Is there any recent work on modeling how we rapidly acquire new knowledge?
Humans actually exhibit both slow and fast learning and they have somewhat different properties. One distinction is between "declarative" memory (for example, facts like "tigers have stripes" or "Paris is the capital of France") and "procedural" learning (such as perceptuo-motor skills like riding a bike or playing a musical instrument). Declarative memory lends itself to fast learning but procedural memory requires a lot of practice. 

Perhaps even more relevant to your question is that neural network models are also capable of fast and slow learning, depending on the sparseness of the representations and tolerance for disruption. One of the foundational papers on this topic (McClelland, McNaughton, &amp; O'Reilly, 1995) described a neural network model with two "complementary learning systems": (1) A fast-learning hippocampal memory system that relies on sparse representations and is not able to maintain previously-learned information when learning new information (sometimes called "catastrophic interference"). (2) A slow-learning neocortical system that relies on distributed representations and is able to integrate many different examples into an overall structure that can support generalization.

References:

McClelland, J. L., McNaughton, B. L., &amp; O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457.