Sentence examples for are faster to detect from inspiring English sources

Indeed, high anxious subjects are faster to detect deviant faces as suggested by earlier reaction times and P3b component.

For example, alcohol, cannabis, and cigarette users are faster to detect changes to the stimulus corresponding to their habit (Jones, Jones, Smith, & Copley, 2003; Yaxley & Zwaan, 2005), because they attend to these stimuli over other elements in the display.

These action-relevant attentional priorities also translate to change detection, as observers making a precision or a power grasp response are faster to detect changes to grasp-congruent objects (Symes, Tucker, Ellis, Vainio, & Ottoboni, 2008) and change detection for orientation is better when preparing a grasp versus a pointing action (Gutteling, Kenemans, & Neggers, 2011).

Evidence of equivalent competitive effects on emotional biases includes the finding that pathological worriers are faster to detect threatening words in lexical decision tasks than non-anxious controls, but only when two competing target strings (one being a word and one not), are presented together.

Participants were faster to detect exemplar versus category human target faces, but were faster to detect category versus exemplar non-human target faces (Fig. 1B).

At the long 1650 ms SOA, however, participants were faster to detect targets in the uncued hemifield compared to the cued hemifield, showing an IOR effect.

Results showed that the participants administered Arginine Vasopressin nasal spray were faster to detect sexual words over other types of words.

Behaviorally, participants were faster to detect changes in the foreground than in the background and when the visual input included binocular cues.

Analyses of fixation patterns revealed that the participants gave higher search priority to front/near locations and, consistent with this, they were faster to detect color changes in foreground compared with background objects.

The results of the analysis of the detection times (Hit trials only) were consistent with the accuracy data: subjects were faster to detect changes in the foreground compared to changes in the background (F1,19 = 35.0, P < 0.001; Fig. 1C) and were faster with bmC viewing than with two-dimensional (2D) viewing (F1,19 = 5.23, P < 0.05).

The optimum design was fastest to detect MMN and MEG had the best signal-to-noise ratio.