Human efficiency for classifying natural versus random text.
Vision Res
; 50(6): 557-63, 2010 Mar 17.
Article
em En
| MEDLINE
| ID: mdl-20079757
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Leitura
/
Discriminação Psicológica
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article