The phrase "attention derived" is correct and usable in written English.
It can be used in contexts discussing the source or origin of attention, often in academic or analytical writing.
Example: "The attention derived from the media coverage significantly impacted public perception of the event."
Alternatives: "attention gained" or "attention obtained".
Wilde's flamboyant dress sense and craving for attention derived, in part, from his mother's bohemian example.
The measures of visual attention derived from the gap-overlap paradigm within the saccade tasks and the covert spatial attention task assessed the influence of exercise-induced fatigue on attentional factors involved in oculomotor control.
The stock plunged more than 30 per cent on Wednesday as the company revealed it expects full year revenue to fall by up to 7 per cent, citing the "recent media attention" derived from a California bill proposal seeking to end killer whale shows at Sea World.
To be in the limelight, or center of attention, derives from the brilliant lights of the same name.
But mostly, the attention derives from the fact that their outrageously theatrical dancing always becomes the talk of the night.
The benefits of vitamin D have received much attention, deriving primarily from observational data, which suggest that low vitamin D status is associated with higher mortality [ 1, 2].
Our estimates of reductions in older adult repeat falls requiring medical attention derives from a randomized trial of MOB because comparable data are not available for MOB/VLL.
This explanation, as well as the overall data pattern, is by and large compatible with the premotor theory of attention (Rizzolatti et al., 1987; Rizzolatti, Riggio & Sheliga, 1994), which maintains that covert spatial attention derives from the activation of brain maps engaged in sensorimotor transformation processes.
This finding is consistent with attention theories derived from Bayesian theories of brain function (the free-energy principle) that equate spatial attention to a precision-dependent gain modulation of sensory input.
We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data.
The proposed set of features for attention estimation, derived from low-level Kinect features, is described in Section 4.3.
