Sentence examples for detection learning from inspiring English sources

The aim of our study was to investigate the performance of trainee colonoscopists in detecting adenomas and to depict the shape of adenoma detection learning curves during apprenticeship.

Nonselective increases in neural responsiveness were observed during detection learning.

At target sites, Figure 9A, neural responses increased from an average of 29.0 imp/sec before detection learning to 42.0 imp/sec after discrimination.

Together, therefore, the results reveal a heretofore-unknown capacity of the human visual system to support the detection, learning, and recognition of higher-order visuospatial associations that are masked from conscious awareness, under conditions where the response locations did not spatially correspond with the locations of the target sequence.

More generally, the results pose a challenge to neural models of learning to account for a previously unknown capacity of the human visual system to support the detection, learning and recognition of higher-order sequential associations under conditions where observers are unable to see the target sequence or perform responses that coincide with structure of the target sequence.

Changes in responsiveness from before detection learning until after discrimination learning were stronger at control locations than at target locations.

The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency.

Although the idea of automatic detection of learning styles is not new, the techniques used are novel.

This system, developed within the scope of the European project RACE, integrates detection, tracking, learning and recognition of tabletop objects.

Notim.press/ed (a clever URL that reads "Not Impressed") lets you enter a URL and instantly see a site's Alexa top websites ranking, sentiment analysis, bounce rate, domain location, and a headline clickbait detection machine learning score.

Yet, the only procedural statute calling for the early detection of learning disabilities in public schools was drafted when the Hispanic population was just twenty-one percent.