Stimulus competition mediates the joint effects of spatial and feature-based attention

Abstract

Distinct attentional mechanisms enhance the sensory processing of visual stimuli that appear at task-relevant locations and have task-relevant features. We used a combination of psychophysics and computational modeling to investigate how these two types of attention--spatial and feature based--interact to modulate sensitivity when combined in one task. Observers monitored overlapping groups of dots for a target change in color saturation, which they had to localize as being in the upper or lower visual hemifield. Pre-cues indicated the target's most likely location (left/right), color (red/green), or both location and color. We measured sensitivity (d') for every combination of the location cue and the color cue, each of which could be valid, neutral, or invalid. When three competing saturation changes occurred simultaneously with the target change, there was a clear interaction: The spatial cueing effect was strongest for the cued color, and the color cueing effect was strongest at the cued location. In a second experiment, only the target dot group changed saturation, such that stimulus competition was low. The resulting cueing effects were statistically independent and additive: The color cueing effect was equally strong at attended and unattended locations. We account for these data with a computational model in which spatial and feature-based attention independently modulate the gain of sensory responses, consistent with measurements of cortical activity. Multiple responses then compete via divisive normalization. Sufficient competition creates interactions between the two cueing effects, although the attentional systems are themselves independent. This model helps reconcile seemingly disparate behavioral and physiological findings.

Figure 1

Stimuli and design. (a) Example dot stimuli for Experiment 1, with four simultaneous saturation increments and (b) for Experiment 2, with a single target saturation increment in the upper right red dots. (c) Trial sequence. Numbers below each segment indicate duration in milliseconds. (d) The cues presented at fixation. (e) Proportions of trials in each cue-validity condition.

Figure 2

Results from Experiment 1 (left column) and Experiment 2 (right column). (a, c) Average d′ levels. Yellow points are model fits. (b, d) Mean reaction times on correct trials (RTs). Error bars indicate ± one within-subject SEM (Morey, 2008).

Figure 3

Data from both experiments divided by whether the target occurred adjacent to the horizontal boundary (“inner segments”; left column) or in the upper- or lower-most segments of the dot field (“outer segments”; right column). Error bars indicate ± one within-subject SEM.

Figure 4

Diagram of the putative visual processing stages formalized in the ISC model. Stimulus inputs to the model are illustrated on the left. In this example, the red dots on the right are cued. In the first panel, the lengths of the colored bars represent the encoded saturation levels r_ij for each of the top (j = 1) and bottom (j = 2) halves all four dot fields (i). Note that dots with physically incremented saturation have longer bars (larger r), red bars are longer than green bars, and, independently, bars on the right are longer than bars on the left. The middle panel represents the stimulus representations after they have been exponentiated and normalized by each other. The final panel illustrates how the model makes a decision about the target dot field (q), with additive noise (represented by the spread of the distribution of x_q) limiting accuracy. See text for details.