Human-machine interactions with clinical phrase prediction system, aligning with Zipf’s least effort principle?

• Published in: PloS one Vol. 19; no. 12; p. e0316177
• Main Authors: Zaghir, Jamil, Bjelogrlic, Mina, Goldman, Jean-Philippe, Ehrsam, Julien, Gaudet-Blavignac, Christophe, Lovis, Christian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.12.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptation; Analysis; Biology and Life Sciences; Chatbots; Chronic obstructive pulmonary disease; Cognition & reasoning; Communication; Efficiency; Female; Human-computer interaction; Humans; Language; Linguistics; Male; Man-Machine Systems; Medical personnel; Observational studies; Physical Sciences; Research and Analysis Methods; Social Sciences; Technology; Technology application; Terminology; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0316177

The essence of language and its evolutionary determinants have long been research subjects with multifaceted explorations. This work reports on a large-scale observational study focused on the language use of clinicians interacting with a phrase prediction system in a clinical setting. By adopting principles of adaptation to evolutionary selection pressure, we attempt to identify the major determinants of language emergence specific to this context. The observed adaptation of clinicians’ language behaviour with technology have been confronted to properties shaping language use, and more specifically on two driving forces: conciseness and distinctiveness. Our results suggest that users tailor their interactions to meet these specific forces to minimise the effort required to achieve their objective. At the same time, the study shows that the optimisation is mainly driven by the distinctive nature of interactions, favouring communication accuracy over ease. These results, published for the first time on a large-scale observational study to our knowledge, offer novel fundamental qualitative and quantitative insights into the mechanisms underlying linguistic behaviour among clinicians and its potential implications for language adaptation in human-machine interactions.