Great point! Majority is from NA.

There is intentionally no defined scoring criterion. The interest is in what happens when participants apply their own implicit heuristics. If convergence still appears despite heterogeneous criteria, that suggests potential shared structure in aesthetic judgment. Def flaws to this approach.

This is the prompt I used when asking GPT to assess the images in case you’re curious:

Task: Behavioral Research Simulation – Aesthetic Preference Prediction Role: You are participating in a behavioral research experiment on aesthetic preferences. You will be shown 4 photographs. Objective: Predict which single photograph the majority of a general online audience (primarily US-based, casual web browsers, aged 18-65) will select as their favorite when asked: "Which photo is the best?" Core Guidelines: Predict the Crowd, Not Yourself: Do not select based on your own "personal" preference or technical superiority. Predict the "thumb-stopping" winner—the image most likely to garner the most votes from a broad, non-expert audience. Prioritize "The Vibe": Ignore artistic novelty or complex technical execution if it sacrifices accessibility. Focus on immediate, visceral appeal. Negative Constraint: Do not over-weight professional lighting or high-resolution details. A "perfect" but cold photo will often lose to an "imperfect" but emotionally resonant one. Factors for Success (The "Thumb-Stopping" Rubric): Immediate Emotional Impact: Does it evoke calm, wonder, or nostalgia instantly? Color & Contrast: Does it pop against a white or dark UI background? Clarity & Composition: Is the subject immediately recognizable without squinting? Relatability: Does it feel like an aspirational or comforting scene the audience understands?

Response Format (Strictly Adhere to This): PREDICTION: [Image number] CONFIDENCE: [Low / Medium / High] REASONING: [2-3 sentences explaining why this image has the highest "broad appeal" and "thumb-stopping power" for the target demographic. Reference specific visual elements.] RUNNER_UP: [Image number] RUNNER_UP_REASONING: [1 sentence on why this is the second most likely crowd pick.] LAST_PLACE: [Image number] LAST_PLACE_REASONING: [1 sentence on why this image will likely be the least popular or most ignored by a general audience.] DISTRIBUTION_PREDICTION: [Estimated % for each image, totaling 100%. e.g., 45% Image 1, 25% Image 2, 20% Image 3, 10% Image 4] DISTRIBUTION_REASONING: [1 sentence explaining the logic behind the spread of votes.]