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The Elementary Operations of Human Vision Are Not Reducible to Template-Matching.

Neri P - PLoS Comput. Biol. (2015)

Bottom Line: We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task.Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions.They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire des Systèmes Perceptifs, CNRS UMR 8248, 29 rue d'Ulm, 75005 Paris, France.

ABSTRACT
It is generally acknowledged that biological vision presents nonlinear characteristics, yet linear filtering accounts of visual processing are ubiquitous. The template-matching operation implemented by the linear-nonlinear cascade (linear filter followed by static nonlinearity) is the most widely adopted computational tool in systems neuroscience. This simple model achieves remarkable explanatory power while retaining analytical tractability, potentially extending its reach to a wide range of systems and levels in sensory processing. The extent of its applicability to human behaviour, however, remains unclear. Because sensory stimuli possess multiple attributes (e.g. position, orientation, size), the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models, or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade. We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task. However, no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit, resulting in a model that no longer belongs to the linear-nonlinear family. Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions. They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components.

No MeSH data available.


Nonlinear tests return no deviations for gain-control model, but detect nonlinear behaviour exhibited by push-pull model.Black histograms plot distributions for the 1st-order nonlinear test, grey histograms for the 2nd-order nonlinear test (see caption to Fig 3 for brief description of these two tests), orange histograms for 1st-order full PF’s (reflecting linear component of LN models when applicable), across all 4 noise conditions (different rows) and for both gain-control (A) and push-pull model (B). Left insets plot target-present/target-absent (red/blue) first-order PF’s (similar to Fig 3B–3E); right insets plot second-order PF’s for the 1D condition. The second-order PF associated with the push-pull model displays substantial modulations; in the 1D condition, this model returns clearly positive values for both nonlinear tests. All simulated first-order PF’s display measurable structure (see orange distributions) except for the push-pull model in the 2D condition (top right, see also Fig 5B and 5G).
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pcbi.1004499.g007: Nonlinear tests return no deviations for gain-control model, but detect nonlinear behaviour exhibited by push-pull model.Black histograms plot distributions for the 1st-order nonlinear test, grey histograms for the 2nd-order nonlinear test (see caption to Fig 3 for brief description of these two tests), orange histograms for 1st-order full PF’s (reflecting linear component of LN models when applicable), across all 4 noise conditions (different rows) and for both gain-control (A) and push-pull model (B). Left insets plot target-present/target-absent (red/blue) first-order PF’s (similar to Fig 3B–3E); right insets plot second-order PF’s for the 1D condition. The second-order PF associated with the push-pull model displays substantial modulations; in the 1D condition, this model returns clearly positive values for both nonlinear tests. All simulated first-order PF’s display measurable structure (see orange distributions) except for the push-pull model in the 2D condition (top right, see also Fig 5B and 5G).

Mentions: We only implemented the 2D variant of the push-pull model (diagram in Fig 5F). Each input stimulus is matched to both the target signal and to its orhtogonal image (the non-target signal in the discrimination task, see icons to the left of Fig 2E); the model response associated with the stimulus is the difference between the two squared matches: . The corresponding PF’s across noise types are shown in Fig 5 (red traces in panels C-E,I). The corresponding results for 1st- and 2nd-order nonlinear tests are plotted in Fig 7B.


The Elementary Operations of Human Vision Are Not Reducible to Template-Matching.

Neri P - PLoS Comput. Biol. (2015)

Nonlinear tests return no deviations for gain-control model, but detect nonlinear behaviour exhibited by push-pull model.Black histograms plot distributions for the 1st-order nonlinear test, grey histograms for the 2nd-order nonlinear test (see caption to Fig 3 for brief description of these two tests), orange histograms for 1st-order full PF’s (reflecting linear component of LN models when applicable), across all 4 noise conditions (different rows) and for both gain-control (A) and push-pull model (B). Left insets plot target-present/target-absent (red/blue) first-order PF’s (similar to Fig 3B–3E); right insets plot second-order PF’s for the 1D condition. The second-order PF associated with the push-pull model displays substantial modulations; in the 1D condition, this model returns clearly positive values for both nonlinear tests. All simulated first-order PF’s display measurable structure (see orange distributions) except for the push-pull model in the 2D condition (top right, see also Fig 5B and 5G).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4640830&req=5

pcbi.1004499.g007: Nonlinear tests return no deviations for gain-control model, but detect nonlinear behaviour exhibited by push-pull model.Black histograms plot distributions for the 1st-order nonlinear test, grey histograms for the 2nd-order nonlinear test (see caption to Fig 3 for brief description of these two tests), orange histograms for 1st-order full PF’s (reflecting linear component of LN models when applicable), across all 4 noise conditions (different rows) and for both gain-control (A) and push-pull model (B). Left insets plot target-present/target-absent (red/blue) first-order PF’s (similar to Fig 3B–3E); right insets plot second-order PF’s for the 1D condition. The second-order PF associated with the push-pull model displays substantial modulations; in the 1D condition, this model returns clearly positive values for both nonlinear tests. All simulated first-order PF’s display measurable structure (see orange distributions) except for the push-pull model in the 2D condition (top right, see also Fig 5B and 5G).
Mentions: We only implemented the 2D variant of the push-pull model (diagram in Fig 5F). Each input stimulus is matched to both the target signal and to its orhtogonal image (the non-target signal in the discrimination task, see icons to the left of Fig 2E); the model response associated with the stimulus is the difference between the two squared matches: . The corresponding PF’s across noise types are shown in Fig 5 (red traces in panels C-E,I). The corresponding results for 1st- and 2nd-order nonlinear tests are plotted in Fig 7B.

Bottom Line: We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task.Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions.They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire des Systèmes Perceptifs, CNRS UMR 8248, 29 rue d'Ulm, 75005 Paris, France.

ABSTRACT
It is generally acknowledged that biological vision presents nonlinear characteristics, yet linear filtering accounts of visual processing are ubiquitous. The template-matching operation implemented by the linear-nonlinear cascade (linear filter followed by static nonlinearity) is the most widely adopted computational tool in systems neuroscience. This simple model achieves remarkable explanatory power while retaining analytical tractability, potentially extending its reach to a wide range of systems and levels in sensory processing. The extent of its applicability to human behaviour, however, remains unclear. Because sensory stimuli possess multiple attributes (e.g. position, orientation, size), the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models, or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade. We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task. However, no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit, resulting in a model that no longer belongs to the linear-nonlinear family. Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions. They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components.

No MeSH data available.