Summary statistics for size over space and time

Goréa, Andrei; Belkoura, Seddik; Solomon, Joshua A.

doi:10.1167/14.9.22

Cited by 57 publications

(69 citation statements)

References 37 publications

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“…Our results for effective sample size are lower than the majority of studies reporting samples for averaging size33 and motion31, however Manning et al 34. have observed sampling efficiencies at or below one for motion in children.…”

Section: Discussioncontrasting

confidence: 86%

“…Estimates of subsampling vary across the literature and depend on the total number of samples in the array, the stimulus property being averaged as well as stimulus presentation duration33. Estimates range from as many as 80 samples in densely populated arrays of 1024 oriented Gabors30 to as low as just 1 out of 100 for motion direction integration in young children34.…”

Section: Introductionmentioning

confidence: 99%

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Spatial limitations in averaging social cues

Florey

Clifford

Dakin

et al. 2016

Sci Rep

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The direction of social attention from groups provides stronger cueing than from an individual. It has previously been shown that both basic visual features such as size or orientation and more complex features such as face emotion and identity can be averaged across multiple elements. Here we used an equivalent noise procedure to compare observers’ ability to average social cues with their averaging of a non-social cue. Estimates of observers’ internal noise (uncertainty associated with processing any individual) and sample-size (the effective number of gaze-directions pooled) were derived by fitting equivalent noise functions to discrimination thresholds. We also used reverse correlation analysis to estimate the spatial distribution of samples used by participants. Averaging of head-rotation and cone-rotation was less noisy and more efficient than averaging of gaze direction, though presenting only the eye region of faces at a larger size improved gaze averaging performance. The reverse correlation analysis revealed greater sampling areas for head rotation compared to gaze. We attribute these differences in averaging between gaze and head cues to poorer visual processing of faces in the periphery. The similarity between head and cone averaging are examined within the framework of a general mechanism for averaging of object rotation.

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Section: Discussioncontrasting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Spatial limitations in averaging social cues

Florey

Clifford

Dakin

et al. 2016

Sci Rep

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“…This theory suggests that only a small subset of items is sampled and integrated to accomplish proxy statistics for the entire ensemble (Allik, Toom, Raidvee, Averin, & Kreegipuu, 2013;Maule & Franklin, 2016;Solomon, 2010). Based on different model estimates, it was suggested that the visual system might integrate a fixed number of items (Maule & Franklin, 2016; or a flexible number of items depending on the number of observed items like the square root of presented items (Dakin, 2001;Gorea, Belkoura, & Solomon, 2014;Solomon, 2010;Whitney & Yamanashi Leib, 2018) or about a half of them (Allik et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, according to Kanaya et al (2018), amplification can have to do with the efficiently integrated set size that grows slower than the presented set size (~√N, Whitney & Yamanashi Leib, 2018). Adopting the attentional nature of amplification suggested by Kanaya et al (2018), this "√N account" appears very consonant with the limited capacity of attentional processing (Gorea et al, 2014;Pylyshyn & Storm, 1988;Scholl, 2001). Does this mean that people pick the √N items and that saliency determines which particular items are picked?…”

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confidence: 99%

Roles of saliency and set size in ensemble averaging

Iakovlev¹,

Utochkin²

2020

Preprint

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Ensemble statistics are often thought of as a reliable impression of numerous items despite limited capacities to consciously represent each individual. However, whether all items equally contribute to ensemble summaries (e.g., mean) and whether they might be provided by known limited-capacity processes, such as attention, is still debated. We addressed these questions via a recently described "amplification effect", a systematic bias of perceived mean (e.g., average size) towards the more salient "tail" of a feature distribution (e.g., larger items). In our experiments, observers adjusted the mean orientation of sets of items varying in set size. We made some of the items more salient or less salient by changing their size. While the whole orientation distribution was fixed, the more salient subset could be shifted relative to the set mean or differ in range. We measured the bias away from the set mean and the standard deviation (SD) of errors, as it is known to reflect the physical range from which ensemble information is sampled. We found that bias and SD changes followed the shifts and range changes in salient subsets, providing evidence for amplification. However, these changes were weaker than those expected from sampling only salient items, suggesting that less salient items were also sampled. Importantly, the SD decreased as a function of set size, which is only possible if the number of sampled elements increased with set size. Overall, we conclude that orientation summary statistics are integrated by a high-capacity mechanism modulated by the amplification effect of attention.

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“…For example, when presented with two sets, containing (N = 8) circles each, human observers can select the set whose circles have the larger average diameter as well as the otherwise-ideal observer that perfectly measures the diameters of M = 5 randomly selected circles in each set (Solomon, Morgan, & Chubb, 2011;Gorea, Belkoura, & Solomon, 2014). Thus, we can say that the efficiency of size averaging can be as high as 5/8 or 62.5%.…”

Section: Introductionmentioning

confidence: 99%

Orientation-defined boundaries are detected with low efficiency

Solomon

Morgan

2017

Vision Research

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This is the accepted version of the paper.This version of the publication may differ from the final published version. they use information from no more than two or three items. We hypothesised that observers would attain greater sampling efficiency when their task was to perform a texture segmentation rather than a did not require an explicit representation of mean orientation. We tested this hypothesis using a texture-segmentation task. Two arrays of 32 wavelets each were presented; one left and one right of fixation. Orientations in the target array were sampled from wrapped normal distributions having two different means with the same variance. One distribution defined orientations above the horizontal meridian, the other defined orientations below the meridian. All orientations in the other array were defined by a single wrapped normal distribution having the same variance as each of the distributions in the target array. Contrary to our hypothesis, results indicate that observers effectively ignored all but one item from the top and bottom of each array. In fact, we found no change in the threshold difference between the target's two means when all but one item from the top and bottom of each array were removed. We are forced to conclude that the visual system does not compute the average of more than a few orientations, even for texture segmentation. Permanent repository link

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Summary statistics for size over space and time

Cited by 57 publications

References 37 publications

Spatial limitations in averaging social cues

Spatial limitations in averaging social cues

Roles of saliency and set size in ensemble averaging

Orientation-defined boundaries are detected with low efficiency

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