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AI heatmaps vs eye tracking: which question can each answer?

AI predicts visual saliency from pixels. Eye tracking measures gaze from participants. Choose between them by the decision you need to make, not by the colour of the map.

By the Heatpoints team · Updated 9 September 2026

Try the image heatmap

Prediction and measurement are different evidence

Heatpoints uses UNISAL to estimate a spatial attention distribution from an image. It produces a repeatable review input without recruiting participants for that analysis. It does not tell you where a particular person looked.

An eye-tracking study measures gaze while participants carry out a task or view a stimulus. Its conclusions depend on recruitment, calibration, study design and analysis. A measured gaze path can contain temporal information that a static saliency image does not provide.

References: UNISAL paper and implementation

Use prediction when you need a design hypothesis

Early layout reviews often concern visible competition: an oversized image, a weak headline or several equally prominent actions. A predicted map can help a team discuss that composition while the design is still easy to change.

Keep variants comparable and avoid converting a score difference into a performance forecast. The model does not know whether a visitor recognises your brand, is searching for a price or has already decided what to buy.

Use participants when the task changes the question

If you need to know how people search a dashboard, interpret a warning or navigate an unfamiliar flow, recruit relevant users and observe the task. Depending on the question, usability testing may be enough; eye tracking is an additional measurement method, not a requirement for every design decision.

For example, a visually subtle price may be found quickly by a participant explicitly asked to locate the price. A saliency model applied without that task cannot represent that person's motivation.

Do not compare accuracy badges out of context

Academic saliency evaluation uses metrics such as NSS and AUC on defined datasets. A vendor's percentage may refer to a different metric, dataset or study protocol. Ask what was measured and whether the evaluation resembles your intended use.

Heatpoints does not publish a universal accuracy percentage or claim equivalence to an eye-tracking study. Its current processing and the difference between SALICON mouse-derived data and MIT1003 eye-tracking weights are described on the science page.

References: Heatpoints methodology