  Humans can generalize from only a few examples and from little pretraining on
similar tasks. Yet, machine learning (ML) typically requires large data to
learn or pre-learn to transfer. Motivated by nativism and artificial general
intelligence, we directly model human-innate priors in abstract visual tasks
such as character and doodle recognition. This yields a white-box model that
learns general-appearance similarity by mimicking how humans naturally
``distort'' an object at first sight. Using just nearest-neighbor
classification on this cognitively-inspired similarity space, we achieve
human-level recognition with only $1$--$10$ examples per class and no
pretraining. This differs from few-shot learning that uses massive pretraining.
In the tiny-data regime of MNIST, EMNIST, Omniglot, and QuickDraw benchmarks,
we outperform both modern neural networks and classical ML. For unsupervised
learning, by learning the non-Euclidean, general-appearance similarity space in
a $k$-means style, we achieve multifarious visual realizations of abstract
concepts by generating human-intuitive archetypes as cluster centroids.
