Cognitive and affective responses to marijuana prevention and educational messaging

Glenn Leshner

Elise M. Stevens

Amy M. Cohn

Seunghyun Kim

Narae Kim

Theodore L. Wagener

Andrea C. Villanti

Perceptions of risk of using marijuana have decreased significantly in the US over the last decade, while marijuana use has increased. In order to educate people on the risks associated with marijuana use, large-scale health messaging campaigns have been deployed to educate the public about the risks associated with marijuana use, particularly in states where medical or recreational marijuana is legal. Few studies have examined how messages about marijuana affect the audiences’ cognitive and emotional responsivity to these messages.

To address this knowledge gap, this study used psychophysiological assessment (heart rate, skin conductance, facial action coding) and self-report measures to explore the impact of different marijuana risk messages on real-time cognitive and affective responses and self-reported message receptivity, likeability, and intentions to use marijuana in a sample of 50 young adult marijuana users and non-users. Each participant saw six messages. Three messages were used from each of two campaigns, representing one of three risks (cognitive ability, driving, health harms).

Psychophysiological responses showed that the driving-themed messages for both campaigns had the greatest cognitive resource allocation to encoding the message, the greatest arousal, and the most positive emotional response, regardless of user status. Self-reports showed a less consistent pattern.

Overall, psychophysiological measures provided a more consistent picture of message processing and effects than self-report measures. Findings from this study provide immediately useful data for improving the development and effectiveness of marijuana health-risk prevention campaigns by elucidating cognitive and emotional processes that could be targeted in future programs.

This publication uses ECG, Facial Expression Analysis and GSR which is fully integrated into iMotions Lab

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