Human Hallucination Prediction

Predict what visual hallucinations humans may experience using neural networks.

How to predict hallucinations:

1. Select an example image below and click "Load Parameters" to set the prediction settings
2. Click "Predict Hallucinations" to predict what hallucination humans may perceive
3. View the prediction: Watch as the model reveals the perceptual structures it expects—matching what humans typically hallucinate
4. You can upload your own images
5. You can download the results as a .gif file together with the configs.json

Examples

Select an example and click Load Parameters to apply its settings

🧠 About Hallucination Prediction

This tool predicts human visual hallucinations using generative inference with adversarially robust neural networks. Robust models develop human-like perceptual biases, allowing them to forecast what perceptual structures humans will experience.

Prediction Methods:

Prior-Guided Drift Diffusion (Primary Method)
Starting from a noisy representation, the model converges toward what it expects to perceive—revealing predicted hallucinations

IncreaseConfidence
Moving away from unlikely interpretations to reveal the most probable perceptual experience

Parameters:

• Drift Noise: Initial uncertainty in the prediction process
• Diffusion Noise: Stochastic exploration during prediction
• Update Rate: Speed of convergence to the predicted hallucination
• Number of Iterations: How many prediction steps to perform
• Model Layer: Which perceptual level to predict from (early edges vs. high-level objects)
• Epsilon (Stimulus Fidelity): How closely the prediction must match the input stimulus

Why Does This Work?

Adversarially robust neural networks develop perceptual representations similar to human vision. When we use generative inference to reveal what these networks "expect" to see, it matches what humans hallucinate in ambiguous images—allowing us to predict human perception.

Developed by Tahereh Toosi