Sentence examples for more errors compared from inspiring English sources

Subjects who were annoyed performed less well (i.e., produced more errors compared to the "no noise" group) and "not annoyed" subjects showed a speed-accuracy trade-off (i.e., reacted faster but made more errors compared to "no noise" subjects).

Therefore, for the same channel fading, the 2x1 Alamouti MISO diversity corrects more errors compared to lower orders and achieves a better diversity gain.

For Experiment 1, syntactic processing, behavioural responses were ignored because we expected the G-SLI participants to make more errors compared to controls.

In the speeded response task, participants made significantly more errors compared to the delayed response task (main effect of TIME PRESSURE: F1,16 = 34.63, p = 2.30*10−5, ANOVA details as for reaction times).

Thus, it appears that in the win-shift task, not only do defeated rats commit more errors compared to controls at the shortest delay used, but their performance also appears to asymptote across subsequent increased delay periods.

The ADHD group reported more wrong responses and showed a trend towards more omissions errors compared to the non-ADHD groups.

Therefore, averaging over M overlapped frames will cause more misclassification errors compared to the case when M is 1.

Participants who relied on y-a-h maps or directories took longer to perform the wayfinding and made more circulation errors compared to those who used interactive kiosks.

A within-subject comparison revealed that in the abstinence session, smokers had significantly longer response latencies (in the 2-back condition) and made more overall errors compared to the satiety session.

Through the observation, the following user behaviors were noticed: Participants who relied on y-a-h maps or directories took longer to perform the wayfinding and made more circulation errors compared to those who used interactive kiosks.

Therefore, the spectral power over these 30 frames will be less uniform; the LSFM feature value will then be small, and there will be more classification errors compared to the case when R is 5. Similarly, for factory floor noise, the optimal choice of M is 1 for all SNR levels.