It's an interesting point. A few quick reactions -
1. I don't know how to test that hypothesis (and indeed most neuroevolutionary "conjectures"). A mutation is one thing, but linked to very specific aspects of cognition is something else. For instance, how can we run a controlled study while leaving evolution free to vary? Some animal models may help, eventually, with certain aspects of knowledge (e.g., tools), but abstract thought is a whole other beast.
2. We've done some preliminary developmental work, and it seems with a specifically targeted instruction we can radically improve performance in young kids, and quickly too. So one answer might be to start early and never let kids (or users) go too far afield. The best way seems to be to show kids that only biological things move toward goals (e.g., you go to the sink when thirsty, roots grow towards moisture). But we don't know if that learning sticks or if they fall back into old patterns.
3. An alternative approach is to test a population that may not see animacy in the same way early in development and so may not exhibit the same types of traces later on. One idea is to follow up with autistic kids. To me, that's the difference between evolutionary accounts and a testable hypothesis about neurodevelopment. If the results show less potent traces, then we can use brain imaging to examine how early differences in the neuroanatomy lead to different processing streams.
4. If I were to agree with anything in that direction, an evolutionary foundation may reflect a static versus dynamic division (rather than living or non-living) of the visual system. Problem is, to understand biological mechanisms, static things (e.g., plants) have to be seen as dynamic, and dynamic things have to be seen as illusory (e.g., cloud). So irrespective of the true first cause, learning involves overcoming biases that initially "appear" to be powerfully important.
I read about a study where they found that people noticed moving animals faster than equally large moving cars. (I could look up the source if anyone is interested.) For your point 3: As far as I recall this effect did not seem to rely on whether those people grew up with animals (African subjects) or not (American subjects).
Perhaps if you would find some other task for your volunteers to test you learn/unlearn hypothesis with. Some task that we do not suspect of being hard wired into our brain. But a task people would nevertheless encounter at an early age.
Anyway - thanks for reading through my amateurish ideas.
1. I don't know how to test that hypothesis (and indeed most neuroevolutionary "conjectures"). A mutation is one thing, but linked to very specific aspects of cognition is something else. For instance, how can we run a controlled study while leaving evolution free to vary? Some animal models may help, eventually, with certain aspects of knowledge (e.g., tools), but abstract thought is a whole other beast.
2. We've done some preliminary developmental work, and it seems with a specifically targeted instruction we can radically improve performance in young kids, and quickly too. So one answer might be to start early and never let kids (or users) go too far afield. The best way seems to be to show kids that only biological things move toward goals (e.g., you go to the sink when thirsty, roots grow towards moisture). But we don't know if that learning sticks or if they fall back into old patterns.
3. An alternative approach is to test a population that may not see animacy in the same way early in development and so may not exhibit the same types of traces later on. One idea is to follow up with autistic kids. To me, that's the difference between evolutionary accounts and a testable hypothesis about neurodevelopment. If the results show less potent traces, then we can use brain imaging to examine how early differences in the neuroanatomy lead to different processing streams.
4. If I were to agree with anything in that direction, an evolutionary foundation may reflect a static versus dynamic division (rather than living or non-living) of the visual system. Problem is, to understand biological mechanisms, static things (e.g., plants) have to be seen as dynamic, and dynamic things have to be seen as illusory (e.g., cloud). So irrespective of the true first cause, learning involves overcoming biases that initially "appear" to be powerfully important.