We present a theoretical framework bearing on the evolution of written communication. We analyze writing as a special kind of graphic code. Like languages, graphic codes consist of stable, conventional mappings between symbols and meanings, but (unlike spoken or signed languages) their symbols consist of enduring images. This gives them the unique capacity to transmit information in one go across time and space. Yet this capacity usually remains quite unexploited, because most graphic codes are insufficiently informative. They may only be (...) used for mnemonic purposes or as props for oral communication in real-time encounters. Writing systems, unlike other graphic codes, work by encoding a natural language. This allows them to support asynchronous communication in a more powerful and versatile way than any other graphic code. Yet, writing systems will not automatically unlock the capacity to communicate asynchronously. We argue that this capacity is a rarity in non-literate societies, and not so frequent even in literate ones. Asynchronous communication is intrinsically inefficient because asynchrony constrains the amount of information that the interlocutors share and limits possibilities for repair. This would explain why synchronous, face-to-face communication always fosters the development of sophisticated codes (natural languages), but similar codes for asynchronous communication evolve with more difficulties. It also implies that writing cannot have evolved, at first, for supporting asynchronous communication. (shrink)

The amount of information conveyed by linguistic conventions depends on their precision, yet the codes that humans and other animals use to communicate are quite ambiguous: they may map several vague meanings to the same symbol. How does semantic precision evolve, and what are the constraints that limit it? We address this question using a multiplayer gaming app, where individuals communicate with one another in a scaled-up referential game. Here, the goal is for a sender to use black and white (...) symbols to communicate colors. We expected that the players’ mappings between symbols and colors would grow more specific over time, through a selection process whereby precise mappings are preferentially copied. We found that players become increasingly more precise in their use of symbols over the course of their interactions. This trend did not, however, result from selective copying of precise mappings. We explore the implications of this result for the study of lexical ambiguity, Zipf's Law of Meaning, and disagreements over semantic conventions. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans’ ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal (...) interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants’ own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily. (shrink)

Human language looms large in the emergence and evolution of graphic codes. Here, I argue that language not only acts as a strong constraint on graphic codes, but it is also a precondition for their emergence and their evolution as computational devices.