In summary, we provide the first evidence that a non-tool-using bird species with a high level of physical cognition innovated habitual tool use in the wild in response to facilitation. This finding is consistent with a prediction of the cognitive constraints hypothesis explaining why flexible tool use is rare. That is, birds with a high level of intelligence and physical cognition, like the kea, should more readily innovate tool use when it is facilitated. We suggest an experimental paradigm to test this prediction, where non-tool-using species that differ in cognitive ability would be required to innovate tool use that is facilitated to varying degrees. The expectation would be that the level of physical cognition and object manipulation skills will be negatively correlated with the degree of facilitation needed for tool use to emerge. Furthermore, our findings indicate that even with a high degree of facilitation the invention of tool use in the wild for species like the kea with considerable domain-general intelligence is cognitively demanding.
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This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification. Our IIS approach screens important interactions by examining only $p$ features instead of all two-way interactions of order $O(p^2)$. Our theory shows that the proposed method enjoys sure screening property in interaction selection in the high-dimensional setting of $p$ growing exponentially with the sample size. In the selection and classification step, we establish a sparse inequality on the estimated coefficient vector for QDA and prove that the classification error of our procedure can be upper-bounded by the oracle classification error plus some smaller order term. Extensive simulation studies and real data analysis show that our proposal compares favorably with existing methods in interaction selection and high-dimensional classification. 2ff7e9595c
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