The target article offers a negative, eliminativist thesis, dissolving the specialness of mirroring processes into a solution of associative mechanisms. We support the authors' project enthusiastically. What they are currently missing, we argue, is a positive, generative thesis about associative learning mechanisms and how they might give way to the complex, multimodal coordination that naturally arises in social interaction.

The words people use and the way they use them can reveal a great deal about their mental states when they attempt to deceive. The challenge for researchers is how to reliably distinguish the linguistic features that characterize these hidden states. In this study, we use a natural language processing tool called Coh-Metrix to evaluate deceptive and truthful conversations that occur within a context of computer-mediated communication. Coh-Metrix is unique in that it tracks linguistic features based on cognitive and social factors that are hypothesized to influence deception. The results from Coh-Metrix are compared to linguistic features reported in previous independent research, which used a natural language processing tool called Linguistic Inquiry and Word Count. The comparison reveals converging and contrasting alignment for several linguistic features and establishes new insights on deceptive language and its use in conversation.