  Systems are typically made from simple components regardless of their
complexity. While the function of each part is easily understood, higher order
functions are emergent properties and are notoriously difficult to explain. In
networked systems, both digital and biological, each component receives inputs,
performs a simple computation, and creates an output. When these components
have multiple outputs, we intuitively assume that the outputs are causally
dependent on the inputs but are themselves independent of each other given the
state of their shared input. However, this intuition can be violated for
components with probabilistic logic, as these typically cannot be decomposed
into separate logic gates with one output each. This violation of conditional
independence on the past system state is equivalent to instantaneous
interaction --- the idea is that some information between the outputs is not
coming from the inputs and thus must have been created instantaneously. Here we
compare evolved artificial neural systems with and without instantaneous
interaction across several task environments. We show that systems without
instantaneous interactions evolve faster, to higher final levels of
performance, and require fewer logic components to create a densely connected
cognitive machinery.
