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In the last few years, researchers have begun to study novel human communication systems in the laboratory (Experimental Semiotics, ES). The first goal of this article is to provide a primer to ES, which we will do by reviewing the experimental paradigms developed by experimental semioticians, as well as the main research themes that have emerged in the discipline. A second goal is to illustrate what implications ES has for linguistics. In particular, we will argue that ES has the potential to complement linguistics in important ways and illustrate such potential in the context of each of the themes we review.

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In the dominant theoretical framework, human communication is modeled as the faithful transmission of information. This implies that when people are involved in communicational exchanges, they should be sensitive to the success with which information is transmitted, easily detecting when conversations lack coherence. The expectation that humans are good at detecting conversational incoherence is in line with common intuition, but there are several reasons to suspect that it might be unrealistic. First, similar intuitions have been shown to be unrealistic for a number of psychological processes. Second, faithful information transmission may conflict with other conversational goals. Third, mechanisms supporting information transmission may themselves lead to cases of incoherence being missed. To ascertain the extent to which people are insensitive to patches of serious conversational incoherence, we generated such patches in the laboratory by repeatedly crossing two unrelated conversations. Across two studies, involving both narrowly and broadly focused conversations, between 27% and 42% of the conversants did not notice that their conversations had been crossed. The results of these studies suggest that it may indeed be unrealistic to model spontaneous conversation as faithful information transmission. Rather, our results are more consistent with models of communication that view it as involving noisy and error-prone inferential processes, serving multiple independent goals.