MNL Lab Notebook
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  • Expectations and Responsibilities
    • Research Assistants
  • Lab Resources
  • Reading List
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  • Reading List
  • Narratives and Morality
  • Computational Methods
  • Brain Imaging
  • Neuroscience of Persuasion / Narrative Processing
  • Neuroscience of Attention / Flow
  • Neuroscience of Aggression / Media Violence
  • Network Neuroscience
  • Philosophy of Science / Theory
  • Stats
  • Skills
  • Other

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Reading List

Reading List

One of the core aspects of being a productive researcher is being aware of foundational and current work in the field. Here we have included a list of some of the "greatest hits" in each of our major research areas to get you started. You can typically access them by searching the title on Google Scholar, but we also have them available in a Zotero library. Get in touch with one of the graduate students to get access to the library.

Narratives and Morality

  • Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318.

  • Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S., & Ditto, P. H. (2012). Moral foundations theory: The pragmatic validity of moral pluralism. Advances in Experimental Social Psychology, 47, 55–130.

  • Huskey, R., Bowman, N., Eden, A., Grizzard, M., Hahn, L., Lewis, R., Matthews, N., Tamborini, R., Walther, J.B., & Weber, R. 2018). Things we know about media and morality. Nature Human Behavior, 2, 315.

  • Tamborini, R. (2013). Model of intuitive morality and exemplars. In R. Tamborini (Ed.), Media and the moral mind (pp. 43–74). London, UK: Routledge.

  • Weber, R., Mangus, J. M., Huskey. R., Hopp, F. R., Amir, O. Swanson, R., Gordon, A., Khooshabeh, P., Hahn, L., Tamborini, R. (2018). Extracting latent moral information from text narratives: Relevance, challenges, and solutions. Communication Methods and Measures.

Computational Methods

  • van Atteveldt, W., & Peng, T. Q. (2018). When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science. Communication Methods and Measures, 12(2-3), 81-92.

  • Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.

  • Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations (pp. 55-60).

  • Vanderplas, J. (2016) Python data science handbook: Essential tools for working with data

Brain Imaging

  • Turner, B. O., Huskey, R., & Weber, R. (2019). Charting a future for fMRI in communication science. Communication Methods and Measures, 13(1), 1-18.

  • Weber, R., Fisher, J. T., Hopp, F. R., & Lonergan, C. (2018). Taking messages into the magnet: Method–theory synergy in communication neuroscience. Communication Monographs, 85(1), 81-102.

  • Weber, R., Mangus, J. M., & Huskey, R. (2015). Brain imaging in communication research: A practical guide to understanding and evaluating fMRI studies. Communication Methods and Measures, 9(1-2), 5-29.

  • Weber, R. (2015). Brain, mind, and media: Neuroscience meets media psychology. Journal of Media Psychology (Editorial), 27(3), 89-92.

  • Weber, R., Eden, A., Huskey, R., Mangus, J. M., & Falk, E. (2015). Bridging media psychology and cognitive neuroscience: Challenges and opportunities. Journal of Media Psychology, 27(3), 146-156.

Neuroscience of Persuasion / Narrative Processing

  • Berkman, E. T., & Falk, E. B. (2013). Beyond brain mapping: Using neural measures to predict real-world outcomes. Current Directions in Psychological science, 22(1), 45-50.

  • Falk, E. B., Cascio, C. N., & Coronel, J. C. (2015). Neural prediction of communication-relevant outcomes. Communication Methods and Measures, 9(1–2), 30–54.

  • Gabrieli, J. D. E., Ghosh, S. S., & Whitfield-Gabrieli, S. (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron, 85(1), 11–26.

  • Weber, R., Huskey, R., Mangus, J. M., Westcott-Baker, A., & Turner, B. (2015). Neural predictors of message effectiveness during counterarguing in antidrug campaigns. Communication Monographs, 82(1), 4-30.

Neuroscience of Attention / Flow

  • Fisher, J. T., Huskey, R., Keene, J. R., & Weber, R. (2018). The Limited Capacity Model of Motivated Mediated Message Processing: Looking to the Future. Annals of the International Communication Association, 42(4), 291-315.

  • Fisher, J. T., Keene, J. R., Huskey, R., & Weber, R. (2018). The Limited Capacity Model of Motivated Mediated Message Processing: Taking stock of the past. Annals of the International Communication Association, 42(4), 270-290.

  • Huskey, R., Craighead, B., Miller, M. B., & Weber, R. (2018). Does intrinsic reward motivate cognitive control? A naturalistic-fMRI study based on the synchronization theory of flow. Cognitive, Affective, & Behavioral Neuroscience, 18(5), 902–924.

  • Weber, R., Tamborini, R., Westcott-Baker, A., & Kantor, B. (2009). Theorizing flow and media enjoyment as cognitive synchronization of attentional and reward networks. Communication Theory, 19(4), 397-422.

  • Weber, R., Alicea, B., Huskey, R., & Mathiak, K. (2018). Network dynamics of attention during a naturalistic behavioral paradigm. Frontiers in Human Neuroscience, 12.

Neuroscience of Aggression / Media Violence

  • Klasen, M., Wolf, D., Eisner, P. D., Eggermann, T., Zerres, K., Zepf, F. D., Weber, R., Mathiak, K. (2019). Serotonergic contributions to human brain aggression networks. Frontiers in Neuroscience, 13(42).

  • Mathiak, K., & Weber, R. (2006). Toward brain correlates of natural behavior: fMRI during violent video games. Human Brain Mapping, 27(12), 948-956.

  • Weber, R., Ritterfeld, U., & Mathiak, K. (2006). Does playing violent video games induce aggression? Empirical evidence of a functional magnetic resonance imaging study. Media Psychology, 8(1), 39-60.

Network Neuroscience

  • Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209.

  • Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364.

  • Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290.

  • Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27(8), 1471–1491.

  • Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21(5), 357–371.

  • Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67(1), 613–640.

Philosophy of Science / Theory

  • Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time.

  • Gigerenzer, G., & Marewski, J. N. (2014). Surrogate Science: The Idol of a Universal Method for Scientific Inference . Journal of Management, 41(2), 421–440.

  • Greenwald, A. G. (2012). There is nothing so theoretical as a good method. Perspectives on Psychological Science, 7(2), 99-108.

  • Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017). Neuroscience needs behavior: correcting a reductionist bias. Neuron, 93(3), 480–490.

  • Meehl, P. (1967). Theory testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.

  • Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 1.

  • Watts, D. J. (2017). Should social science be more solution-oriented?. Nature Human Behaviour, 1(1), 0015.

  • Weber, R., Sherry, J., & Mathiak, K. (2008). The neurophysiological perspective in mass communication research. Theoretical rationale, methods, and applications. In M. J. Beatty, J. C. McCroskey & K. Floyd (Eds.), Biological dimensions of communication: Perspectives, methods, and research (pp. 41-71). Cresskill, NJ: Hampton Press.

  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122.

Stats

  • Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6), 460–465.

  • Levine, T. R., Weber, R., Park, H., & Hullett, C. (2008). A communication researcher’s guide to null hypothesis significance testing and alternatives. Human Communication Research, 34, 188-209.

  • Levine, T. R., Weber, R., Hullett, C., Park, H., & Massi-Lindsey, L. (2008). A critical assessment of null hypothesis significance testing in quantitative communication research. Human Communication Research, 34, 171-187.

  • Weber, R., & Popova, L. (2012). Testing equivalence in communication research: Theory and applications. Communication Methods and Measures, 6(3),190-213.

Skills

  • Blischak, J. D., Davenport, E. R., & Wilson, G. (2016). A quick introduction to version control with Git and GitHub. PLOS Computational Biology, 12(1).

  • Mensh, B., & Kording, K. (2017). Ten simple rules for structuring papers. PLOS Computational Biology, 13(9), 1–9.

Other

  • Rains, S. A., Levine, T. R., & Weber, R. (2018). Sixty years of quantitative communication research summarized: Lessons from 149 meta-analyses. Annals of the International Communication Association. 42(2), 105-124.

PreviousLab Resources

Last updated 5 years ago

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