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.
•We studied crowding with unlimited viewing duration and with unrestricted eye movements.•Stimulus duration beyond 250ms had little effect on the spatial extent of crowding.•Unrestricted eye ...movements caused a large variability in crowding extent.•Crowding extent with eye movements was dependent on the effective eccentricity of the target.•Unlimited amount of viewing time and unrestricted eye movements do not substantially reduce crowding in peripheral vision.
Crowding impairs the perception of form in peripheral vision. It is likely to be a key limiting factor of form vision in patients without central vision. Crowding has been extensively studied in normally sighted individuals, typically with a stimulus duration of a few hundred milliseconds to avoid eye movements. These restricted testing conditions do not reflect the natural behavior of a patient with central field loss. Could unlimited stimulus duration and unrestricted eye movements change the properties of crowding in any fundamental way? We studied letter identification in the peripheral vision of normally sighted observers in three conditions: (i) a fixation condition with a brief stimulus presentation of 250ms, (ii) another fixation condition but with an unlimited viewing time, and (iii) an unrestricted eye movement condition with an artificial central scotoma and an unlimited viewing time. In all conditions, contrast thresholds were measured as a function of target-to-flanker spacing, from which we estimated the spatial extent of crowding in terms of critical spacing. We found that presentation duration beyond 250ms had little effect on critical spacing with stable gaze. With unrestricted eye movements and a simulated central scotoma, we found a large variability in critical spacing across observers, but more importantly, the variability in critical spacing was well correlated with the variability in target eccentricity. Our results assure that the large body of findings on crowding made with briefly presented stimuli remains relevant to conditions where viewing time is unconstrained. Our results further suggest that impaired oculomotor control associated with central vision loss can confound peripheral form vision beyond the limits imposed by crowding.
Classification images with uncertainty Tjan, Bosco S; Nandy, Anirvan S
Journal of vision (Charlottesville, Va.),
04/2006, Letnik:
6, Številka:
4
Journal Article
Recenzirano
Odprti dostop
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.
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.
Visual crowding is an ubiquitous limitation of peripheral vision and manifests itself as the marked inability to identify shapes when targets are flanked by other objects. It presents a fundamental ...bottleneck to object recognition in peripheral vision. Although the phenomenon has been widely studied over the last four decades, the neural mechanisms underlying crowding remain unsettled. Such an understanding is critical for the development of visual enhancement aids for patients with central field loss. Here we first investigate the nature of form vision deficits in the periphery through a series of psychophysical experiments. We develop a novel method of classification images to overcome the intrinsic spatial uncertainty in the periphery (Tjan and Nandy, 2006), and show that the perceptual templates utilized in the periphery are undistorted. By using higher order reverse correlation analysis, we show that the form of flanking objects greatly influence target recognition errors under crowding and that crowding is associated with an inefficient selection and usage of low-level features (Nandy and Tjan, 2007). We further show that feature integration across spatial frequency channels in optimal (for letter identification) in both central and peripheral vision (Nandy and Tjan, 2008). We next develop a unified model of visual crowding. Our theory, guided by empirical findings, including the ones mentioned above, views crowding as a necessary consequence of gathering image statistics during eye movements. We show that any temporal overlap between spatial attention in the peripheral visual field that precedes a saccadic eye movement and the motion blur due to the subsequent saccade can cause a misrepresentation of image statistics in peripheral V1, where saccadic suppression is weak. We demonstrate with simulations that the strength and shape of long-range horizontal connections formed under such conditions quantitatively explain the three hallmark signatures of crowding: (a) the spatial extent of crowding scales linearly with eccentricity (Bouma, 1970); (b) crowding is asymmetric with respect to the target (Bouma, 1973; Petrov et al., 2007); and (c) the zone of crowding is anisotropic (Toet and Levi, 1992). The model provides a basis for understanding specific target-flanker interactions at the feature level and makes predictions about cortical reorganization in patients with central field loss.
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