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 reliable 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.

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in guinea pig behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, model performance quantitatively matched guinea pig behavior to a remarkable degree. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

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.

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