AUTHOR=Wegener Detlef , Galashan Fingal Orlando , Aurich Maike Kathrin , Kreiter Andreas Kurt TITLE=Attentional spreading to task-irrelevant object features: experimental support and a 3-step model of attention for object-based selection and feature-based processing modulation JOURNAL=Frontiers in Human Neuroscience VOLUME=8 YEAR=2014 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2014.00414 DOI=10.3389/fnhum.2014.00414 ISSN=1662-5161 ABSTRACT=

Directing attention to a specific feature of an object has been linked to different forms of attentional modulation. Object-based attention theory founds on the finding that even task-irrelevant features at the selected object are subject to attentional modulation, while feature-based attention theory proposes a global processing benefit for the selected feature even at other objects. Most studies investigated either the one or the other form of attention, leaving open the possibility that both object- and feature-specific attentional effects do occur at the same time and may just represent two sides of a single attention system. We here investigate this issue by testing attentional spreading within and across objects, using reaction time (RT) measurements to changes of attended and unattended features on both attended and unattended objects. We asked subjects to report color and speed changes occurring on one of two overlapping random dot patterns (RDPs), presented at the center of gaze. The key property of the stimulation was that only one of the features (e.g., motion direction) was unique for each object, whereas the other feature (e.g., color) was shared by both. The results of two experiments show that co-selection of unattended features even occurs when those features have no means for selecting the object. At the same time, they demonstrate that this processing benefit is not restricted to the selected object but spreads to the task-irrelevant one. We conceptualize these findings by a 3-step model of attention that assumes a task-dependent top-down gain, object-specific feature selection based on task- and binding characteristics, and a global feature-specific processing enhancement. The model allows for the unification of a vast amount of experimental results into a single model, and makes various experimentally testable predictions for the interaction of object- and feature-specific processes.