Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...) a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. (shrink)

This paper describes a comprehensive survey of English verbs that semantically allow or require an Instrument role. It sheds light on the nature of Instrument roles and instrumentality by examining the distribution in semantic space of those verbs. We show first that verbs that semantically require instruments are typically semantically more complex than predicted by current theories of the structural complexity of verb meanings. We also show that verbs that require or allow instruments constrain the end states of situations they (...) describe more than they constrain the agent's initial activity. Our survey further suggests that the causal role played by the instrument is more varied than suggested by previous studies and requires the introduction of a new subtype of causal relation, which we dub helping. Finally, our survey demonstrates that verbs that semantically require an instrument cluster together more closely in semantic space and constrain the instrument's role and properties more than verbs that merely allow the presence of an instrument. (shrink)