Evidence for unlimited capacity processing of simple features in visual cortex

Abstract

Performance in many visual tasks is impaired when observers attempt to divide spatial attention across multiple visual field locations. Correspondingly, neuronal response magnitudes in visual cortex are often reduced during divided compared with focused spatial attention. This suggests that early visual cortex is the site of capacity limits, where finite processing resources must be divided among attended stimuli. However, behavioral research demonstrates that not all visual tasks suffer such capacity limits: The costs of divided attention are minimal when the task and stimulus are simple, such as when searching for a target defined by orientation or contrast. To date, however, every neuroimaging study of divided attention has used more complex tasks and found large reductions in response magnitude. We bridged that gap by using functional magnetic resonance imaging to measure responses in the human visual cortex during simple feature detection. The first experiment used a visual search task: Observers detected a low-contrast Gabor patch within one or four potentially relevant locations. The second experiment used a dual-task design, in which observers made independent judgments of Gabor presence in patches of dynamic noise at two locations. In both experiments, blood-oxygen level-dependent (BOLD) signals in the retinotopic cortex were significantly lower for ignored than attended stimuli. However, when observers divided attention between multiple stimuli, BOLD signals were not reliably reduced and behavioral performance was unimpaired. These results suggest that processing of simple features in early visual cortex has unlimited capacity.

Figure 1

Example trial sequence in Experiment 1. The precue could be directed to one of the four visual quadrants (focal) or all four quadrants (distributed). The size of the cue is exaggerated for purpose of demonstration. In this example, a vertical target Gabor is present in the lower right location.

Figure 2

Behavioral (left column) and fMRI (right column) data from Experiment 1. The conditions are illustrated in the legend at the top. “Focal uncued” data are the mean of responses to the three uncued stimuli (for behavior) or the mean response to the stimulus opposite the cued stimulus (for fMRI) in trials with the focal cue; “focal cued” data are the mean response to the single cued stimulus in the same trials; “distributed cued” data are the mean of responses to all four stimuli in distributed cue trials. (A) Behavioral accuracy in units of area under the ROC curve (A_g). (B) Mean BOLD responses in area V1 in each attention condition. (C) Differences in behavioral A_g between two pairs of conditions. (D) Differences in V1 BOLD responses between the same two pairs of conditions. All error bars = ±1 SEM. Solid points = data; open points = model predictions for divided attention under the all-or-none switching model (see text).

Figure 3

Example trial sequence in Experiment 2. In this example, the blue cues indicate potential target locations. A target Gabor is present in the noise movie on the right.

Figure 4

Behavioral (left column) and fMRI (right column) data from Experiment 2. The conditions referred to in the plots are illustrated in the legend at the top. (A) Behavioral accuracy (d′) in each cue condition. (B) BOLD responses in a combined ROI that encompasses all of V1–V4. (C) Differences in d′ between two pairs of conditions. (D) Differences in BOLD responses between the same pairs of conditions in the combined V1–V4 ROI. Solid points are data; open points are predictions of the switching model for the distributed cue condition. All error bars indicate ±1 SEM.

Figure 5

BOLD data from Experiment 2 in individual retinotopic areas. (A) Mean BOLD responses in each cue condition, with a separate panel for each retinotopic area. (B) Mean differences in BOLD responses between two pairs of conditions, in each retinotopic area. Solid points are data; open points are predictions of the switching model for the divided attention condition. All error bars indicate ±1 SEM.