Dissociable effects of attention and crowding on orientation averaging

Abstract

It has been proposed that visual crowding-the breakdown in recognition that occurs when objects are presented in cluttered scenes-reflects a limit imposed by visual attention. We examined this idea in the context of an orientation averaging task, having subjects judge the mean orientation of a set of oriented signal elements either in isolation, or "crowded" by nearby randomly oriented elements. In some conditions, subjects also had to perform an attentionally demanding secondary task. By measuring performance at different levels of signal orientation variability, we show that crowding increases subjects' local uncertainty (about the orientation of individual elements) but that diverting attention reduces their global efficiency (the effective number of elements they can average over). Furthermore, performance with the same stimulus-sequence, presented multiple times, reveals that crowding does not induce more stimulus-independent variability (as would be predicted by some accounts based on attention). We conclude that crowding and attentional load have dissociable perceptual consequences for orientation averaging, suggesting distinct neural mechanisms for both. For the task we examined, attention can modulate the effects of crowding by changing the efficiency with which information is analyzed by the visual system but since crowding changes local uncertainty, not efficiency, crowding does not reflect an attentional limit.

Figure 1

While fixating the red marker in (a), details of individual birds are difficult to make out. Now try fixating the marker in (b) and attending to a pair of the same birds presented in isolation. Much more information about the birds should now be apparent. This disruptive effect of “clutter” on object recognition in the periphery is known as visual crowding.

Figure 2

Noise paradigm for quantifying limits of local and global processing of orientation. (a) Stimuli contain a Gaussian range of orientations, and subjects judge whether the overall orientation is clockwise or anti-clockwise (in this case, clockwise) of a reference orientation (here, vertical). The inset to the lower-right shows a model observer limited by the local precision with which it can estimate the orientation of each element (σ_loc) and by the global number of samples it can average (N_glo). (b) Sample psychometric function collected with various offsets of the mean orientation; the slope of this function is our performance estimate. (c) Such noise experiments involve estimating such thresholds as a function of the range of orientations present. Data can then be modeled (using the boxed equation) to yield estimates of local and global limits on performance. The two dashed lines show the signatures of poorer local or global processing.

Figure 3

(a–d) examples of the experimental stimuli. (a) Target patches have an orientation s.d. of 1° and a mean orientation of −4° (counter-clockwise from vertical). (b) As (a) with an orientation s.d. of 16° and a mean tilt +20°. (c, d) Similar to (a) and (b) respectively, but with the addition of randomly oriented distractor elements.

Figure 4

Comparison of the effects of crowding and attentional load on judgment of mean orientation. (a–c) Three subjects’ mean-orientation discrimination thresholds are plotted as a function of the orientation variability of the stimulus, under four experimental conditions (indicated by the legend). Fits of the noise model to the crowded (dashed lines) and attentional-load conditions (thicker lines) are also shown with corresponding estimates of local noise (s) and global sampling (n) listed in the legend box. Note that the addition of attentional load, to crowded or uncrowded conditions, has the effect of shifting data upward on log-log axes. Crowding, by contrast selectively impairs performance only at low levels of orientation variability. (d–g) Summarizes these findings for all observers. Crowding reduces subjects’ local precision i.e. at estimating the orientation of individual elements, while attentional load reduces the number of elements they effectively pool to make their judgment.

Figure 5

(a, b) Plot of percent correct performance, for two runs with the same (uncrowded) stimuli, against the degree of agreement between runs. (c, d) Same for crowded conditions. The fit-lines in (a–d) are a model from Gold et al. (2004) (dashed lines are 95% confidence intervals for the fit). (e, f) By comparing the distribution of best-fitting slopes, using a bootstrapped data set, one can see that the pattern of percent correct versus percent-agreement is essentially similar with or without crowding. Crowding does not induce subjects to make more stimulus-independent random errors.

Figure 6

The height of the blue histogram indicates the number of target + flanker combinations (from a sample of 128) that induced a particular level of performance (x-axis). Results are based on the pooled responses of two observers each conducting 20 identical runs. The heavy red line shows the expected number of stimuli that should elicit given levels of proportion correct, if agreement across runs is based solely on binomial probability. The red shaded regions show the 99.9% (i.e. 0.00005 & 0.00095) confidence interval on this estimate. The green shaded region indicate 15 stimuli that consistently produce performance lower than 45% across all 40 runs, an extraordinarily unlikely eventuality if crowding simply made subjects’ responses more variable. The two inset stimuli both contain targets generated to contain stimuli with an average clockwise tilt of 3.12°; subjects correctly judged the orientation of the target elements in the upper stimulus on 40/40 presentations, but in the lower stimulus on only 1/40 presentations (i.e. subjects consistently saw the target elements of the lower stimulus as being anti-clockwise of vertical).