Sounds of sickness: can people identify infectious disease using sounds of coughs and sneezes?

Abstract

Cough, cough. Is that person sick, or do they just have a throat tickle? A growing body of research suggests pathogen threats shape key aspects of human sociality. However, less research has investigated specific processes involved in pathogen threat detection. Here, we examine whether perceivers can accurately detect pathogen threats using an understudied sensory modality-sound. Participants in four studies judged whether cough and sneeze sounds were produced by people infected with a communicable disease or not. We found no evidence that participants could accurately identify the origins of these sounds. Instead, the more disgusting they perceived a sound to be, the more likely they were to judge that it came from an infected person (regardless of whether it did). Thus, unlike research indicating perceivers can accurately diagnose infection using other sensory modalities (e.g. sight, smell), we find people overperceive pathogen threat in subjectively disgusting sounds.

Keywords: accuracy; auditory perception; behavioural immune system; evolutionary psychology; pathogen detection.

Figure 1.

Plots visualize judgement accuracy by sound origin across studies (study 3 depicts separate panels by sound rating condition). The dashed lines represent chance levels, and error bars represent profile 95% confidence intervals based on the standard error of the sound origin difference. Points represent average accuracy for stimuli, ‘jittered' with random noise to make visible unique accuracy scores. (Online version in colour.)

Figure 2.

Plots visualize the relationship between sound rating (clarity or disgust) and judgement accuracy by sound origin (infectious or non-infectious targets). Higher ratings of disgust correlated positively with accuracy for infectious sounds but negatively with accuracy for non-infectious sounds (clarity ratings had no significant effect). Dashed lines represent adjusted average accuracy for each condition. Small points represent raw judgements ‘jittered’ with random noise to make visible unique judgements. Ribbons around the lines represent 95% confidence intervals based on the three-way interaction (see effects package in R [30]). (Online version in colour.)