{'fraction_non_alphanumeric': 0.015523932729624839, 'fraction_numerical': 0.0, 'mean_word_length': 5.730434782608696, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Computational foundations for attentive processes

Notions such as capacity limits pervade the attention literature. This presentation attempts to make these concrete and to discover constraints on plausible solutions to vision. Through the proofs, approximations, and optimizations to find architectures that plausibly do not violate biological constraints, important problems such as information routing and signal interference can be addressed. Perhaps the most important conclusion is that the brain is not solving the generic vision problem. Rather, the generic problem is reshaped through approximations so that it becomes solvable by the amount of processing power available for vision. Selective attention in feature, image, and object space plays a necessary role.