In a warehouse, there are boxes of apples and pears. There are many apples and few pears. But when the stock clerk says "a fruit with smooth skin," we are somehow more likely to recall the rare pear than the common apple. This is the inverse base-rate effect — a persistent glitch in how the brain resolves ambiguity between categories.

For a long time, it was thought that prediction error was to blame: the brain makes a mistake, receives a mismatch signal, and recalibrates expectations. But the authors of this study went further: they removed the very possibility of such an error from their experiments. First, they switched to observational learning (participants simply watched without making predictions), and then they removed category labels altogether. The effect did not disappear.

Thus the OSCAR model was born. It operates on self-generated feedback — something like internal pattern completion. Upon seeing partial features, the system fills in the whole and learns from this internal response. The model not only replicated results on old data but also predicted individual differences among people and, for the first time, explained previously puzzling eye movements recorded in experiments.