title: Human efficiency for classifying natural versus random text.
abstract: 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.
authors.affiliation: Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, United Kingdom. pn@white.stanford.edu
authors.forename: Peter
authors.initials: P
authors.lastname: Neri
authors.forename: Alicia
authors.initials: A
authors.lastname: Liu
authors.forename: Dennis M
authors.initials: DM
authors.lastname: Levi
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D004192:Discrimination, Psychological; D005260:Female; D006801:Humans; D008297:Male; D011932:Reading; D012684:Sensory Thresholds; D013997:Time Factors; D055815:Young Adult