A didactic advantage is the high-level error messages generated by the compiler.
Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors.
Algorithm-based fault tolerance (ABFT) was first introduced by Huang and Abraham [1] and was directed toward detection of high-level errors because of internal processing failures.
The higher level of error correction represents the QR tag which has the ability to resist larger damage.
The former scenario corresponds to what has been termed involuntary orienting of attention, indicated by the P3a, a higher level prediction error response (e.g. Escera et al., 2000).
In conclusion, it is worthwhile examining a fuller range of sample sizes when using exact methods for single-stage phase II trials, so that the smallest acceptable sample size could be chosen after allowing a slightly higher α level (error rate) than the conventional 5 or 10%, and lower power than the nominal 80%.
Modified yes-no task: The repeated presentation of new, untagged faces alongside tagged faces instead of a unique presentation of each face in early studies may have lead to a slightly higher level of errors, but trial repetition conferred robustness to the averaged accuracy values reported here.
Thus, the higher level of errors biased toward nontargets in the same-location condition occurred with a corresponding decrease in reporting around the target item.
Instead, we relied on available commercial data, subject to higher levels of error.
The interpretation of phenolic data was less clear, partly because of the higher levels of error in the model as discussed above.
Additionally, the size of a district was observed to have an important impact on survey performance, with districts with an area greater than ∼1,500 km having higher levels of error than those with smaller areas.
