000 03954naaaa2200361uu 4500
001 https://directory.doabooks.org/handle/20.500.12854/61795
005 20220220092844.0
020 _a978-2-88919-753-8
020 _a9782889197538
024 7 _a10.3389/978-2-88919-753-8
_cdoi
041 0 _aEnglish
042 _adc
100 1 _aRemy Allard
_4auth
700 1 _aJocelyn Faubert
_4auth
700 1 _aDenis G. Pelli
_4auth
245 1 0 _aUsing Noise to Characterize Vision
260 _bFrontiers Media SA
_c2016
300 _a1 electronic resource (127 p.)
506 0 _aOpen Access
_2star
_fUnrestricted online access
520 _aNoise 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.
540 _aCreative Commons
_fhttps://creativecommons.org/licenses/by/4.0/
_2cc
_4https://creativecommons.org/licenses/by/4.0/
546 _aEnglish
653 _aLinear amplifier model
653 _aContrast jitter
653 _aNoise
653 _aperceptual template model
653 _abandpass noise
653 _aEquivalent input noise
653 _anoise image classification
653 _aphase noise
856 4 0 _awww.oapen.org
_uhttp://journal.frontiersin.org/researchtopic/1423/using-noise-to-characterize-vision
_70
_zDOAB: download the publication
856 4 0 _awww.oapen.org
_uhttps://directory.doabooks.org/handle/20.500.12854/61795
_70
_zDOAB: description of the publication
999 _c78680
_d78680