In this paper we start with a simple question, how is it possible that humans
can recognize different movements over skin with only a prior visual experience
of them? Or in general, what is the representation of spatial sequences that
are invariant to scale, rotation, and translation across different modalities?
To answer, we rethink the mathematical representation of spatial sequences,
argue against the minimum description length principle, and focus on the
movements of attention. We advance the idea that spatial sequences must be
represented on different levels of abstraction, this adds redundancy but is
necessary for recognition and generalization. To address the open question of
how these abstractions are formed we propose two hypotheses: the first invites
exploring selectionism learning, instead of finding parameters in some models;
the second proposes to find new data structures, not neural network
architectures, to efficiently store and operate over redundant features to be
further selected. Movements of attention are central to human cognition and
lessons should be applied to new better learning algorithms.

我们重新思考了空间序列的数学表示方法，提出了两种假设来解释抽象形成的方式，并将注意力移动视为人类认知的核心，将相关教训应用于更好的学习算法。