کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4034483 | 1263460 | 2010 | 7 صفحه PDF | دانلود رایگان |
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.
Journal: Vision Research - Volume 50, Issue 6, 17 March 2010, Pages 557–563