The Civic Platform - A Political Journal of Ideas and Analysis

Well, this supports my belief that the differences in the races and ethnies are instinctual and require no learning until an incident triggers the instinct. It is why Beethoven lives, and myths, themes, allegory, and even morals, appeal to and are expressed in certain populations while in others they are not. This assumes that our DNA has not been overly insulted by foreign intrusions.

University, skills such as flying are easy to refine because the
innate ability of today’s birds depends indirectly on the learning
that their ancestors did, which leaves a genetically specified latent
memory for flying.

Learning to fly is easy, if you are a bird. But why is it that birds
learn so easily how to fly? It is well known that birds learn through
practice, and that they gradually refine their innate ability into a
finely tuned skill. According to a new theory by Dr Stone of
Sheffield University, skills such as flying are easy to refine
because the innate ability of today’s birds depends indirectly on the
learning that their ancestors did, which leaves a genetically
specified latent memory for flying.

The theory has been tested on simple models of brains called
artificial neural networks, which can be made to evolve using genetic
algorithms. Whilst these networks do not fly, they do learn
associations, and these associations could take the form of a skill
such as flying.

Using computer simulations, Stone demonstrates in a study, publishing
in the open access journal PLoS Computational Biology, that the
ability to learn in network models has two surprising consequences.
First, learning accelerates the rate at which a skill becomes innate
over generations, so it accelerates the evolution of innate skill
acquisition. For comparison, evolution is slow if a network simply
inherits its innate ability from its parents, but is not allowed to
learn in order to improve this innate ability. Second, learning in
previous generations indirectly induces the formation of a latent
memory in the current generation, and therefore decreases the amount
of learning required. It matters how quickly learning occurs, because
time spent learning is time spent not eating, or time spent being
eaten, which incurs the ultimate penalty for slow learners. These
effects are especially pronounced if there is a large
biological ‘fitness cost’ to learning, where biological fitness is
measured in terms of the number of offspring each individual has.

Crucially, the beneficial effects of learning depend on the unusual
form of information storage in neural networks, a form common to
biological and artificial neural networks. Unlike computers, which
store each item of information in a specific location in the
computer’s memory chip, neural networks store each item distributed
over many neuronal connections. If information is stored as
distributed representations then evolution is accelerated. This may
help explain how complex motor skills, such as nest building and
hunting skills, are acquired by a combination of innate ability and
learning over many generations.

The new theory has its roots in ideas proposed by James Baldwin in
1896, who made the counter-intuitive argument that learning within
each generation could guide evolution of innate behaviour over future
generations. It now seems that Baldwin may have been more right than
he could have guessed, even though concepts such as artificial neural
networks and distributed representations were not known in his time.

A previous version of this article appeared as an Early Online
Release on June 8, 2007 (doi: 10.1371/journal. pcbi.0030147. eor).

Stone JV (2007) Distributed representations accelerate evolution of
adaptive behaviours. PLoS Comput Biol 3(7): e147.