The Hyperspace Analogue to Language (HAL) model encodes contextual experience in a representational form that cuts across traditional semantic, grammatical, and syntactic boundaries. Attempts to model subsymbolic representations have been limited for three reasons: contrived representations, narrow focus on one aspect of context (semantics, grammar, etc.), and not being truly "subsymbolic" (just see Glenberg, 1997, BBS article). HAL's vector representations avoid these limitations and capture contexts in which words occur and, as such, have explanatory power that captures a broad range of cognitive phenomena. Results from word priming, syntactic processing, and other domains will be used to support these claims. New results will be unveiled that demonstrate equivalence (at the result level) between Elman's recurrent network approach and the HAL global co- occurrence model of word meaning. You can find out more at http://HAL.ucr.edu