Central tenets to my research approach

The human brain is the biological mediator between a message and its effects.

Interdisciplinary pioneers have opened the doors for the integration of neuroscientific methods into communication science (Falk et al., 2015; Schmälzle et al., 2015; Weber et al., 2008). Communication neuroscience approaches allow us to unlock the “black box” of the brain as a critical component of the human information processing system.

Time is an essential variable to studying the message reception process.

Stories evolve through exposition, rising action, climax, falling action, and resolution- and so too do our responses! Although we can summarize our thoughts at the end of a film, this does not capture how the story made us laugh, cry, or jump right out of our seats. In order to do that, we need to measure responses over time.

Message content matters.

Just as it is essential to study audience biology to understand how we respond to messages, it is just as important to study the messages that drive those responses. Developments in computer science and computational methods give us a clear path forward to develop the content analytic methods that are ingrained in the field of communication. Already, we can leverage new tools to efficiently and reliably assess the complexities of lower-order narrative content from objects on screen to the music that moves us (Grall & Schmälzle, 2018).

Messages that represent what we see in our daily lives matter.

I want to study the communication process from message to audience in a way that reflects what happens in our everyday life. I strive for external validity, and new developments in methods and analyses across computer science and neuroscience make this an increasingly achievable goal (Hasson et al., 2004; McNamara et al., 2017).

Science makes progress through transparency.

I am a strong advocate of open science, and I have adopted several practices to maximize the replicability and reproducibility (and therefore quality) of my work. All current projects have open data and code in development on Github with the aid of Jupyter Notebooks, and many have accompanying pre-registrations. I am committed to upholding these practices as an essential part of the research process.