CAREER: Tracking correlation or inferring causation: How human language processing adapts to the environment

When people converse face-to-face, on the phone, or by video conference, the signal is often noisy and incomplete. Luckily, communication usually still proceeds smoothly because listeners can use other sources of information (the context, visual cues, etc.) to make rational inferences about what the speaker said. This CAREER award aims to expand our theoretical understanding of this process and refine existing computational accounts, by investigating how listeners flexibly adapt their inferences and expectations in different contexts. For example, how do adults adapt their expectations when speaking with a child rather than with another adult or when speaking with other adults with different accents? Results of this work will advance knowledge of the cognitive processes that enable human communication and may have important implications for the development of technologies that support human-to-human or human-to-AI conversations. The PI is in a Hispanic-Serving Institution and plans to broaden participation in STEM science on multiple scales, from one-on-one mentorship of undergraduates to an upper-level undergraduate course and an outreach talk series. The project investigates how listeners flexibly adapt to different contexts (e.g., different speakers/environments), within the perspective of human communication as rational inference under uncertain input. Two possible mechanisms are that listeners track correlations in the environment or that they deploy and update a causal model of the speaker and transmission process. A series of eye-tracking experiments, paired with computational simulations, will capture the inferences that listeners make “on the fly,” when listening to speech. How listeners learn and adjust their representations to the context will be probed by varying the nature and quantity of errors and noise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.