Independent and parallel visual processing of ensemble statistics: Evidence from dual tasks

Khvostov, Vladislav; Utochkin, Igor

doi:10.31234/osf.io/k94hs

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…The results of this experiment, of Experiments 2–5, and of previous literature (e.g., Khayat & Hochstein, 2018 ) all demonstrate that participants are not only sensitive to the mean feature value of ensemble displays, but that they are also sensitive to the range. Furthermore, Khvostov and Utochkin ( 2019 ) suggest that the processing of ensemble mean and range are mediated by independent cognitive mechanisms, whereas the results of Experiment 5 in the present study suggest that they interact and are not independent. It is possible that we observed this interaction because the numerical value of the mean was equal to the center of the range (i.e., the centroid) in Experiment 5 .…”

Section: Discussioncontrasting

confidence: 74%

“…In contrast, Khvostov and Utochkin ( 2019 ) have suggested that the mean and the range of an ensemble are processed by independent cognitive mechanisms. To explain this discrepancy, we highlight some important methodological differences between our studies.…”

Section: Discussionmentioning

confidence: 96%

“…This raises the possibility that the range and mean arise from the same summary metric. Previous research has noted this problem when studying the range and mean of ensemble stimuli (Hochstein et al, 2018 ; Khvostov & Utochkin, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range

Sama

Srikanthan

Nestor

et al. 2021

Atten Percept Psychophys

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Through ensemble encoding, the visual system compresses redundant statistical properties from multiple items into a single summary metric (e.g., average size). Numerous studies have shown that global summary information is extracted quickly, does not require access to single-item representations, and often interferes with reports of single items from the set. Yet a thorough understanding of ensemble processing would benefit from a more extensive investigation at the local level. Thus, the purpose of this study was to provide a more critical inspection of global-local processing in ensemble perception. Taking inspiration from Navon (Cognitive Psychology, 9(3), 353-383, 1977), we employed a novel paradigm that independently manipulates the degree of interference at the global (mean) or local (single item) level of the ensemble. Initial results were consistent with reciprocal interference between global and local ensemble processing. However, further testing revealed that local interference effects were better explained by interference from another summary statistic, the range of the set. Furthermore, participants were unable to disambiguate single items from the ensemble display from other items that were within the ensemble range but, critically, were not actually present in the ensemble. Thus, it appears that local item values are likely inferred based on their relationship to higher-order summary statistics such as the range and the mean. These results conflict with claims that local information is captured alongside global information in summary representations. In such studies, successful identification of set members was not compared with misidentification of items within the range, but which were nevertheless not presented within the set.

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Section: Discussioncontrasting

confidence: 74%

Section: Discussionmentioning

confidence: 96%

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Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range

Sama

Srikanthan

Nestor

et al. 2021

Atten Percept Psychophys

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“…It has been previously demonstrated that people can efficiently extract various statistical information form briefly presented multiple items. Summary statistics such as mean and variance can be extracted about various features of individual items from basic sensory dimensions, like orientation (Alvarez & Oliva, 2009;Dakin & Watt, 1997;Morgan, Chubb, & Solomon, 2008;Parkes, Lund, Angelucci, Solomon, & Morgan, 2001;Suárez-Pinilla, Seth, & Roseboom, 2018), size (Ariely, 2001;Chong & Treisman, 2003;Khvostov & Utochkin, 2019;Tokita, Ueda, & Ishiguchi, 2016), color (Bronfman, Brezis, Jacobson, & Usher, 2014;Gardelle & Summerfield, 2011;Maule & Franklin, 2015), to quite complex and high-level dimensions, like facial expression (Haberman, Lee, & Whitney, 2015;Haberman & Whitney, 2007) or animacy (Leib, Kosovicheva, & Whitney, 2016). Interestingly, the efficiency and accuracy of such ensemble representation does not suffer (Ariely, 2001;Chong & Treisman, 2005;Fouriezos, Rubenfeld, & Capstick, 2008;Haberman, Harp, & Whitney, 2009; or even benefits (Chong, Joo, Emmmanouil, & Treisman, 2008;Robitaille & Harris, 2011) from increasing set size, whereas our ability to report individual items quickly degrades with set size (Ariely, 2001;Haberman & Whitney, 2007).…”

Section: Introductionmentioning

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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Independent and parallel visual processing of ensemble statistics: Evidence from dual tasks

Cited by 2 publications

References 13 publications

Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range

Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range

Roles of saliency and set size in ensemble averaging

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