  Artificial neural networks (ANNs) exhibit a narrow scope of expertise on
stationary independent data. However, the data in the real world is continuous
and dynamic, and ANNs must adapt to novel scenarios while also retaining the
learned knowledge to become lifelong learners. The ability of humans to excel
at these tasks can be attributed to multiple factors ranging from cognitive
computational structures, cognitive biases, and the multi-memory systems in the
brain. We incorporate key concepts from each of these to design a novel
framework, Dual Cognitive Architecture (DUCA), which includes multiple
sub-systems, implicit and explicit knowledge representation dichotomy,
inductive bias, and a multi-memory system. The inductive bias learner within
DUCA is instrumental in encoding shape information, effectively countering the
tendency of ANNs to learn local textures. Simultaneously, the inclusion of a
semantic memory submodule facilitates the gradual consolidation of knowledge,
replicating the dynamics observed in fast and slow learning systems,
reminiscent of the principles underpinning the complementary learning system in
human cognition. DUCA shows improvement across different settings and datasets,
and it also exhibits reduced task recency bias, without the need for extra
information. To further test the versatility of lifelong learning methods on a
challenging distribution shift, we introduce a novel domain-incremental dataset
DN4IL. In addition to improving performance on existing benchmarks, DUCA also
demonstrates superior performance on this complex dataset.
