  A cognitive architecture aimed at cumulative learning must provide the
necessary information and control structures to allow agents to learn
incrementally and autonomously from their experience. This involves managing an
agent's goals as well as continuously relating sensory information to these in
its perception-cognition information stack. The more varied the environment of
a learning agent is, the more general and flexible must be these mechanisms to
handle a wider variety of relevant patterns, tasks, and goal structures. While
many researchers agree that information at different levels of abstraction
likely differs in its makeup and structure and processing mechanisms, agreement
on the particulars of such differences is not generally shared in the research
community. A binary processing architecture (often referred to as System-1 and
System-2) has been proposed as a model of cognitive processing for low- and
high-level information, respectively. We posit that cognition is not binary in
this way and that knowledge at any level of abstraction involves what we refer
to as neurosymbolic information, meaning that data at both high and low levels
must contain both symbolic and subsymbolic information. Further, we argue that
the main differentiating factor between the processing of high and low levels
of data abstraction can be largely attributed to the nature of the involved
attention mechanisms. We describe the key arguments behind this view and review
relevant evidence from the literature.
