My research focuses on how media content influence human cognition and behavior. Philosophically, this work understands the mind (and the communication phenomena it enables) as a physical property of the brain. From this multi-level view, cognitive neuroscience complements media psychology by providing an additional level of explanation for communication behavior (e.g., sociocultural, individual, biological, chemical, physical). It is from this perspective that my research investigates three core topics: attitude and behavior change, motivation and cognitive control, and the influence of moral narratives. I also develop open-source tools for conducting naturalistic investigations into human behavior. My vita can be downloaded here.
Asteroid Impact is a point-and-click style video game where subjects use a cursor to collect crystal-shaped targets that are displayed at different locations while avoiding asteroids that bounce around the screen. All aspects of the game can be manipulated with a high level of experimental control. The stimulus records all game events with 16ms temporal resolution, which are then output as a .csv file. Asteroid Impact is written in Python, is platform agnostic, and can synchronize with TTL triggers. The game features an open source license (CC BY-SA 4.0), and is freely available to the research community. Those wishing to download or further develop Asteroid Impact or download experimental releases can do so on Github.
Content features of persuasive messages modulate neural activity and this variation can be used to identify messages that are most likely to result in attitudinal and behavioral change. Informed by the Elaboration Likelihood Model of persuasion, my research applies a “brain-as-predictor” approach to understand how audiences evaluate counter-attitudinal messages. In one study, subjects that were either at high- or low-risk for drug use were exposed to anti-drug public service announcements (PSAs) while undergoing functional magnetic resonance imaging. Results show that neural activity in brain regions associated with executive and self-referential processing accurately predict perceptions of message effectiveness in independent samples. Moreover, this prediction accuracy exceeds that achieved by traditional self-report measures. Previous efforts have struggled to identify messages that resonate with high-risk audiences as these viewers tend to rate anti-drug PSAs as having low effectiveness regardless of variation in the message. My work demonstrates that it is possible to use neuroimaging data to overcome this obstacle.
My current research builds on these findings in two ways. First, I am exploring how message features contribute to dynamic changes in neural network connectivity and the extent to which these connectivity patterns predict persuasive message effectiveness. Secondly, given that message tailoring is associated with increases in persuasion, I am examining how individual differences explain variability in neural activity while viewing anti-drug PSAs with the goal of better identifying the relevant dimensions along which messages should be tailored.
For a three-minute summary of this work, check out my recent talk: Using Brains to Change Minds.
Media Use and Flow Experiences
My work in this area examines how interactive media content modulate brain states that correspond to distinct affective states. Specifically, it investigates how the psychological state of flow results from an interaction between task difficulty and individual differences in skill. This research uses behavioral, self-report, and functional magnetic resonance imaging measures to test the central predictions of the Synchronization Theory of Flow. My results demonstrate that attentional allocation and self-reported flow experiences increase under conditions of a balance (but not imbalance) between task challenge and individual skill. This balance synchronizes attentional and reward networks and indicates that flow experiences are comprised of distinct brain states that differ from those observed during other affective experiences. By identifying neural markers of flow, my research provides a foundation for examining long-theorized but largely untested relationships between individual differences, moment-by-moment changes in media content, and flow experiences.
While flow is typically understood as an optimal experience that contributes to life satisfaction, some have suggested that it is an antecedent to media addiction. My current research is focused on distinguishing flow from addiction. Initial results indicate that flow and media addiction are comprised of distinct brain states that correspond with different psychological and behavioral outcomes.
Narratives and Morality
In collaboration with researchers at the Institute for Collaborative Biotechnologies at the University of California Santa Barbara, the Institute for Collaborative Technologies at the University of Southern California, and the Army Research Lab, I am examining the influence of moral content in online narratives. This project has three main goals: (a) dynamically measure morally-laden online communication content, (b) demonstrate that these underlying moral values affect attitudes and motivate behavior, and (c) make predictions about the influence of moral narratives for specific audiences at the population level. The first step in this process resulted in the Moral Narrative Analyzer (MoNA), an online tool that allows human coders to content analyze news articles. These human-coded data are then used to tune a semi-supervised classification algorithm that automatically content analyzes remaining documents. Early results demonstrate that social sharing (e.g., Facebook, Twitter) increases as moral conflict within an article increases.
Currently, we are examining the extent to which the inter-rater agreement of our human coders can be used as a measure of how clearly a given narrative expresses a shared morally-salient point of view within a group of individuals. Subsequent research will use functional magnetic resonance imaging to evaluate the extent to which moral content in narratives synchronizes neural activity among different groups. These brain-imaging data will then be used to predict behavioral outcomes in the broader population.