Methodology spotlight: separating curiosity from understanding from liking

Ad pre-testing usually ends with a likability score and some recall questions. The scores are easy to read and easy to defend — but they collapse a complex mental process ("I noticed it… I'm trying to figure it out… I get it now… I like it") into a single number measured at the wrong moment.

Recent peer-reviewed work — run on the platform that powers Pagegazer — demonstrates that the underlying experience can be pulled apart, in real time, using readily available signals: gaze, pupillometry, heart rate, and continuous response.

Curiosity, insight, understanding, and liking are different states

Welke and Vessel (2025) tracked viewers continuously during interactions with visual art and identified distinct physiological signatures for four stages of an aesthetic encounter: curiosity (the moment of approach), insight (the moment of "getting it"), understanding (sustained comprehension), and liking (evaluative response). Heart-rate dynamics, pupil dilation, and gaze patterns each contributed differently to identifying which stage the viewer was in at any given moment.

In a commercial creative context — a TV spot, a social video, a long-form ad — these stages happen in seconds, often in the same order. A viewer can be highly curious about an ad, never reach insight, and rate it as "fine" at the end. The likability score reads as flat. The physiological time-series shows exactly where the experience broke down.

Music and visuals don't combine the way intuition suggests

Fink, Fiehn, and Wald-Fuhrmann (2024), in Scientific Reports, measured what happens when music is paired with visual art — congruent versus incongruent matches. The aesthetic effect of audiovisual pairing was not a simple sum of the two channels' separate effects. Match quality changed how the visuals were processed, including where attention went and how engaged viewers were.

For ad and video research, this finding maps directly: the music bed, voiceover tone, and sound design are not background; they actively shape how the visuals are received. A re-edit that swaps the audio while leaving the cut intact can change attention patterns measurably.

Voice carries more than people realise

Two related studies extend the finding into voice. Bruder, Frieler, and Larrouy-Maestri (2024), in Royal Society Open Science, showed that appreciation of singing and speaking voices is highly idiosyncratic — a much smaller share of voice preference is shared across listeners than industry assumptions suggest. Bruder, Breda, and Larrouy-Maestri (2025) extended the work to synthetic voices in Computers in Human Behavior: Artificial Humans, with implications for any brand using AI-generated narration.

For a marketer, the practical takeaway is that "we tested the voice and people liked it" is a weaker claim than it sounds. Voice preference has a large individual-difference component; segment-level analysis is required, and a voice that wins on average can lose decisively in important sub-populations.

Attention to the brand reveal — at PNAS-level rigour

A recent study at the highest tier of the literature illustrates how robustly these methods can resolve subtle effects. Canessa-Pollard, Anikin, and Reby (2025), in Proceedings of the National Academy of Sciences, identified shared acoustic features across chant traditions from seven cultures that consistently produce subjective relaxation. The methodology — controlled stimulus presentation, continuous physiological measurement, and structured response collection in the participant's own browser — is the same instrumentation used to test whether an ad's audio bed lands the intended emotional effect.

What this means for ad and video research

The methodology behind these studies — eye tracking, heart-rate measurement (via webcam rPPG), facial-expression analysis, all in the participant's own browser — is what Pagegazer runs for ad pre-testing. The questions it answers, in commercial terms:

Where in the cut does attention drop, and is the brand reveal inside or outside the high-attention window?
Does the music edit support or fight the intended emotional arc?
Are viewers reaching "understanding" before the call-to-action, or is the spot landing the message after they have disengaged?
Does a voice-over choice work across audience segments, or only in some?

A likability score is a single readout. Continuous physiology and attention provide the timeline.

Citations

Welke, D., & Vessel, E. A. (2025). Tracing the Epistemic Arc: Distinct Physiological Signatures for Curiosity, Insight, Understanding and Liking during Interactions with Visual Art. bioRxiv. doi.org/10.1101/2025.05.15.654230
Fink, L., Fiehn, H., & Wald-Fuhrmann, M. (2024). The role of audiovisual congruence in perception and aesthetic appreciation of contemporary music and visual art. Scientific Reports. doi.org/10.1038/s41598-024-71399-y
Bruder, C., Frieler, K., & Larrouy-Maestri, P. (2024). Appreciation of singing and speaking voices is highly idiosyncratic. Royal Society Open Science. doi.org/10.1098/rsos.241623
Bruder, C., Breda, P., & Larrouy-Maestri, P. (2025). Attractive synthetic voices. Computers in Human Behavior: Artificial Humans. doi.org/10.1016/j.chbah.2025.100211
Canessa-Pollard, V., Anikin, A., & Reby, D. (2025). Chants across seven traditions share acoustic traits that enhance subjective relaxation. Proceedings of the National Academy of Sciences. doi.org/10.1073/pnas.2506480122

For the full list of peer-reviewed work using the Pagegazer measurement platform, see published research.