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This finding raises a critical question: is the patients' increased error for multiple items simply a result of degraded memory of a fractal's location, or could it be that patients localized a fractal at the location of another object they had seen in the original memory array?
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All other estimators are quite robust against noise and show only a significantly increasing error for very low iSNRs.
We tested the performance of both MEN and BBSR on these PKI sets with increasing error for two choices of the respective weight parameters as follows.
Adult humans estimating large numbers of items show increasing errors for proportionately smaller differences, but our accuracy using numerals is almost entirely independent of number magnitude, i.e. it is linear.
Frank et al. (2007) found an increased error positivity for met/met carriers but no COMT effects on ERN amplitudes, concurring with our results.
However the fit of the model to the northern data was less good so there may be increased error in the calculation for this region.
For many types of variability, the reasons for the increased error rates are still unknown.
Thus, following EF-learning there might be a "double impact" on ERN amplitude: an increased error-likelihood for yes responses and an increased conflict due to interfering false candidates after EF-learning.
In general, for each aligner, both recall and precision improve for increasing read length for a fixed error rate, and degrade for increasing error rates for a fixed read length.
Another work in [27] also studied unequal error protection raptor codes for increasing error robustness of scalable video transmission.
Increasing the acceptance criteria by 5% only increased the error for the fractions of deep and young groundwater by 1%.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com