In their 2025 study published in Nature - Scientific Reports, Maack, Ostrowski, & Rose explore how the human brain encodes and retrieves the sequence order of multisensory information. Specifically, how the order of auditory and visual stimuli affects memory recognition.

When companies bring me in to review or teach neuro-based research methods, I can usually predict where the conversation will go. We’ll talk about EEG. GSR. Maybe eye tracking. Often the Implicit Association Test (IAT). There’s usually excitement about the potential of these tools. And rightly so, they can provide valuable insights when used well.

But there's one message I find myself repeating in every session, like a drum I can’t stop beating: humans are complicated.

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.

At its core, framing taps into how our brains process information. Humans are not entirely rational decision-makers. Instead, we’re influenced by cognitive biases, which shape how we interpret and respond to information.

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.

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.