A bit confusing for sure, but I think (not sure) I get what they're saying. Training a nn (for visual tasks at least) consists of training a model with much more dimensions (params) than the input space (eg: controller inputs + atari pixels). This contrasts with a lot of what humans do, which is take higher dimensional information (tons of data per second combining visual, audio, touch/vibration, etc) and synthesizing much lower dimensional models / heuristics / rules of thumb, like the example they give of the 5 second per mile rule for thunder.