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

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 06 Jul 2018 United States Publisher:Center for Open ScienceJournal:Journal of Vision, volume 18, page 3 (issn: 1534-7362, Copyright policy )
Authors: Brady , Timothy; Lau , Jonas; Brady, Timothy;

doi: 10.31234/osf.io/c2av9 , 10.1167/18.9.3

pmid: 30193345

pmc: PMC6126932

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

Abstract

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 2 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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Average
Top 10%
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gold
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