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I have rudimentary knowledge of evolution, and biology in general, so bear with me if this question is a bit naive.

Let's say we have a particular trait, like highly sensitive peripheral vision. There are two ways to go about explaining how this came about:

One is to say that evolution selected for it over time, because animals with a poor sensitivity to peripheral activity could easily become prey. So there is an advantage to having sensitive peripheral vision, and such a trait is adaptive. The picture I have in my mind is of an assembly line where there are randomly wired visual cortices and the best ones get selected for automatically. This seems to be the view of the field of evolutionary algorithms, at least to my mind.

Now, if we focus on the random nature of the wiring, we might see that there is an explosion of possibilities in terms of how the visual system can be wired together. After all, evolution is blind, and it doesn't know beforehand which physical signals are of interest.

I'm trying to think of a more plausible mechanistic explanation. Suppose we assume that given certain reliable properties of the physical world, like permanence of objects for example, evolution has found the optimal way to wire the visual cortex so that peripheral vision (and a host of other good-to-have features) is a natural outcome, then it would give us more focus in what environmental features and algorithms to look for. This can also be thought of as the idea that evolution, over time, has come to assume that certain things in the physical world will be a given and exploits these. (For example, objects in the peripheral areas of vision will tend to have different perceived velocities, so perhaps normalizing velocities across the field of vision automatically results in a need for higher sensitivity in that region. It just so happens that this could also be useful in detecting predators).

My question then is, is my assumption in the above paragraph valid? (Of course, the question then gets shifted to "Why normalize velocities across the visual field in the first place?").

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I'm not sure that I fully understand the question, so I'm not answering. However the idea that adaptive evolution proceeds from a set of randomly wired visual cortices seems to me to be a wrong assumption - rather at each stage it starts from an already partially optimised wiring, with smallish changes offering some selective advantage. I think this resembles your more plausible explanation, but as I say, I'm not sure that I fully understand. –  Alan Boyd Feb 14 at 9:53
    
What is your assumption, I don't fully understand. You assume that given some properties of the world, evolution selects for the most optimal solution (for a given living thing to increase its reproductive success). Is this your assumption? –  Remi.b Feb 14 at 9:57
    
@Remi.b: My assumption, stated more simply, is a sort of "maximum efficiency hypothesis". That is, that evolution assumes the environment to be a certain way, and codes this into the developmental program, and therefore implicit in the developmental program are certain invariant features of the world, which the program uses to develop the brain fully. I think my question is more about developmental biology than it is about evolution. –  Joebevo Feb 14 at 11:11
    
@Joebevo See my update. let me know if it starts to answer your question! Sorry if I use some technical words that you don't know but their meaning are easily understandable from wiki articles. You might already know the concept of fitness landscape from computer science and genetic algorithm. –  Remi.b Feb 14 at 11:57
    
I recommend looking at Hoffman's interface theory of perception to help you explore some of the assumptions you are implicitly making in your question. Also, just because a fitness peak exists, doesn't mean you'll get there (for any reasonable notion of getting there). –  Artem Kaznatcheev Feb 15 at 14:20

1 Answer 1

I don't fully understand your question. I hope the following will help a bit.

I'd like to pinpoint what seems to me to be one important mistake in your text. Reading you we have the feeling that selection acts on wires so to select the best combination. If this is what you meant, then it is obviously wrong. The thing is that the change that get inherited are alleles. An allele is a variant of a gene. For example: gene codes for eyes color. A bi-allelic gene has an allele which codes for blue eyes while the other codes for brown eyes. Assuming that there is genetic variance underlying the wire connection variance in the population, then yes selection will select for the best genetic variant. This does not necessarily mean that this best genetic variant is the most optimal possible to imagine solution. The most optimal allele might have never been created just because of the randomness nature of mutations.

EDIT:

To answer:

Do natural selection always bring a particular phenotypic (loosly speaking, phenotype = morphological) trait to its optimal state?

No! It does not.

  1. The main reason is that the genetic variant coding for this optimal trait does not necessarily exist because of the randomness of mutational process. Also, because of the metabolic pathway bringing some variance to a trait, it is often very hard to even imagine a mutation that would allow creating a new, more optimal, phenotypic variant. So indeed developmental biology has to be taken in account to.

  2. Because in absence of genetic drift (infinite population), selection can only bring genotypes uphill in the fitness landscape. If a very high peak exist somewhere in the fitness landscape but in order to reach such peak, 10 mutations are needed were all of them in any other genetic background are deleterious (correspond to valleys in fitness landscape), then the optimal high will probably never be reached (except if all 10 mutations appear suddenly in the same individual).

  3. Because selection might be weaker than genetic drift. Also, different subpopulations might be in different environments were different traits correspond to optimal traits and because of migration (gene flow) between subpopulations, it is possible that no optimal phenotypic trait can be ever seen although all the genotypic variance exist.

  4. The seemingly most optimal trait might actually not be really optimal. For example: Peripheral vision might be limited by the brain rather than by the eye because it would be an important energy cost for the brain to process more information, a cost that might outweight the benefit of having a better peripheral vision.

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