When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits

Klepacz, N.A., Nash, R.A., Egan, M. Bernadette, Hodgkins, Charo E. and Raats, Monique M. (2016) When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits. Health Psychology, 35 (8), 898–907. (doi:10.1037/hea0000317).

Abstract

Objective: Images on food and dietary supplement packaging might lead people to infer (appropriately or inappropriately) certain health benefits of those products. Research on this issue largely involves direct questions, which could (a) elicit inferences that would not be made unprompted, and (b) fail to capture inferences made implicitly. Using a novel memory-based method, in the present research, we explored whether packaging imagery elicits health inferences without prompting, and the extent to which these inferences are made implicitly. Method: In 3 experiments, participants saw fictional product packages accompanied by written claims. Some packages contained an image that implied a health-related function (e.g., a brain), and some contained no image. Participants studied these packages and claims, and subsequently their memory for seen and unseen claims were tested. Results: When a health image was featured on a package, participants often subsequently recognized health claims that—despite being implied by the image—were not truly presented. In Experiment 2, these recognition errors persisted despite an explicit warning against treating the images as informative. In Experiment 3, these findings were replicated in a large consumer sample from 5 European countries, and with a cued-recall test. Conclusion: These findings confirm that images can act as health claims, by leading people to infer health benefits without prompting. These inferences appear often to be implicit, and could therefore be highly pervasive. The data underscore the importance of regulating imagery on product packaging; memory-based methods represent innovative ways to measure how leading (or misleading) specific images can be.

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Contributors

Author: N.A. Klepacz

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