 While many AI researchers have been interested in semantic representation and inference as practical means for achieving linguistic and inferential competence in machines, others have approached these issues from the perspective of modeling human cognition. Prior to the 1980s, computational modeling of NLP and cognition more broadly were pursued almost exclusively within a representationalist paradigm, i.e., one that regarded all intelligent behavior as reducible to symbol manipulation (Newell and Simon's physical symbol systems hypothesis). In the 1980s, connectionist (or neural) models enjoyed a resurgence, and came to be seen by many as rivalling representationalist approaches. We briefly summarize these developments under two subheadings below. 