I think us commenters are all on the same page here :)
The author is almost making it seem like models are reality and that people think that. They're not and I don't think anyone has ever thought they were...
Further and like other comments already mentioned, the brain is thought of and treated as a turing machine, not a digital computer. It's done this way, because the brain can be mapped to the definition of a turing machine.
And I have to defend Von Neumann. In his book, he explored turing equivalencies between the brain and computer concepts at the time used to implement the digital turing machine, he didn't actually think that the brain was a one-to-one mapping to a digital computer... He knew the difference between models and reality.
Even for the history of models the author mentions (hydraulics, automata, etc.), these all contain some turing equivalencies if implemented correctly and they were simply using the language and examples at the time to express this.
The author also continues to mangle any and all ideas of modeling, abstraction, and equivalence throughout the whole article. With regard to his 'uniqueness problem', I mean 'information loss' is modeled digitally for a reason.. just because humans are lossy, doesn't mean we can't model them that way. Think of a compressed image file.
I don't think there's a single researcher worth their salt that thinks the 'IP Metaphor' is gospel. That is just a grossly unscientific idea to assume.
We're all free to choose any model or collection of models we wish to approximate reality, but some of them work better than others and the brain is a complicated thing to model.
One mating season is monogamous? Tell my wife that.
Also, this seems to make no mention of 'rearing complexity'. A more developmentally complex brain should imply prolonged rearing and have a bias of survival in favor of shared parenting.
I guess you can find anything you're looking for in gene expressions, if you really want.
It sounds like you need to just slow down when you're working/coding. I assume your handwriting is somewhat sloppy too? If this is the case, then start focusing on specificity of everything you do (and adding a review step to your workflow). For example, you should have said 'too many mistakes' not 'to much mistakes'. Focus on specificity and precision in your writing and thoughts (i.e. each word, each phrase, each comma), then it will follow in your code and maths procedures. TL,DR: slow down to go fast.
Good points, and I am not advocating sweeping lies in every instance. There's also the assumption that lies always take on a negative form, there are many positive forms of lies. The most obvious example being white lies in conversation to not insult people, when the truth can only be interpreted as insulting (the 'Do I Look Fat In This?' Dilemma).
And then there is also the consideration of the lies lifespan. If the effect of a lie is momentary, its residual effects could only last mere moments, instead of continuously into perpetuity (the ripples of water from a rock thrown into a pond only last for so long). It's chain of causality could die-off - now continuous lying is another matter all together.
My points in this blurb merely show that a rational economic member of society should use lies in some cases; for it is a best response in many common instances - but of course not all and, if avoidable, not for the wrong reasons.
The author is almost making it seem like models are reality and that people think that. They're not and I don't think anyone has ever thought they were...
Further and like other comments already mentioned, the brain is thought of and treated as a turing machine, not a digital computer. It's done this way, because the brain can be mapped to the definition of a turing machine.
And I have to defend Von Neumann. In his book, he explored turing equivalencies between the brain and computer concepts at the time used to implement the digital turing machine, he didn't actually think that the brain was a one-to-one mapping to a digital computer... He knew the difference between models and reality.
Even for the history of models the author mentions (hydraulics, automata, etc.), these all contain some turing equivalencies if implemented correctly and they were simply using the language and examples at the time to express this.
The author also continues to mangle any and all ideas of modeling, abstraction, and equivalence throughout the whole article. With regard to his 'uniqueness problem', I mean 'information loss' is modeled digitally for a reason.. just because humans are lossy, doesn't mean we can't model them that way. Think of a compressed image file.
I don't think there's a single researcher worth their salt that thinks the 'IP Metaphor' is gospel. That is just a grossly unscientific idea to assume.
We're all free to choose any model or collection of models we wish to approximate reality, but some of them work better than others and the brain is a complicated thing to model.
The author is trying to dramatize a triviality.