Habitual versus goal-driven attention

Abstract

Recent research has expanded the list of factors that control spatial attention. Beside current goals and perceptual salience, statistical learning, reward, motivation and emotion also affect attention. But do these various factors influence spatial attention in the same manner, as suggested by the integrated framework of attention, or do they target different aspects of spatial attention? Here I present evidence that the control of attention may be implemented in two ways. Whereas current goals typically modulate where in space attention is prioritized, search habits affect how one moves attention in space. Using the location probability learning paradigm, I show that a search habit forms when people frequently find a visual search target in one region of space. Attentional cuing by probability learning differs from that by current goals. Probability cuing is implicit and persists long after the probability cue is no longer valid. Whereas explicit goal-driven attention codes space in an environment-centered reference frame, probability cuing is viewer-centered and is insensitive to secondary working memory load and aging. I propose a multi-level framework that separates the source of attentional control from its implementation. Similar to the integrated framework, the multi-level framework considers current goals, perceptual salience, and selection history as major sources of attentional control. However, these factors are implemented in two ways, controlling where spatial attention is allocated and how one shifts attention in space.

Keywords: Implicit learning; Search habit; Spatial attention; Spatial reference frame.

Figure 1

Location probability learning. Left: The training-testing two-phase location probability design. In the training phase the target more often appears in one visual quadrant (the “rich”, high-probability quadrant). In the testing phase, the target’s location is random. Right: Reaction time (RT) becomes faster when the target appears in the high-probability quadrant. This effect persists during the testing phase. Each phase has about 500 trials in the experiment depicted here (adapted from Jiang, Swallow, Rosenbaum, & Herzig, 2013).

Figure 2

Experimental setup for testing the persistence of probability cuing after a viewpoint change. Participants perform the T-among-L task on a display laid flat on a desk. Testing phase RT data (in milliseconds) show evidence for viewer-centered, but not environment-centered, coding of the frequently attended locations (adapted from Jiang & Swallow, 2013b; Twedell, Koutstaal, & Jiang, 2016).

Figure 3

Left. Experimental setup used in the study where people stand at variable viewpoints around a display monitor. Right. An illustration of three trials viewed from above. The green footprint indicates the participants’ standing position on that trial. The 50% region indicates where the target is most often placed from that viewpoint. In this setup, the high-probability quadrant is in the same region of the monitor (away from the “red-wall” landmark; adapted from Jiang, Swallow, & Capistrano, 2013).

Figure 4

The dual-system view of attentional implementation (adapted from Jiang, Swallow, & Capistrano, 2013).