Cargando…

Perceptual learning of second order cues for layer decomposition

Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition—the process of distinguishing between reflectance and illumination changes—is supported by a range of secondary cues including colour and texture. For an illuminated cor...

Descripción completa

Detalles Bibliográficos
Autores principales: Dövencioğlu, Dicle N., Welchman, Andrew E., Schofield, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science Ltd 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552157/
https://www.ncbi.nlm.nih.gov/pubmed/23200744
http://dx.doi.org/10.1016/j.visres.2012.11.005
_version_ 1782256658278252544
author Dövencioğlu, Dicle N.
Welchman, Andrew E.
Schofield, Andrew J.
author_facet Dövencioğlu, Dicle N.
Welchman, Andrew E.
Schofield, Andrew J.
author_sort Dövencioğlu, Dicle N.
collection PubMed
description Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition—the process of distinguishing between reflectance and illumination changes—is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.
format Online
Article
Text
id pubmed-3552157
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Elsevier Science Ltd
record_format MEDLINE/PubMed
spelling pubmed-35521572013-01-25 Perceptual learning of second order cues for layer decomposition Dövencioğlu, Dicle N. Welchman, Andrew E. Schofield, Andrew J. Vision Res Article Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition—the process of distinguishing between reflectance and illumination changes—is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow. Elsevier Science Ltd 2013-01-25 /pmc/articles/PMC3552157/ /pubmed/23200744 http://dx.doi.org/10.1016/j.visres.2012.11.005 Text en © 2013 Elsevier Ltd. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license
spellingShingle Article
Dövencioğlu, Dicle N.
Welchman, Andrew E.
Schofield, Andrew J.
Perceptual learning of second order cues for layer decomposition
title Perceptual learning of second order cues for layer decomposition
title_full Perceptual learning of second order cues for layer decomposition
title_fullStr Perceptual learning of second order cues for layer decomposition
title_full_unstemmed Perceptual learning of second order cues for layer decomposition
title_short Perceptual learning of second order cues for layer decomposition
title_sort perceptual learning of second order cues for layer decomposition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552157/
https://www.ncbi.nlm.nih.gov/pubmed/23200744
http://dx.doi.org/10.1016/j.visres.2012.11.005
work_keys_str_mv AT dovenciogludiclen perceptuallearningofsecondordercuesforlayerdecomposition
AT welchmanandrewe perceptuallearningofsecondordercuesforlayerdecomposition
AT schofieldandrewj perceptuallearningofsecondordercuesforlayerdecomposition