Advances in computational linguistics and discourse processing have made it possible to automate many language-and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.

A variety of theoretical frameworks predict the resemblance of behaviors between two people engaged in communication, in the form of coordination, mimicry, or alignment. However, little is known about the time course of the behavior matching, even though there is evidence that dyads synchronize oscillatory motions (e.g., postural sway). This study examined the temporal structure of nonoscillatory actions-language, facial, and gestural behaviors-produced during a route communication task. The focus was the temporal relationship between matching behaviors in the interlocutors (e.g., facial behavior in one interlocutor vs. the same facial behavior in the other interlocutor). Cross-recurrence analysis revealed that within each category tested (language, facial, gestural), interlocutors synchronized matching behaviors, at temporal lags short enough to provide imitation of one interlocutor by the other, from one conversational turn to the next. Both social and cognitive variables predicted the degree of temporal organization. These findings suggest that the temporal structure of matching behaviors provides low-level and low-cost resources for human interaction.

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.

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