There's a well-documented phenomenon in consumer and user research that most practitioners know intuitively but rarely name directly: people don't report their experience accurately.
Not because they're dishonest. Because self-reporting is hard. In the moment of an interview, participants are performing a version of themselves. They round off hesitation. They describe their behaviour more charitably than it actually was. They say "yes, I'd probably use this" when what they felt was closer to "maybe, under the right circumstances, if the price were different." The social pressure of being in a conversation, even with an AI, shapes what gets said.
This is what we call the Say-Do Gap. The distance between what someone tells you and what they actually feel or do.
It shows up everywhere. In concept tests where participants say they love a product they'd never buy. In usability sessions where someone says "this makes sense" while visibly struggling. In brand perception studies where stated attitudes don't match purchasing behaviour.
How does the facial coding and emotion AI actually perform across different cultures since expressions and emotional cues vary so widely globally?
Mira
@zgreg0z Our models are trained across demographic groups rather than on a single population, and we apply normalisation based on cultural benchmarks we have collected to reduce bias in emotional scoring. The goal is to measure genuine reactions, not to apply one cultural standard globally. We also let researchers set study-specific calibration. Emotion expression does vary across cultures and we do not claim perfect universality — it is an ongoing area of work. For global studies we recommend always combining the emotional signal with the transcript and not reading it in isolation.
Mira
Product Manger at Decode here
One thing that always fascinated me about qualitative research: researchers don't just analyze what people say. They spend hours replaying interviews to understand how they said it.
A micro-expression of disgust at the pricing slide, a long pause before "I'd probably use it," hands that stopped moving the moment they said they were "comfortable."
That's the invisible layer of qual research. The part that turns a quote into an actual insight.
Mira is built around that gap, it conducts interviews in 75+ languages, probes emotionally in real time, and simultaneously captures vocal hesitation, facial emotion, and where attention went. So researchers stop losing those moments to memory and manual replay.
The goal isn't to replace researchers.
It's to give them superpowers, so they can spend more time discovering why people behave the way they do, instead of manually reviewing hours of recordings.
Happy to answer any questions about how any of this works.
How does the facial coding and eye tracking actually work in practice — is it through a browser plugin, a mobile app, or do participants need special hardware?
Mira
@nkurumak28108 No plugin, no app, no special hardware. Everything runs in the browser; participants just grant access to the webcam and microphone when they join. There is a quick calibration check before the session starts to make sure lighting and camera positioning are good. Works on any standard laptop or desktop webcam.
The facial coding during interviews actually caught a reaction I would have completely missed reviewing the transcript alone. Seeing emotion data layered with what people said felt like a real research upgrade, not just another AI wrapper.
Mira
@memetjr2q Thank you for putting it through a real test. That gap between what the transcript says and what the face shows is exactly what Mira is built to catch, most tools only ever see the words.
How does the facial coding and eye tracking piece actually work in practice, do participants need to opt in and run any special setup on their end?
Mira
@ezelsukut Everything runs in the browser, no downloads or setup. Participants grant camera access, run a short calibration (only for eye tracking), and the session starts on a standard laptop or phone with in-built cam. Fully consent driven.
The facial coding and emotion layer actually feels different from typical survey tools — I ran a quick concept test and the sentiment data picked up nuances I usually miss in write-ups.
Mira
@gllfevp Thank you for testing it on a real concept. The hesitation read is the whole point. People rarely say "I'm unsure" out loud, but the face shows it, and that gap between what's said and what's felt is exactly what Decode is built to surface
How does the facial coding and eye tracking actually work in practice with participants who are on their phones or in different lighting conditions? Curious how reliable that data really is across such a massive global panel.
Mira
@miratngl8j5s Great question Miraç.
A few specifics on how we handle this:
Phones: Mira currently runs in-browser on desktop, participants join via a standard webcam, not a mobile camera. We found controlled desktop sessions produce more reliable facial signal than mobile, where camera angle and movement vary too much. For global panel recruitment, participants are briefed on the setup requirement before joining.
Lighting: Before every session, participants go through a quick calibration check, the system guides them to adjust their face position and lighting until confidence scores are acceptable. Frames that fall below our threshold are automatically excluded from the emotional signal rather than being interpolated. We never guess on weak data.
Global reliability: Our models are trained across demographic groups and we apply cultural normalisation benchmarks to reduce bias in emotional scoring. The signal is most reliable when used comparatively, patterns across a study group, rather than reading single participant moments in isolation.
Honest caveat: webcam-based tracking is less precise than lab infrared. For questions like where attention went first or whether someone showed hesitation at a specific moment, it works well. For pixel-level gaze accuracy, a lab setup still wins.
Happy to go deeper on any of this.