My work pictured by AI – Huw Swanborough

What is this work about? New-born infants are entirely dependent on caregiver attention and support, unable to care for themselves. As such, crying plays a vital role in allowing infants to capture the attention of caregivers to communicate aversive states and an effective cry must be sufficiently salient in the acoustic environment for this to succeed. However, optimal crying is a balancing act as overly salient sounds are often highly aversive to the listener, potentially eliciting detrimental caregiver responses that can range from reduced care quality to infant abuse or infanticide in extreme cases. An optimal infant cry then, may have evolved to be maximally salient, yet mitigating the aversive quality of the cry itself. One such mechanism this optimisation may occur is via cry pitch contours, as new-born infants have been shown to produce cries with the pitch/accent contour of the language prevalent in the environment in which they gestated, we hypothesise that these native accent contours may be survival advantageous for infants in mitigating the aversive nature of cries, whilst not impacting the perceptual salience of the signals. To this end, we will investigate subjective and objective measures of aversion and salience to infant cries and their interaction with native/non-native accent contours, as well as investigate the impact this has on neural circuits underlying cry-perception in adults.

The first word that came to mind when seeing the AI-generated picture? Crying.