The limited capacity model of motivated mediated message processing: taking stock of the past

ABSTRACT

In the 15 years since its inception, the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) has contributed to understanding regarding the dynamics of message processing in a variety of domains. In this manuscript we outline the foundations and assumptions of the LC4MP, discussing salient research from biology, cognitive psychology, and communication upon which the model is built. We then conduct a systematic review of the LC4MP literature with a focus on three primary domains: cognitive load, motivated processing, and memory. In a companion piece (Fisher, Huskey, Keene, & Weber, 2018) we look to the future of the model, incorporating recent findings from communication and cognate fields to inform an updated suite of predictions.

KEYWORDS:

• LC4Mp
• emotion
• motivation
• cognitive load
• memory
• systematic review

Acknowledgements

Special thanks to Mable Truong, Kory Feath, and Leandro Calcagnotto for their assistance in collecting and coding articles for the systematic review.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jacob T. Fisher http://orcid.org/0000-0002-2968-2557

Justin Robert Keene http://orcid.org/0000-0002-1404-0025

Richard Huskey http://orcid.org/0000-0002-4559-2439

René Weber http://orcid.org/0000-0002-8247-7341

Notes

1. As later published in (Popper, 1985).

2. Citation metrics retrieved from Thomson ReutersTM Web of ScienceTM on September 12th, 2018.

3. A complete list of the articles and chapters considered in this manuscript is available at https://osf.io/5zs4j/.

4. As a point of clarification, it is worth noting that Lang and colleagues (2006) describe resources available as resources required minus resources allocated. This seems to be in error, as (Lang & Basil, 1998) characterize available resources as resources allocated minus resources required, as we have asserted here.

Additional information

Funding

J.F. is an awardee of the National Science Foundation IGERT Traineeship for Network Science and Big Data.