Using Noise to Characterize Vision
Noise has been widely used to investigate the processing properties of various visual functions (e.g. detection, discrimination, attention, perceptual learning, averaging, crowding, face recognition), in various populations (e.g. older adults, amblyopes, migrainers, dyslexic children), using noise a...
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| Formaat: | Online |
| Taal: | Engels |
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Frontiers Media SA
2021
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| Online toegang: | 18873 |
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| _version_ | 1869516465179197440 |
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| author | Remy Allard Jocelyn Faubert Denis G. Pelli |
| author_browse | Denis G. Pelli Jocelyn Faubert Remy Allard |
| author_facet | Remy Allard Jocelyn Faubert Denis G. Pelli |
| author_sort | Remy Allard |
| collection | Directory of Open Access Books |
| description | Noise has been widely used to investigate the processing properties of various visual functions (e.g. detection, discrimination, attention, perceptual learning, averaging, crowding, face recognition), in various populations (e.g. older adults, amblyopes, migrainers, dyslexic children), using noise along various dimensions (e.g. pixel noise, orientation jitter, contrast jitter). The reason to use external noise is generally not to characterize visual processing in external noise per se, but rather to reveal how vision works in ordinary conditions when performance is limited by our intrinsic noise rather than externally added noise. For instance, reverse correlation aims at identifying the relevant information to perform a given task in noiseless conditions and measuring contrast thresholds in various noise levels can be used to understand the impact of intrinsic noise that limits sensitivity to noiseless stimuli. Why use noise? Since Fechner named it, psychophysics has always emphasized the systematic investigation of conditions that break vision. External noise raises threshold hugely and selectively. In hearing, Fletcher used noise in his famous critical-band experiments to reveal frequency-selective channels in hearing. Critical bands have been found in vision too. More generally, the big reliable effects of noise give important clues to how the system works. And simple models have been proposed to account for the effects of visual noise. As noise has been more widely used, questions have been raised about the simplifying assumptions that link the processing properties in noiseless conditions to measurements in external noise. For instance, it is usually assumed that the processing strategy (or mechanism) used to perform a task and its processing properties (e.g. filter tuning) are unaffected by the addition of external noise. Some have suggested that the processing properties could change with the addition of external noise (e.g. change in filter tuning or more lateral masking in noise), which would need to be considered before drawing conclusions about the processing properties in noiseless condition. Others have suggested that different processing properties (or mechanisms) could be solicited in low and high noise conditions, complicating the characterization of processing properties in noiseless condition based on processing properties identified in noise conditions. The current Research Topic probes further into what the effects of visual noise tell us about vision in ordinary conditions. Our Editorial gives an overview of the articles in this special issue. |
| format | Online |
| id | doab-20.500.12854ir-61795 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-617952024-03-29T08:00:02Z Using Noise to Characterize Vision Remy Allard Jocelyn Faubert Denis G. Pelli BF1-990 Q1-390 Linear amplifier model Contrast jitter Noise perceptual template model bandpass noise Equivalent input noise noise image classification phase noise bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology Noise has been widely used to investigate the processing properties of various visual functions (e.g. detection, discrimination, attention, perceptual learning, averaging, crowding, face recognition), in various populations (e.g. older adults, amblyopes, migrainers, dyslexic children), using noise along various dimensions (e.g. pixel noise, orientation jitter, contrast jitter). The reason to use external noise is generally not to characterize visual processing in external noise per se, but rather to reveal how vision works in ordinary conditions when performance is limited by our intrinsic noise rather than externally added noise. For instance, reverse correlation aims at identifying the relevant information to perform a given task in noiseless conditions and measuring contrast thresholds in various noise levels can be used to understand the impact of intrinsic noise that limits sensitivity to noiseless stimuli. Why use noise? Since Fechner named it, psychophysics has always emphasized the systematic investigation of conditions that break vision. External noise raises threshold hugely and selectively. In hearing, Fletcher used noise in his famous critical-band experiments to reveal frequency-selective channels in hearing. Critical bands have been found in vision too. More generally, the big reliable effects of noise give important clues to how the system works. And simple models have been proposed to account for the effects of visual noise. As noise has been more widely used, questions have been raised about the simplifying assumptions that link the processing properties in noiseless conditions to measurements in external noise. For instance, it is usually assumed that the processing strategy (or mechanism) used to perform a task and its processing properties (e.g. filter tuning) are unaffected by the addition of external noise. Some have suggested that the processing properties could change with the addition of external noise (e.g. change in filter tuning or more lateral masking in noise), which would need to be considered before drawing conclusions about the processing properties in noiseless condition. Others have suggested that different processing properties (or mechanisms) could be solicited in low and high noise conditions, complicating the characterization of processing properties in noiseless condition based on processing properties identified in noise conditions. The current Research Topic probes further into what the effects of visual noise tell us about vision in ordinary conditions. Our Editorial gives an overview of the articles in this special issue. 2021-02-12T07:16:14Z 2021-02-12T07:16:14Z 2016-04-07 11:22:02 2016 book 18873 16648714 9782889197538 https://directory.doabooks.org/handle/20.500.12854/61795 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Using_Noise_to_Characterize_Vision/808#nogo http://journal.frontiersin.org/researchtopic/1423/using-noise-to-characterize-vision Frontiers Media SA 10.3389/978-2-88919-753-8 10.3389/978-2-88919-753-8 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889197538 127 open access |
| spellingShingle | BF1-990 Q1-390 Linear amplifier model Contrast jitter Noise perceptual template model bandpass noise Equivalent input noise noise image classification phase noise bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology Remy Allard Jocelyn Faubert Denis G. Pelli Using Noise to Characterize Vision |
| title | Using Noise to Characterize Vision |
| title_full | Using Noise to Characterize Vision |
| title_fullStr | Using Noise to Characterize Vision |
| title_full_unstemmed | Using Noise to Characterize Vision |
| title_short | Using Noise to Characterize Vision |
| title_sort | using noise to characterize vision |
| topic | BF1-990 Q1-390 Linear amplifier model Contrast jitter Noise perceptual template model bandpass noise Equivalent input noise noise image classification phase noise bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology |
| topic_facet | BF1-990 Q1-390 Linear amplifier model Contrast jitter Noise perceptual template model bandpass noise Equivalent input noise noise image classification phase noise bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology |
| url | 18873 |
| work_keys_str_mv | AT remyallard usingnoisetocharacterizevision AT jocelynfaubert usingnoisetocharacterizevision AT denisgpelli usingnoisetocharacterizevision |