Attention Modifies Spatial Resolution According to Task Demands

Abstract

How does visual attention affect spatial resolution? In texture-segmentation tasks, exogenous (involuntary) attention automatically increases resolution at the attended location, which improves performance where resolution is too low (at the periphery) but impairs performance where resolution is already too high (at central locations). Conversely, endogenous (voluntary) attention improves performance at all eccentricities, which suggests a more flexible mechanism. Here, using selective adaptation to spatial frequency, we investigated the mechanism by which endogenous attention benefits performance in resolution tasks. Participants detected a texture target that could appear at several eccentricities. Adapting to high or low spatial frequencies selectively affected performance in a manner consistent with changes in resolution. Moreover, adapting to high, but not low, frequencies mitigated the attentional benefit at central locations where resolution was too high; this shows that attention can improve performance by decreasing resolution. Altogether, our results indicate that endogenous attention benefits performance by modulating the contribution of high-frequency information in order to flexibly adjust spatial resolution according to task demands.

Keywords: adaptation; attention; spatial frequency; spatial resolution; texture segmentation.

Fig. 1.

Typical performance on a texture-segmentation task. The task is to detect a target (a patch of oriented lines) within a larger array of differently oriented lines. Performance peaks at the target eccentricity where resolution matches the scale of the texture and drops where resolution is too low (at peripheral locations) or too high (at central locations). The decrease in performance at central locations is known as the central performance drop (CPD).

Fig. 2.

Experimental procedure and design. An example trial sequence from the yes/no detection task is presented in (a). Each block began with a 180-s adaptation period, and each trial started with a 4-s top-up. This was followed by a fixation interval, after which a precue appeared. The precue could be either neutral (no information given regarding the target location) or valid (a diagonal line and a number from 0–3 indicating, respectively, the diagonal meridian and eccentricity of the target location). The precue was followed by an interstimulus interval (ISI) and then a texture display, in which a target patch could appear at the cued location. Regardless of whether a trial was neutral or valid, a response cue appeared after the texture display to indicate the relevant location where the target patch would have been, if present. The mean orientation of the target patch (±45º) was always orthogonal to the mean orientation of the background lines (±45°). Task difficulty was controlled by varying the orientation bandwidth of the texture (i.e., the orientation range of the uniform distribution from which the orientation of each line was randomly selected). The target patch, which appeared on 50% of both neutral and valid trials, could appear at any of 25 possible locations (b), at seven possible eccentricities along the diagonal meridians. The seven eccentricities were presented in two sessions, with only 0° eccentricity overlapping between sessions. Texture-defined second-order adaptors (c) consisted of a carrier noise (6 cycles/deg, or cpd) that was either unmodulated (baseline) or modulated in contrast by a vertical grating of low or high spatial frequency (SF). The images in this figure were altered to improve visibility.

Fig. 3.

Schematic predictions of spatial-frequency (SF) adaptation effects on (a) texture segmentation and (b) how SF adaptation interacts with attention. Relative to a baseline condition, high-SF adaptation should reduce the central performance drop (CPD) and shift peak performance closer to the fovea (a). Conversely, low-SF adaptation should accentuate the CPD and shift peak performance toward the periphery. Adaptation should be stronger at central locations and fade with eccentricity because of reduced sensitivity to the adaptors at peripheral locations. The bar graphs illustrate predicted changes in peak eccentricity and CPD in each adaptation condition. The graphs in (b) illustrate the expected effects of low-SF, baseline, and high-SF adaptation on attention according to each of three hypothesized attention mechanisms. Endogenous attention could decrease resolution to benefit performance at the CPD via two mechanisms: by enhancing the sensitivity of low-SF filters (Hypothesis 1) or by reducing the sensitivity of high-SF filters (Hypothesis 2). Alternatively, attention could benefit performance without affecting resolution (e.g., by improving the signal-to-noise ratio at all eccentricities) and would not interact with adaptation (Hypothesis 3). The bar graphs (right-most column) show the predicted pattern of attentional benefits for the three adaptation conditions, which would differ for the three hypotheses. In both panels, the dashed vertical lines highlight the eccentricity at which peak performance would be expected.

Fig. 4.

Effects of spatial-frequency (SF) adaptation in the neutral attention condition. Mean performance (a) is shown at each of the seven target eccentricities as a function of adaptation condition. For each condition, the curves correspond to the best-fitting second-order polynomials (all R²s > .9), and the dashed vertical lines highlight the eccentricity at which the peak performance occurred. Estimates for mean peak eccentricity (b) and mean central performance drop (c) are shown as a function of adaptation condition. Symbols indicate significant (*p < .05, **p < .01, ***p < .001) and marginal (^†p < .10) differences between conditions. Error bars for means represent ±1 SEM within subjects (Morey, 2008), and error bars for the differences between condition means represent ±1 SEM.

Fig. 5.

Effects of attention on performance as a function of spatial-frequency (SF) adaptation. Mean performance at each of the seven target eccentricities is shown as a function of trial type, separately for (a) the low-SF adaptation condition, (b) the baseline adaptation condition, and (c) the high-SF adaptation condition. For each condition, the curves correspond to the best-fitting second-order polynomials (all R²s > .9), and the dashed vertical lines highlight the eccentricity at which the peak performance occurred. Change in performance between valid and neutral trials (d) is shown as a function of adaptation condition. The asterisks indicate significant differences between conditions (p < .05). Error bars for means represent ±1 SEM within subjects (Morey, 2008), and error bars for the differences between condition means represent ±1 SEM.