Proceedings of the Annual Meeting of the Cognitive Science Society

Although learning is a cognitive task, machine learning algorithms, in general, fail to take advantage of existing psychological limitations. In this paper, we use a learning task from the field of natural language processing and examine three well-known cognitive biases for human information processing: 1) the tendency to rely on the most recent information, 2) the heightened accessibility of the subject of a sentence, and 3) short term memory limitations. In a series of experiments, w e modify a baseline instance representation in response to these limitations and show that the overall performance of the learning algorithm improves as increasingly more cognitive biases and limitations are explicitly incorporated into the instance representation.