At this year’s AChemS meeting, I gave a talk on AI in sensory research. One of the demonstrations I gave was intentionally provocative: I asked ChatGPT to simulate a consumer research study using MaxDiff and Implicit Association Testing (IAT) to explore the perception of "freshness" in home fragrance products.

Let me be clear: the goal was not to fake data but to stress-test a study design. By simulating how people might respond, I wanted to explore gaps, assumptions, and how well our methods differentiated between products. Consider it a pilot study by proxy.

I've been asked by at least five different people this week for book recommendations to learn neuroscience, especially from those working in marketing, UX, product development, and consumer research. And I totally get it, the brain IS fascinating. The idea of tapping into real reactions, getting “under the hood” of decision-making, “reading minds”, and predicting behavior is understandably appealing.

Implicit methods hold immense potential for consumer research, but only when applied thoughtfully. As the field continues to evolve, we must push back against oversimplified approaches and prioritize rigor in experimental design and data analysis. Let’s ensure implicit testing serves as a tool for uncovering genuine insights—not just a buzzword for selling research services.

We dive into how social proof—people’s natural tendency to follow the crowd—has taken center stage in today’s digital world. From viral TikTok reviews to influencer-approved skincare, learn why authentic user-generated content now outshines traditional ads, and discover how product developers can tap into memetic trends to build trust and grow their brands.

When neuroscience intersects with consumer research, it promises an enticing frontier: uncovering what truly drives human behavior, often at a subconscious level. Yet as appealing as it sounds, this approach can run into a significant methodological pitfall—reverse inference.