  Culture involves the origination and transmission of ideas, but the
conditions in which culture can emerge and evolve are unclear. We constructed
and studied a highly simplified neural-network model of these processes. In
this model ideas originate by individual learning from the environment and are
transmitted by communication between individuals. Individuals (or "agents")
comprise a single neuron which receives structured data from the environment
via plastic synaptic connections. The data are generated in the simplest
possible way: linear mixing of independently fluctuating sources and the goal
of learning is to unmix the data. To make this problem tractable we assume that
at least one of the sources fluctuates in a nonGaussian manner. Linear mixing
creates structure in the data, and agents attempt to learn (from the data and
possibly from other individuals) synaptic weights that will unmix, i.e., to
"understand" the agent's world. For a variety of reasons even this goal can be
difficult for a single agent to achieve; we studied one particular type of
difficulty (created by imperfection in synaptic plasticity), though our
conclusions should carry over to many other types of difficulty. We previously
studied whether a small population of communicating agents, learning from each
other, could more easily learn unmixing coefficients than isolated individuals,
learning only from their environment. We found, unsurprisingly, that if agents
learn indiscriminately from any other agent (whether or not they have learned
good solutions), communication does not enhance understanding. Here we extend
the model slightly, by allowing successful learners to be more effective
teachers, and find that now a population of agents can learn more effectively
than isolated individuals. We suggest that a key factor in the onset of culture
might be the development of selective learning.
