Pupillometry as a reliable metric of auditory detection and discrimination across diverse stimulus paradigms in animal models

Autor: Isha Kumbam, Pilar Montes-Lourido, Manaswini Kar, Srivatsun Sadagopan
Přispěvatelé: NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM), Centro de Estudos de Doenças Crónicas (CEDOC)
Rok vydání: 2020
Předmět:
Zdroj: Scientific Reports
Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
ISSN: 2045-2322
Popis: Estimates of detection and discrimination thresholds are often used to explore broad perceptual similarities between human subjects and animal models. Pupillometry shows great promise as a non-invasive, easily-deployable method of comparing human and animal thresholds. Using pupillometry, previous studies in animal models have obtained threshold estimates to simple stimuli such as pure tones, but have not explored whether similar pupil responses can be evoked by complex stimuli, what other stimulus contingencies might affect stimulus-evoked pupil responses, and if pupil responses can be modulated by experience or short-term training. In this study, we used an auditory oddball paradigm to estimate detection and discrimination thresholds across a wide range of stimuli in guinea pigs. We demonstrate that pupillometry yields reliable detection and discrimination thresholds across a range of simple (tones) and complex (conspecific vocalizations) stimuli; that pupil responses can be robustly evoked using different stimulus contingencies (low-level acoustic changes, or higher level categorical changes); and that pupil responses are modulated by short-term training. These results lay the foundation for using pupillometry as a high-throughput method of estimating thresholds in large experimental cohorts, and unveil the full potential of using pupillometry to explore broad similarities between humans and animal models.
Databáze: OpenAIRE