Latent Semantic Analysis (LSA) treats language learning and representation as a problem in mathematical induction. It casts the passages of a large and representative text corpus as a system of simultaneous linear equations in which passage meaning equals the sum of word meanings. LSA simulates human language understanding with surprising fidelity. Successes to date disprove the poverty of the stimulus argument for lexical meaning and recast the problem of syntax learning, but leave much room for improvement. Semantic atoms are not only single words; idioms need lexicalization. Syntax surely matters; LSA ignores word order. LSA’s knowledge resembles intuition; people also use language for logic. Relations to other input matter. LSA represents perceptual phenomena vicariously, e.g. color relations. Demonstrations that people think in other modes, or that LSA does not exhaust linguistic meaning do not question LS A’s validity, but call for more modeling, testing, and integration.