Spatially intermixed objects of different categories are parsed automatically

Khvostov, Vladislav; Lukashevich, Anton; Utochkin, Igor

doi:10.1038/s41598-020-79828-4

Cited by 7 publications

(2 citation statements)

References 59 publications

(77 reference statements)

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“…It is believed that ensemble statistics are extracted within a few milliseconds and beyond the bottleneck of attention and single-object recognition (2,(4)(5)(6)(7)(8)(9). The rapid extraction of ensemble statistics supports fundamental visual functions, such as gist perception (10,11), grouping (12)(13)(14)(15)(16), and visual search (17- 19), but the underlying mechanisms are still debated (3,20). In particular, it remains unclear how the visual system integrates information from local elements dispersed over the entire visual field.…”

Section: Introductionmentioning

confidence: 99%

The functional role of spatial anisotropies in ensemble perception

Tiurina¹,

Markov²,

Whitney³

et al. 2023

Preprint

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The human brain can rapidly represent sets of similar stimuli by their ensemble summary statistics, like the average orientation or size. Classic models assume that ensemble statistics are computed by integrating all elements with equal weight. Challenging this view, here we show that ensemble statistics are estimated by combining parafoveal and foveal statistics in proportion to their reliability. In a series of experiments, observers reproduced the average orientation of an ensemble of stimuli under varying levels of visual uncertainty. Ensemble statistics were affected by multiple spatial biases. In particular, a strong and persistent bias toward the center of the visual field. This bias, evident in the majority of subjects and in all experiments, scaled with uncertainty: the higher the uncertainty in the ensemble statistics the larger the bias towards the element shown at the fovea. Our findings indicate that ensemble perception cannot be explained by simple uniform pooling. The visual system weights information anisotropically from both the parafovea and the fovea, taking the intrinsic spatial anisotropies of vision into account to compensate for visual uncertainty.

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Section: Introductionmentioning

confidence: 99%

The functional role of spatial anisotropies in ensemble perception

Tiurina¹,

Markov²,

Whitney³

et al. 2023

Preprint

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show abstract

“…In contrast, if the distribution of features has only one smooth peak (e.g. the first group is reddish and orangish objects, the second group is yellowish and greenish objects), subset selection becomes a much harder task (Im et al, 2021;Khvostov et al, 2021;Utochkin, 2015;Utochkin et al, 2018;Utochkin & Yurevich, 2016). However, less is known about how various objects intermixed over space are categorized as being members of one or another ensemble.…”

Section: Introductionmentioning

confidence: 99%

What is the Basis of Ensemble Subset Selection?

Khvostov¹,

Iakovlev²,

Wolfe³

et al. 2023

Preprint

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The visual system can rapidly summarize multiple objects in a form of ensemble statistics: e.g., people can easily estimate an average size of apples on a tree. To accomplish this, it is not always enough to summarize all the visual information. If there are various types of objects, the visual system should select a relevant subset: only apples without leaves. Here, we ask: what is the representational basis of ensemble selection, i.e., what kind of visual information makes a ‘good’ ensemble that can be selectively attended to provide an accurate summary estimate? We tested three candidate representations: basic features, preattentive object files, and full-fledged bound objects. In four experiments, we presented a target and several distractors’ sets of differently colored objects. We found that conditions, where a target ensemble had at least one unique color (basic feature), provided ensemble averaging performance comparable to the baseline displays without distractors. When the target subset was defined as a conjunction of two colors or color-shape partly shared with distractors (so that they could be differentiated only as preattentive object files), subset averaging was also possible but less accurate than in the baseline and the feature conditions. Finally, performance was very poor when the target subset was defined by an exact feature relationship, such as in the spatial conjunction of two colors (spatially bound object). Overall, these results suggest that distinguishable features and, to a lesser degree, preattentive object files can serve as the representational basis of ensemble selection, while bound objects cannot.

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