Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment
Pretrained vision embeddings are widely used to model how people appraise urban scenes, and they are validated almost entirely by how well they predict human ratings. Accurate prediction shows that an embedding contains the information needed to recover the ratings. It does not show that the embedding arranges scenes as the human visual system does, which is assumed when distances or dimensions in the embedding are read as perceptual. Using openly released EEG recorded while 63 adults viewed and rated street scenes of Berlin, we compared the neural representational geometry of the scenes with the geometry of pretrained vision models that differ in training objective and size, of simple im...
| Authors | Kaizhen Tan, Yuantao Deng |
|---|---|
| Primary category | cs.CV |
| Published | 31 August 2026 |
| Last updated at source | 4 October 2026 |
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