Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism

Maule, John; Franklin, Anna

doi:10.1364/josaa.33.000a22

Cited by 55 publications

(75 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When individual items become organized into an ensemble, they are likely to form a single unit for attention and working memory (Corbett, 2017;Im & Chong, 2014;Im, Park, & Chong, 2015), which means that the quality of ensemble encoding marginally depends on the number of individuals within (Ariely, 2001(Ariely, , 2008Attarha & Moore, 2015;Attarha, Moore, & Vecera, 2014;Chong, Joo, Emmanouil, & Treisman, 2008;Robitaille & Harris, 2011;Utochkin & Tiurina, 2014; but see Marchant, Simons, & De Fockert, 2013;Maule & Franklin, 2016;. However, the number of such ensemble units can be limited.…”

Section: Vwm For Ensembles Vs Objectsmentioning

confidence: 99%

“…We refer the first and the second hypotheses to as sampling and exhaustive encoding, respectively. The experimental distinction between these two hypotheses seems very important for our study, given the debate about sampling vs. exhaustive coding in ensemble perception (Allik, Toom, Raidvee, Averin, & Kreegipuu, 2013;Alvarez, 2011;Ariely, 2008;Chong et al, 2008;Maule & Franklin, 2016;Marchant et al, 2013;Utochkin & Tiurina, 2014).…”

Section: добровольное участие истощение выборки и черты личностиmentioning

confidence: 99%

See 1 more Smart Citation

Untitled

2017

psychology

View full text Add to dashboard Cite

Section: Vwm For Ensembles Vs Objectsmentioning

confidence: 99%

Section: добровольное участие истощение выборки и черты личностиmentioning

confidence: 99%

Untitled

2017

psychology

View full text Add to dashboard Cite

“…An opposite theory argues that ensemble summary representations are supported by a limited-capacity mechanism associated with the bottleneck of focused attention and working memory. 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%

Roles of saliency and set size in ensemble averaging

Iakovlev¹,

Utochkin²

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…For example, Allik, Toom, Raidvec, Averin, & Kreegipuu (2013) suggested that most of the variance in a mean discrimination task could be explained by a simple model taking internal noise and sampling into account. Others have found evidence more consistent with some ''smart'' subsampling strategies (e.g., Marchant, Simons, & de Fockert, 2013;Maule & Franklin, 2016). On the other hand, some findings have provided support for more parallel mechanisms-for example, outliers tend to be discounted in extracting the mean (Haberman & Whitney, 2010), inconsistent with a straightforward random subsampling account; and in pairs of arrays where only a single item changes between arrays, participants can recognize the change in the mean without knowing which item changed (Haberman & Whitney, 2011; see also Ward, Bear, & Scholl, 2016).…”

Section: Introductionmentioning

confidence: 99%

Ensemble Statistics Accessed through Proxies: Range Heuristic and Dependence on Low-Level Properties in Variability Discrimination

Lau¹,

Brady²

2018

Preprint

View full text Add to dashboard Cite

People can quickly and accurately compute not only the mean size of a set of items but also the size variability of the items. However, it remains unknown how these statistics are estimated. Here we show that neither parallel access to all items nor random subsampling of just a few items is sufficient to explain participants' estimations of size variability. In three experiments, we had participants compare two arrays of circles with different variability in their sizes. In the first two experiments, we manipulated the congruency of the range and variance of the arrays. The arrays with congruent range and variability information were judged more accurately, indicating the use of range as a proxy for variability. Experiments 2B and 3 showed that people also are not invariant to low-or mid-level visual information in the arrays, as comparing arrays with different low-level characteristics (filled vs. outlined circles) led to systematic biases. Together, these experiments indicate that range and low-or mid-level properties are both utilized as proxies for variability discrimination, and people are flexible in adopting these strategies. These strategies are at odds with the claim of parallel extraction of ensemble statistics per se and random subsampling strategies previously proposed in the literature.

show abstract

Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism

Cited by 55 publications

References 56 publications

Untitled

Untitled

Roles of saliency and set size in ensemble averaging

Ensemble Statistics Accessed through Proxies: Range Heuristic and Dependence on Low-Level Properties in Variability Discrimination

Contact Info

Product

Resources

About