Proceedings of the Annual Meeting of the Cognitive Science Society

The Hyperspace Analogue to Language (HAL) model is a methodology for capturing semantics from a corpus by analysis of global co-occurrence. A priming experiment from Lund et al. (1995) which did not produce associative priming with humans or in the HAL simulation is repeated with rearranged control trials. Our experiment now finds associative priming with human subjects, while the HAL simulation again does not produce associative priming. Associative word norms are examined in relation to HAL's semantics in an attempt to illuminate the semantic bias of the model. Correlations with association norms are found in the temporal sequence of words within the corpus. When the associative norm data are split according to simulation semantic distances, a minority of the associative pairs that are close semantic neighbors are found to be responsible for this correlation. This result suggests that most associative information is not carried by temporal word sequence in language. This methodology is found to be useful in separating typical "associative" stimuli into pure-associative and semantic-associative subsets. The notion that associativity can be characterized by temporal association in language receives little or no support from our corpus analysis and priming experiments. The extent that "word associations" can be characterized by temporal association seems to be more a function of semantic neighborhood which is a reflection of semantic similarity in HAL's vector representations.