Experiments in psycholinguistics allow us to test hypotheses and build theories. However, psycholinguistic experiments often suffer from low ecological validity, because participants are often required to perform an unusual task in the face of unusual materials. In the current experiment, we test the predictions of Noisy Channel Processing in a naturalistic task: identifying the lyrics of a song. We conducted an experiment where participants heard short excerpts from songs and then indicated which one out of four possible transcriptions they had heard. We found that the predictions of Noisy Channel Processing bear out: options with higher prior and likelihood were chosen more often by participants as the perceived song lyrics. Thus, Noisy Channel Processing is successful in explaining the everyday phenomenon of mis-heard song lyrics. More broadly, this suggests that Noisy Channel Processing captures everyday language processing, and that it is not dependent on unnatural experimental tasks and materials.

Language is simultaneously discrete (symbolic) and continuous (e.g., speech), and meaning-form associations are largely arbitrary. How and why did these properties emerge? To address this question, we study how people develop novel communication systems to refer to a continuous domain (color) using a continuous signal space (whistles). We conducted an experiment in which participants need to generalize from five learned signal-color pairings to a larger range of colors during an online communication game with another participant. We find that: (i) both discreteness and systematicity tend to emerge, such that signaling systems that reflect an underlying symbolic structure as well as systematic association with colors emerge more frequently; and (ii) these emergent systems achieve better communicative performance compared to emergent systems that exhibit only discreteness or only systematicity. These findings suggest a human cognitive bias not only toward symbolic communication, but also toward non-arbitrary meaning-form associations.