Human efficiency for classifying natural versus random text.

Humans are remarkably efficient at processing natural text. We quantified efficiency for discriminating a sample of meaningful text from a sample of random text by disrupting the meaningful sample, and measuring how much disruption human readers can tolerate before the two samples become indistinguishable. We performed these measurements for a wide range of conditions, involving samples of different lengths and containing letters, words or Chinese characters. We then compared human performance to the best possible performance achieved by a Bayesian estimator under the conditions in which we tested our participants, and in so doing we determined their absolute efficiency. Values were mostly in the range 5-40%, in agreement with reported efficiencies for many visual tasks. Although not intended as a veridical model of human processing, we found that the Bayesian model captured some (but not all) aspects of how humans classified text in our tasks and conditions. Copyright 2010 Elsevier Ltd. All rights reserved.