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Representing color and orientation ensembles: Can observers learn multiple feature distributions?

Representing color and orientation ensembles: Can observers learn multiple feature distributions?


Title: Representing color and orientation ensembles: Can observers learn multiple feature distributions?
Author: Hansmann-Roth, Sabrina
Chetverikov, Andrey
Kristjansson, Arni
Date: 2019-08-01
Language: English
Scope: 2
University/Institute: Háskóli Íslands
University of Iceland
School: Heilbrigðisvísindasvið (HÍ)
School of Health Sciences (UI)
Department: Rannsóknamiðstöð um sjónskynjun (HÍ)
Icelandic Vision Lab (UI)
Series: Journal of Vision;19(9)
ISSN: 1534-7362
DOI: 10.1167/19.9.2
Subject: Ophthalmology; Sensory Systems; Ensemble perception; Perceptual learning; Perceptual organization; Statistical learning; Augnlækningar; Sjónskynjun; Skynjun; Nám
URI: https://hdl.handle.net/20.500.11815/1659

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Citation:

Hansmann-Roth, S., Chetverikov, A., & Kristjánsson, &. (2019). Representing color and orientation ensembles: Can observers learn multiple feature distributions? Journal of Vision, 19(9), 2.

Abstract:

Objects have a variety of different features that can be represented as probability distributions. Recent findings show that in addition to mean and variance, the visual system can also encode the shape of feature distributions for features like color or orientation. In an odd-one-out search task we investigated observers' ability to encode two feature distributions simultaneously. Our stimuli were defined by two distinct features (color and orientation) while only one was relevant to the search task.We investigated whether the irrelevant feature distribution influences learning of the task-relevant distribution and whether observers also encode the irrelevant distribution. Although considerable learning of feature distributions occurred, especially for color, our results also suggest that adding a second irrelevant feature distribution negatively affected the encoding of the relevant one and that little learning of the irrelevant distribution occurred. There was also an asymmetry between the two different features: Searching for the oddly oriented target was more difficult than searching for the oddly colored target, which was reflected in worse learning of the color distribution. Overall, the results demonstrate that it is possible to encode information about two feature distributions simultaneously but also reveal considerable limits to this encoding.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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