The limited capacity model of motivated mediated message processing: looking to the future

Fisher, James F.; Huskey, Richard; Keene, Justin Robert; Weber, René

doi:10.1080/23808985.2018.1534551

Cited by 43 publications

(17 citation statements)

References 149 publications

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“…This does not mean that progress is impossible, quite the opposite. A growing handful of theories has pinned down higher-order communication processes with sufficient specificity to be tractable across all three of Marr's levels (see e.g., Fisher et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…This has important consequences. When our theoretical assumptions are falsified, and we fail to notice, our capacity for theory building suffers (see, e.g., Fisher, Huskey, Keene, & Weber, 2018). In addition, the focus on specific why explanations for a behavior may exacerbate the fragmentation of communication science by rooting investigations firmly within their context (e.g., organizational or political) or method (e.g., computational or neuroscientific), while ignoring underlying processes and biological structures that may help explain behaviors across contexts.…”

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confidence: 99%

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

Huskey

Bue

Eden

et al. 2020

Journal of Communication

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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.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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

Huskey

Bue

Eden

et al. 2020

Journal of Communication

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“…These are inevitably limited by certain factors and may not always be fully available to successfully complete the task. This has been extended to Limited Capacity Model of Motivated Mediated Message Processing ( Fisher et al., 2018 ) which may explain why the motivation to attend to taboo, but captivating, sexual messages is so demanding. In this context, sexual advertising may demand more cognitive resources than non-sexual advertising; increased cognitive resources devoted to the ad’s sexual content may then create greater memory storage capacity.…”

Section: Introductionmentioning

confidence: 99%

Sex Does Not Sell: Effects of Sexual Advertising Parameters on Women Viewers’ Implicit and Explicit Recall of Ads and Brands

2021

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This study investigated implicit and explicit memory effects of sexual and non-sexual advertisements embedded in either a sexual or non-sexual program among women viewers. We predicted that sexual appeals would facilitate implicit memory for the brand, and we explored whether program-type (sexual or non-sexual) and its associated congruity would impact or moderate recall of the surrounding advertisement among a small sample (n = 52) of exclusively women advertisement viewers. Sexual (versus non-sexual) advertising led to significantly worse implicit memory for the brand logo but better explicit recall for the advertisement scene itself. There was no effect of sexual appeals on explicit brand name recall, and no significant effect on advertisement recall of the program type. There was a significant interaction effect for program type and advertisement type for explicit recall of the advertisement scene, in which program-type moderated sexual advertisement recall. These results suggest that sexual advertising may increase memory for the advertisement at the expense of recalling the brand advertised. Limitations and implications of this study are discussed.

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“…These findings are also in line with some of the recent theoretical and empirical work that connects LC4MP and ELM with misinformation. Work by Fisher et al (2018) and Kirkwood and Minas (2020) suggests that an individual's a priori beliefs play an important role in how the information is processed. News stories that match with one's prior beliefs tend to be analyzed via the peripheral route, while stories that do not match one's prior beliefs are analyzed via the central route.…”

Section: Discussionmentioning

confidence: 99%

“…This implies that people will respond selectively to certain channels of information in each modality, often in proportion to the intensity of the stimulus. For example, intensely negative stimuli such as offensive language or violence or one that challenges the beliefs of the individual will trigger a greater response than normal conversation or a selfie (Fisher, Huskey, Keene, & Weber, 2018; Lang, 2009). Thus, it is important that a machine‐learning model is able to capture a wide range of information channels so that it is able to process comprehensively the salient features within each modality to classify a news item as fake.…”

Section: Related Workmentioning

confidence: 99%

Detecting fake news stories via multimodal analysis

Singh

Ghosh

Sonagara

2020

Asso for Info Science & Tech

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Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.

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The limited capacity model of motivated mediated message processing: looking to the future

Cited by 43 publications

References 149 publications

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

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

Sex Does Not Sell: Effects of Sexual Advertising Parameters on Women Viewers’ Implicit and Explicit Recall of Ads and Brands

Detecting fake news stories via multimodal analysis

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