Adaptation to variance generalizes across visual domains.

Maule, John; Franklin, Anna

doi:10.1037/xge0000678

Cited by 20 publications

(25 citation statements)

References 71 publications

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“…Factors that influence variance perception Given the paucity of literature on variance perception, there is limited knowledge on the factors that influence variance perception. As with mean perception, adapting to variance biases subsequent variance perception away from the adapted stimulus (as with orientation and color hue: Maule & Franklin, 2019). Spatial locations and spatial relationships between individual items seem to affect variance perception.…”

Section: Variance Perceptionmentioning

confidence: 99%

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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In the age of big data, we are constantly inventing new data visualizations to consolidate massive amounts of numerical information into smaller and more digestible visual formats. These data visualizations use various visual features to convey quantitative information, such as spatial position in scatter plots, color saturation in heat maps, and area in dot maps. These data visualizations are typically composed of ensembles, or groups of related objects, that together convey information about a data set. Ensemble perception, or one's ability to perceive summary statistics from an ensemble, such as the mean, has been used as a foundation for understanding and explaining the effectiveness of certain data visualizations. However, research in data visualization has revealed some perceptual biases and conceptual difficulties people face when trying to utilize the information in these graphs. In this tutorial review, we will provide a broad overview of research conducted in ensemble perception, discuss how principles of ensemble encoding have been applied to the research in data visualization, and showcase the barriers graphs can pose to learning statistical concepts, using histograms as a specific example. The goal of this tutorial review is to highlight possible connections between three areas of research-ensemble perception, data visualization, and statistics education-and to encourage research in the practical applications of ensemble perception in solving real-world problems in statistics education.

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Section: Variance Perceptionmentioning

confidence: 99%

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Cui

Liu

2021

Atten Percept Psychophys

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“…The similarity between attractive serial dependence and repulsive adaptation effects in vision is suggested by the observations that both phenomena emerge not only across “primary” visual dimensions such as orientation ( Fischer & Whitney, 2014 ; He & MacLeod, 2001 ), numerosity ( Fornaciai & Park, 2018b ; Arrighi, Togoli, & Burr, 2014 ), position ( Manassi et al, 2018 ; Whitaker, McGraw, & Levi, 1997 ), motion ( Alais et al, 2017 ; Kohn & Movshon, 2003 ), or shape ( Manassi Kristjánsson, & Whitney, 2019 ; Mattar, Olkkonen, Epstein, & Aguirre, 2018 ), but also across more complex features such as the summary statistics of a visual scene ( Manassi, Liberman, Chaney, & Whitney, 2017 ; Corbett, Wurnitsch, Schwartz, & Whitney, 2012 ) and visual variance ( Suárez-Pinilla et al, 2018 ; Maule & Franklin, 2020 ). Moreover, although adaptation and serial dependence may involve distinct physiological mechanisms, there is evidence that the same stimulus can induce either an attractive or a repulsive effect, depending on whether it was actively judged ( Pascucci et al, 2019 ), or whether it was visible or suppressed by backward masking ( Fornaciai & Park, 2019a ; Fornaciai & Park, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Serial dependence in time and numerosity perception is dimension-specific

et al. 2021

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The perception of a visual event (e.g., a flock of birds) at the present moment can be biased by a previous perceptual experience (e.g., the perception of an earlier flock). Serial dependence is a perceptual bias whereby a current stimulus appears more similar to a previous one than it actually is. Whereas serial dependence emerges within several visual stimulus dimensions, whether it could simultaneously operate across different dimensions of the same stimulus (e.g., the numerosity and the duration of a visual pattern) remains unclear.Here we address this question by assessing the presence of serial dependence across duration and numerosity, two stimulus dimensions that are often associated and can bias each other. Participants performed either a duration or a numerosity discrimination task, in which they compared a constant reference with a variable test stimulus, varying along the task-relevant dimension (either duration or numerosity). Serial dependence was induced by a task-irrelevant inducer, that is, a stimulus presented before the reference and always varying in both duration and numerosity. The results show systematic serial dependencies only within the task-relevant stimulus dimension, that is, stimulus numerosity affects numerosity perception only, and duration affects duration perception only. Additionally, at least in the numerosity condition, the task-irrelevant dimension of the inducer (duration) had an opposite, repulsive effect. These findings thus show that attractive serial dependence operates in a highly specific fashion and does not transfer across different stimulus dimensions. Instead, the repulsive influence, possibly reflecting perceptual adaptation, can transfer from one dimension to another.

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“…Since our model implies ensemble coding by neurons tuned specifically to visual features, it can potentially account for a set of adaptation aftereffects of ensemble statistics (Corbett et al, 2012; Jeong & Chong, 2020; Maule & Franklin, 2020; Norman et al, 2015). Adaptation aftereffects of various features are often taken as evidence for the direct coding of these features by sensory systems (Webster, 2011, 2015).…”

Section: Discussionmentioning

confidence: 99%

A Population Response Model of Ensemble Perception

Utochkin¹,

Choi

Chong

2022

Preprint

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Ensemble representations have been considered as one of strategies that the visual system adopts to cope with its limited capacity. Thus, they include various statistical summaries such as mean, variance, and distributional properties and are formed over many stages of visual processing. The current study proposes a population coding model of ensemble perception to provide a theoretical and computational framework for these various facets of ensemble perception. The proposed model consists of a simple feature layer and a pooling layer. We assumed ensemble representations as population responses in the pooling layer and decoded various statistical properties from population responses. Our model successfully predicted averaging performance in orientation, size, color, and motion direction across different tasks. Furthermore, it predicted variance discrimination performance and the priming effects of feature distributions. Finally, it explained the well-known variance and set size effects and has a potential for explaining the adaptation and clustering effects.

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Adaptation to variance generalizes across visual domains.

Cited by 20 publications

References 71 publications

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Synergy between research on ensemble perception, data visualization, and statistics education: A tutorial review

Serial dependence in time and numerosity perception is dimension-specific

A Population Response Model of Ensemble Perception

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