implemental description of the system (Crockett, 2016;Niv & Langdon, 2016). Because of this, formal models allow researchers to connect behavioral processes to the underlying neural and biological systems that enable the behavior of interest (Huskey et al, 2020;van Rooij & Baggio, 2021). Perhaps of special interest to media psychology scholars, formal models also enable research approaches that bridge individual-and group-level processes, combining models of the behavior of individuals with agent-based models describing their interactions (Madsen et al, 2019;Waldherr & Wettstein, 2019).…”

“…Although there are undoubtedly numerous models that will prove to be useful within media psychology, we suggest that those derived from decision theory (Dayan & Daw, 2008;Körding, 2007) are an especially promising place to begin. This is for two primary reasons: First, decision theory affords a conceptual structure anchored in biological explanation (Cappella, 1996;Weber, Mathiak, et al, 2009) and spanning multiple levels of analysis (Huskey et al, 2020). Second, extant work in decision theory has provided a host of well-validated and well-documented models (and software packages) that can flatten the learning curve.…”

“…A large body of media psychology research has sought to isolate and catalog the goals that motivate media selection and use behaviors based on intrinsic (Tamborini et al, 2010;Weber, Tamborini, et al, 2009), or extrinsic needs (Reinecke et al, 2014). This research serves to illuminate a small portion of the rich landscape of context-specific reasons why people may select certain media, but it is still unclear what rules individuals may use to select media across different (often competing) motivations or how these rules are implemented within the human cognitive system (Huskey et al, 2020). Furthermore, it is unclear how to consider this diverse patchwork of motivational influences relative to one another.…”