If a self driving car sees a bicycle on the freeway, and it is not able to infer that the bicycle may be painted on a truck, it will be restricted to a recognition policy that is constrained by the probability of bicycles on freeways vs reliability of its object recognition.
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I am no expert in cognitive sciences but I know, from CS, top-down is usually slower, hard-to-parallelize, etc. That's bad in terms of evolution. I think it's sequential computation (of low-level pattern detectors) mistaken by top-down computation.
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Perception is mostly predictive, i.e. based on complex hierarchical hypotheses about what we will perceive.
End of conversation
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