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.

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Cost-benefit analysis is a key determinant of decision-making , yet little is known about the underlying neural circuit mechanisms, and investigating this concept using laboratory animals is challenging without quantitative behavioral readouts and theoretical frameworks. In order to tackle this challenge, here we took a theory-based approach and created an experimental design in order for mice to perform a task as a rationale agent and optimize decisions. Our novel behavioral paradigm offers two possibilities of obtaining reward. Mice initially prefer the easier method, but as its cost increases, their preference switches. We quantified these switching decisions using an indifference point where the values of the choices became equivalent. A novel component of our task design is the implementation of a systematic and flexible variation of cost and benefit parameters in two-dimensional parameter space. This allows researchers to choose a wide range of parameter combinations and quantify the shift of the switching decisions. Our results demonstrated that indeed the parametric manipulation successfully influenced the switching decisions relative to the given parameter values, suggesting that mice perform the task as rational agents. A theoretical framework based on the optimization principle further confirmed the switching decisions of mice were optimal. Furthermore, we demonstrated that although the preference shift was influenced by different internal states of motivation, the valuation process of mice was intact regardless of motivational states. Considering the arsenal of tools available for mice , our theory-centered approach provides a quantitative