The striking constraints of some human cognitive processes stand in stark contrast to the near limitless capability of others. While we can acquire and flexibly use vast amounts of information, the amount we can process at any one time is often stiflingly limited: for example the number of items we can hold in working memory or the number of tasks that can be performed at once. Here, we integrate ideas from information theory, cognitive science, and neuroscience to offer a unified account of why processing is often so limited. We argue that this reflects a fundamental tradeoff between generalization—how effectively existing representations can be used in novel settings—and how many distinct representations can be processed in parallel. Representations that best promote strong forms of generalization — a characteristically human cognitive strength — come at the expense of surprisingly strict limits in the number of items that can be processed at once, an equally characteristic human weakness. We refer to this as the “curse of generalization.” We formulate this first in information-theoretic terms, and then in process models, including a neural network model of classic tasks used to demonstrate strict limits in human processing capacity. This tension offers a potential explanation for a range of phenomena — from performance on the tasks on which we focus, to representational learning and skill acquisition more broadly — as well as the performance of modern machine learning architectures that exhibit generalization capabilities comparable to humans.
NetSI authors
