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Visual aftereffects and sensory nonlinearities from a single statistical framework

Overview of attention for article published in Frontiers in Human Neuroscience, October 2015
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Title
Visual aftereffects and sensory nonlinearities from a single statistical framework
Published in
Frontiers in Human Neuroscience, October 2015
DOI 10.3389/fnhum.2015.00557
Pubmed ID
Authors

Valero Laparra, Jesús Malo

Abstract

When adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture, and motion may tell us where these visual illusions come from. However, such empirical models of gain control do not explain why these mechanisms work in this apparently dysfunctional manner. Current normative explanations of aftereffects based on scene statistics derive gain changes by (1) invoking decorrelation and linear manifold matching/equalization, or (2) using nonlinear divisive normalization obtained from parametric scene models. These principled approaches have different drawbacks: the first is not compatible with the known saturation nonlinearities in the sensors and it cannot fully accomplish information maximization due to its linear nature. In the second, gain change is almost determined a priori by the assumed parametric image model linked to divisive normalization. In this study we show that both the response changes that lead to aftereffects and the nonlinear behavior can be simultaneously derived from a single statistical framework: the Sequential Principal Curves Analysis (SPCA). As opposed to mechanistic models, SPCA is not intended to describe how physiological sensors work, but it is focused on explaining why they behave as they do. Nonparametric SPCA has two key advantages as a normative model of adaptation: (i) it is better than linear techniques as it is a flexible equalization that can be tuned for more sensible criteria other than plain decorrelation (either full information maximization or error minimization); and (ii) it makes no a priori functional assumption regarding the nonlinearity, so the saturations emerge directly from the scene data and the goal (and not from the assumed function). It turns out that the optimal responses derived from these more sensible criteria and SPCA are consistent with dysfunctional behaviors such as aftereffects.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Ph. D. Student 5 17%
Student > Bachelor 4 13%
Lecturer 2 7%
Professor 1 3%
Other 2 7%
Unknown 8 27%
Readers by discipline Count As %
Psychology 10 33%
Linguistics 3 10%
Mathematics 2 7%
Computer Science 2 7%
Neuroscience 2 7%
Other 3 10%
Unknown 8 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 October 2015.
All research outputs
#17,775,656
of 22,830,751 outputs
Outputs from Frontiers in Human Neuroscience
#5,710
of 7,153 outputs
Outputs of similar age
#188,078
of 279,229 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#121
of 158 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,153 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.