  Financial markets provide an ideal frame for studying decision making in
crowded environments. Both the amount and accuracy of the data allows to apply
tools and concepts coming from physics that studies collective and emergent
phenomena or self-organised and highly heterogeneous systems. We analyse the
activity of 29,930 non-expert individuals that represent a small portion of the
whole market trading volume. The very heterogeneous activity of individuals
obeys a Zipf's law, while synchronization network properties unveil a community
structure. We thus correlate individual activity with the most eminent
macroscopic signal in financial markets, that is volatility, and quantify how
individuals are clearly polarized by volatility. The assortativity by
attributes of our synchronization networks also indicates that individuals look
at the volatility rather than imitate directly each other thus providing an
interesting interpretation of herding phenomena in human activity. The results
can also improve agent-based models since they provide direct estimation of the
agent's parameters.
