Feature-based attention enhances performance by increasing response gain

Covert spatial attention can increase contrast sensitivity either by changes in contrast gain or by changes in response gain, depending on the size of the attention field and the size of the stimulus (Herrmann et al., 2010), as predicted by the normalization model of attention (Reynolds & Heeger, 2009). For feature-based attention, unlike spatial attention, the model predicts only changes in response gain, regardless of whether the featural extent of the attention field is small or large. To test this prediction, we measured the contrast dependence of feature-based attention. Observers performed an orientation-discrimination task on a spatial array of grating patches. The spatial locations of the gratings were varied randomly so that observers could not attend to specific locations. Feature-based attention was manipulated with a 75% valid and 25% invalid pre-cue, and the featural extent of the attention field was manipulated by introducing uncertainty about the upcoming grating orientation. Performance accuracy was better for valid than for invalid pre-cues, consistent with a change in response gain, when the featural extent of the attention field was small (low uncertainty) or when it was large (high uncertainty) relative to the featural extent of the stimulus. These results for feature-based attention clearly differ from results of analogous experiments with spatial attention, yet both support key predictions of the normalization model of attention.

► The featural extent of the attention field is flexible and can be adjusted according to task demands. ► Accuracy was better for valid than invalid pre-cues, consistent with a change in response gain. ► Response gain whether featural extent of attention field was small or large relative to the stimulus featural extent. ► These results support key predictions of the normalization model of attention.