SUMMARY

Processing of shape information in human peripheral visual fields is impeded beyond what can be expected by poorer spatial resolution. Visual crowding—the inability to identify objects in clutter—has been shown to be the primary factor limiting shape perception in peripheral vision. Despite the well documented effects of crowding, its underlying causes are poorly understood. Since spatial attention both facilitates learning of image statistics and directs saccadic eye movements, we propose that the acquisition of image statistics in peripheral visual fields is confounded by eye-movement artifacts. Specifically, the image statistics acquired under a peripherally deployed spotlight of attention is systematically biased by saccade-induced image displacements. These erroneously represented image statistics lead to inappropriate contextual interactions in the periphery and cause crowding.

Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a “haze” that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms.

Visual crowding refers to the marked inability to identify an otherwise perfectly identifiable object when it is flanked by other objects. Crowding places a significant limit on form vision in the visual periphery; its mechanism is, however, unknown. Building on the method of signal-clamped classification images (Tjan & Nandy, 2006), we developed a series of first- and second-order classification-image techniques to investigate the nature of crowding without presupposing any model of crowding. Using an “o” versus “x” letter-identification task, we found that (1) crowding significantly reduced the contrast of first-order classification images, although it did not alter the shape of the classification images; (2) response errors during crowding were strongly correlated with the spatial structures of the flankers that resembled those of the erroneously perceived targets; (3) crowding had no systematic effect on intrinsic spatial uncertainty of an observer nor did it suppress feature detection; and (4) analysis of the second-order classification images revealed that crowding reduced the amount of valid features used by the visual system and, at the same time, increased the amount of invalid features used. Our findings strongly support the feature-mislocalization or source-confusion hypothesis as one of the proximal contributors of crowding. Our data also agree with the inappropriate feature-integration account with the requirement that feature integration be a competitive process. However, the feature-masking account and a front-end version of the spatial attention account of crowding are not supported by our data.

Objects in natural scenes are spatially broadband; in contrast, feature detectors in the early stages of visual processing are narrowly tuned in spatial frequency. Earlier studies of feature integration using gratings suggested that integration across spatial frequencies is suboptimal. Here we re-examined this conclusion using a letter identification task at the fovea and at 10 deg in the lower visual field. We found that integration across narrow-band (1-octave) spatial frequency components of letter stimuli is optimal in the fovea. Surprisingly, this optimality is preserved in the periphery, even though feature integration is known to be deficient in the periphery from studies of other form-vision tasks such as crowding. A model that is otherwise a white-noise ideal observer except for a limited spatial resolution defined by the human contrast sensitivity function and using internal templates slightly wider in bandwidth than the stimuli is able to account for the human data. Our findings suggest that deficiency in feature integration found in peripheral vision is not across spatial frequencies.

Previous work has shown differential amygdala response to African-American faces by Caucasian individuals. Furthermore, behavioral studies have demonstrated the existence of skin tone bias, the tendency to prefer light skin to dark skin. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate whether skin tone bias moderates differential race-related amygdala activity. Eleven White participants viewed photographs of unfamiliar Black and White faces with varied skin tone (light, dark). Replicating past research, greater amygdala activity was observed for Black faces than White faces. Furthermore, dark-skinned targets elicited more amygdala activity than light-skinned targets. However, these results were qualified by a significant interaction between race and skin tone, such that amygdala activity was observed at equivalent levels for light- and dark-skinned Black targets, but dark-skinned White targets elicited greater amygdala activity than light-skinned White targets.