This feels like it’s similar to the priming issue in humans. Our answers (especially when under stress) tend to resort to heuristics derived from context. Time someone to identify the colors of words like “red” when written in yellow, and they’ll often get it wrong. In the same sense, they aren’t reporting the colors (wavelength) they see, they’re reporting on what they are reading. I wonder how much better the models perform when given more context, like asking it to count instead of priming it with a brand.