2015 International Siberian Conference on Control and Communications (SIBCON) 2015
DOI: 10.1109/sibcon.2015.7146989
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A quantitative measure for information transfer in human-machine control systems

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Cited by 9 publications
(8 citation statements)
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“…The model (10) was also highly significant (F 1,1901 =5.33; p<0.001), the F L factor was significant only at D=0.05 (p=0.021), and the R 2 was even lower, at 0.003. As a comparison, in [12] the subjects' age factor had somehow comparable effect on their TPS (F 1,5830 =9.48; p=0.002; R 2 =0.002).…”
Section: Throughputmentioning
confidence: 91%
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“…The model (10) was also highly significant (F 1,1901 =5.33; p<0.001), the F L factor was significant only at D=0.05 (p=0.021), and the R 2 was even lower, at 0.003. As a comparison, in [12] the subjects' age factor had somehow comparable effect on their TPS (F 1,5830 =9.48; p=0.002; R 2 =0.002).…”
Section: Throughputmentioning
confidence: 91%
“…The ability to excel with signals of increased complexity somehow explains why humans are notably good, in comparison to machines, at image recognition. In one of our previous experiments we found that the higher was the diversity of objects in visual search/selection tasks, the higher throughput (TPS) was demonstrated -from 42 bit/s for objects' vocabulary size = 2 (simplest tasks) to 78 bit/s for vocabulary size = 29 (Russian letters) [12]. It should be noted that recognition speed for familiar letters and digits was previously estimated as 55 bit/s [7, ref 75].…”
Section: Human Information Input and Outputmentioning
confidence: 93%
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