Mechanisms for extracting a signal from noise as revealed through the specificity and generality of task training

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@article{2b722020413d4952ad2876cec7f5c9a9,
title = "Mechanisms for extracting a signal from noise as revealed through the specificity and generality of task training",
abstract = "Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or “coarse” tasks) versus the discrimination of highly similar items (feature difference, or “fine” tasks). First, we consider the processing of binocular disparity signals, examining performance on signal-in-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions. ",
author = "Dorita Chang and Zoe Kourtzi and Andrew Welchman",
year = "2013",
month = jul,
day = "3",
doi = "10.1523/JNEUROSCI.0101-13.2013",
language = "English",
volume = "33",
pages = "10962--10971",
journal = "The Journal of Neuroscience",
issn = "0270-6474",
publisher = "Society for Neuroscience",
number = "27",

}

RIS

TY - JOUR

T1 - Mechanisms for extracting a signal from noise as revealed through the specificity and generality of task training

AU - Chang, Dorita

AU - Kourtzi, Zoe

AU - Welchman, Andrew

PY - 2013/7/3

Y1 - 2013/7/3

N2 - Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or “coarse” tasks) versus the discrimination of highly similar items (feature difference, or “fine” tasks). First, we consider the processing of binocular disparity signals, examining performance on signal-in-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions.

AB - Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or “coarse” tasks) versus the discrimination of highly similar items (feature difference, or “fine” tasks). First, we consider the processing of binocular disparity signals, examining performance on signal-in-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions.

U2 - 10.1523/JNEUROSCI.0101-13.2013

DO - 10.1523/JNEUROSCI.0101-13.2013

M3 - Article

C2 - 23825402

VL - 33

SP - 10962

EP - 10971

JO - The Journal of Neuroscience

JF - The Journal of Neuroscience

SN - 0270-6474

IS - 27

ER -