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Thus, eliminating irrelevant objects between sample and test sets and not the exact number of test objects affected subjects' RT.
This test enabled us to assess whether number of test objects (i.e. selection load) was responsible for subjects' RT variation or objects elimination between sample and test sets.
Applying subsequent Pearson test for comparing correlation coefficients, we found that the difference between correlation coefficients was significant: participants' RT was significantly more correlated to the amount of difference between sample and test sets (p<0.01).
In previous section we showed that participants' RT increased as we increased the number of eliminated objects between sample and test sets (i.e. irrelevancy load) suggesting that participants used a filtering process to eliminate irrelevant objects from their WM.
Showing that participants' RT increase as we eliminate more objects between sample and test sets also rules out the possibility that shifting attention between test objects number, to find the possible change location, is responsible for delayed participants' RT.
Third, we measured the predictability of subjects' RT on the basis of relevant (i.e. number of test objects), irrelevant objects (i.e. number of objects eliminated between sample and test) and also sample objects numbers by measuring the correlation between participants' RT and these three factors.
Similar(46)
Furthermore, the time between sampling and testing did not affect the number of thrombocytes notably.
The intervals between sampling and testing varied between 15 minutes and 24 hours (due to the fact that some samples were taken on the farm of origin).
The intervals between sampling and testing also varied between 10 minutes and 20 hours (due to the fact that some control calves were born during the weekend).
The decay during storage of serum was analyzed with a linear mixed effects model of IV as dependent variable and the time between sampling and testing blood, t-store, as independent variable, applied to the sera which were tested twice.
The DNMS protocol also proved to be highly versatile as it can easily be given with varying retention intervals and altered levels of interference between sample and recognition test [7].
More suggestions(16)
between sample and nozzle
between sample and questionnaire
between learning and test
between grade and test
between train and test
between control and test
between sample and control
between adapter and test
between sample and model
between sample and polishing
between sample and dilution
between sample and population
between sample and sensor
between sample and match
between simulation and test
between trial and test
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