Abstract
Communication, often grounded in shared expectations, faces challenges without common linguistic backgrounds. Our study explores how people instinctively turn to the fundamental principles of the physical world to overcome communication barriers. Specifically, through the Tacit Communication Game, we investigate how participants develop novel strategies for conveying messages without relying on common linguistic signals. We developed a new computational model built from the principle of expectancy violations of a set of common universal priors derived from movement kinetics. The model closely resembles the Sender’s messages, with its core variable – the information-theoretic surprise – explaining the Receiver’s physiological and neural responses. This is evidenced by a significant correlation with the pupil diameter, indicating cognitive effort, and neural activity in brain areas related to expectancy violations. This work highlights the adaptability of human communication, showing how surprise can be a powerful tool in forming new communicative strategies without relying on common language.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have made the following changes to our original manuscript as a response to the reviewers comments. Firstly, we have updated the title to better reflect the focus of our research on goal signaling in novel human interactions, rather than broadly addressing non-verbal communication. In response to reviewer feedback, we have strengthened the connection to verbal communication and emphasized the novelty of our computational model. We now highlight how our framework unifies both verbal and non-verbal contexts through the concept of expectancy violations. We also introduce predictability as a key mechanism in verbal communication, contrasting it with situations requiring unconventional strategies. Additionally, we've added a new figure (Figure 1) to visually summarize the integration of computational modeling with behavioral, physiological, and neural data. Key changes in the results include revising the analysis of pupillary dilation responses (PDR) by modeling surprise as a continuous rather than categorical variable, aligning it with our EEG analysis. We also performed source localization, using participants' MRI data, which identified activations near the SMA and dACC-areas linked to motor planning and error detection-supporting our initial interpretations. Finally, the discussion has been expanded to incorporate relevant literature, particularly on referential communication and Theory of Mind models, and to address the ecological validity and broader implications of our findings.
