Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper, we extended the DiCarlo & Cox 2007 tangled object manifold framework of object recognition to bet-ter address the unsupervised nature of category learning. We developed a novel Markov chain-based similarity metricthat formally connects aspects of manifold untangling with trace learning. Using these developments, we replaced un-observable labels and artificial category boundaries with our observable Markov chain walk based similarity metric as atheoretically grounded target for unsupervised category untangling. Further, we developed a new rationale for how neu-ronal input windows should be chosen for an untangling algorithm using this new framework. This new framework formanifold untangling and trace learning allowed us to synthesize aspects of simple cell learning, complex cell learning,and axonal development theories, into a high-level theory of how the visual cortex learns to separate object categories at acomputational level.