The role of uncertainty in perceptual organization

Zhou, Yanli; Acerbi, Luigi; Ji, Wei

doi:10.1167/17.10.746

Cited by 2 publications

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“…Some studies suggest that BDT is generally a good descriptive model of people's perceptual and motor performance, but quantitative comparison shows divergence from Bayes-optimal behavior ( Bejjanki et al, 2016 ; Zhou, Acerbi, & Ma, 2018 ), not unlike what we report in this study. These deviations from optimality may have arisen because rather than performing the complex computations that a typical Bayesian observer would do, observers draw on simpler non-Bayesian, perhaps even non-probabilistic, heuristics ( Gigerenzer & Gaissmaier, 2011 ; Zhou et al, 2018 ). Laquitaine and Gardner (2018) developed a model that switched between the prior and sensory information, instead of combining the two, which was found to explain the data better than standard Bayesian models.…”

Section: Discussioncontrasting

confidence: 94%

Bayesian transfer in a complex spatial localization task

et al. 2020

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Prior knowledge can help observers in various situations. Adults can simultaneously learn two location priors and integrate these with sensory information to locate hidden objects. Importantly, observers weight prior and sensory (likelihood) information differently depending on their respective reliabilities, in line with principles of Bayesian inference. Yet, there is limited evidence that observers actually perform Bayesian inference, rather than a heuristic, such as forming a look-up table. To distinguish these possibilities, we ask whether previously learned priors will be immediately integrated with a new, untrained likelihood. If observers use Bayesian principles, they should immediately put less weight on the new, less reliable, likelihood (“Bayesian transfer”). In an initial experiment, observers estimated the position of a hidden target, drawn from one of two distinct distributions, using sensory and prior information. The sensory cue consisted of dots drawn from a Gaussian distribution centered on the true location with either low, medium, or high variance; the latter introduced after block three of five to test for evidence of Bayesian transfer. Observers did not weight the cue (relative to the prior) significantly less in the high compared to medium variance condition, counter to Bayesian predictions. However, when explicitly informed of the different prior variabilities, observers placed less weight on the new high variance likelihood (“Bayesian transfer”), yet, substantially diverged from ideal. Much of this divergence can be captured by a model that weights sensory information, according only to internal noise in using the cue. These results emphasize the limits of Bayesian models in complex tasks.

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Section: Discussioncontrasting

confidence: 94%

Bayesian transfer in a complex spatial localization task

et al. 2020

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“…Ma et al identify the performance gain achieved by organisms who incorporate uncertainty measures through visual processing, etc. into their decisionmaking [40,23,34,22] and perceptual organization [81]. Our work differs from these papers in that we approach this concept from a machine learning perspective.…”

Section: Assessing Human Uncertainty In Ambiguous Datamentioning

confidence: 99%

Ambiguous Images With Human Judgments for Robust Visual Event Classification

Sanders¹,

Kriz²,

Liu³

et al. 2022

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Contemporary vision benchmarks predominantly consider tasks on which humans can achieve near-perfect performance. However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data. To address this issue, we introduce a procedure for creating datasets of ambiguous images and use it to produce SQUID-E ("Squidy"), a collection of noisy images extracted from videos. All images are annotated with ground truth values and a test set is annotated with human uncertainty judgments. We use this dataset to characterize human uncertainty in vision tasks and evaluate existing visual event classification models. Experimental results suggest that existing vision models are not sufficiently equipped to provide meaningful outputs for ambiguous images and that datasets of this nature can be used to assess and improve such models through model training and direct evaluation of model calibration. These findings motivate large-scale ambiguous dataset creation and further research focusing on noisy visual data. 1 1 Dataset and code are available at https://katesanders9.github.io/ambiguous-images. 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.

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The role of uncertainty in perceptual organization

Cited by 2 publications

References 0 publications

Bayesian transfer in a complex spatial localization task

Bayesian transfer in a complex spatial localization task

Ambiguous Images With Human Judgments for Robust Visual Event Classification

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