Is there any recent work on modeling how we rapidly acquire new knowledge? I work with neural network models of human cognition a lot, and one thing that bugs me about them is the timescale: they learn over thousands of trials whereas humans seem to learn after a couple examples. So I've been wondering if this discrepancy has studied, and if so, what terminology has been used to study it. Specifically, I'd be very interested to see some work trying to get neural network models to learn at a more human timescale.

A good example is learning to play a new video-game. We can do this in a few minutes if there is a tutorial explaining the basic game mechanics to us. However, modeling the same process would involve slowly strengthening perceptual-motor associations and could take a very long time indeed.

Note that I know about one shot learning, and that is not quite what I'm talking about. That literature mainly revolves around image recognition and learning after a single presentation, whereas I'm curious about rapid learning more generally. This sounds a little vague, but the question "why do our models learn several orders of magnitude slower than us?" seems specific enough to have come up in the literature.
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.