The relational processing limits of classic and contemporary neural network models of language processing
This episode discusses a paper analyzing why both classic and modern neural network models fail at dynamic relational reasoning. The hosts conclude that these models cannot bind roles to fillers on the fly, causing them to fail when concepts are misplaced or when statistical rules are inverted. The solution requires building explicit mechanisms, like dynamic binding, rather than relying solely on massive data scaling.