Connectivity in the human brain dissociates entropy and complexity of auditory inputs
Abstract:
Complex systems are described according to two central dimensions: (a) the randomness of
their output, quantified via entropy; and (b) their complexity, which reflects the
organization of a system’s generators. Whereas some approaches hold that complexity can be
reduced to uncertainty or entropy, an axiom of complexity science is that signals with
very high or very low entropy are generated by relatively non-complex systems, while
complex systems typically generate outputs with entropy peaking between these two
extremes. In understanding their environment, individuals would benefit from coding for
both input entropy and complexity; entropy indexes uncertainty and can inform
probabilistic coding strategies, whereas complexity reflects a concise and abstract
representation of the underlying environmental configuration, which can serve independent
purposes, e.g., as a template for generalization and rapid comparisons between
environments. Using functional neuroimaging, we demonstrate that, in response to passively
processed auditory inputs, functional integration patterns in the human brain track both
the entropy and complexity of the auditory signal. Connectivity between several brain
regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty,
whereas connectivity between other regions tracked entropy in a convex manner consistent
with sensitivity to input complexity. These findings suggest that the human brain
simultaneously tracks the uncertainty of sensory data and effectively models their
environmental generators.