Dynamic brightness induction in V1: Analyzing simulated and empirically acquired fMRI data in a “common brain space” framework

Peters, Judith C.; Jans, Bert; Ven, Vincent van de; Weerd, Peter De; Goebel, Rainer

doi:10.1016/j.neuroimage.2010.03.070

Cited by 10 publications

(22 citation statements)

References 89 publications

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“…In general, computational models using multiscale filtering have not been able to explain the same range of brightness percepts as models that do include an explicit surface filling-in mechanisms (Grossberg & Hong, 2006). In a recent largescale computational model, we predicted our dynamic brightness illusion effect in early visual cortex using principles of lateral connectivity as a means for spreading of surface information across the cortex (Peters et al, 2010).…”

Section: Discussionmentioning

confidence: 94%

“…Our results do not exclude the possibility that V1 may also contribute to the perception of brightness (cf. Peters, Jans, van de Ven, De Weerd, & Goebel, 2010;Roe et al, 2005;Ramsden et al, 2001), although this effect may be smaller compared with V2, and our paradigm or stimulus may not have been sensitive enough to pick up the effect in V1. Some fMRI studies, in fact, did not find a brightness correlate in neither V1 nor extrastriate cortex using brightness induction (Cornelissen et al, 2006;Perna et al, 2005) as experimental paradigms.…”

Section: Discussionmentioning

confidence: 97%

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Early Human Visual Cortex Encodes Surface Brightness Induced by Dynamic Context

Ven

Jans

Goebel

et al. 2012

Journal of Cognitive Neuroscience

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Visual scene perception owes greatly to surface features such as color and brightness. Yet, early visual cortical areas predominantly encode surface boundaries rather than surface interiors. Whether human early visual cortex may nevertheless carry a small signal relevant for surface perception is a topic of debate. We induced brightness changes in a physically constant surface by temporally modulating the luminance of surrounding surfaces in seven human participants. We found that fMRI activity in the V2 representation of the constant surface was in antiphase to luminance changes of surrounding surfaces (i.e., activity was in-phase with perceived brightness changes). Moreover, the amplitude of the antiphase fMRI activity in V2 predicted the strength of illusory brightness perception. We interpret our findings as evidence for a surface-related signal in early visual cortex and discuss the neural mechanisms that may underlie that signal in concurrence with its possible interaction with the properties of the fMRI signal.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 97%

Early Human Visual Cortex Encodes Surface Brightness Induced by Dynamic Context

Ven

Jans

Goebel

et al. 2012

Journal of Cognitive Neuroscience

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“…Importantly, when simulated and measured data co-exist in the same representational space, the same analysis tools (e.g., MVPA, effective connectivity analysis) can be applied to both data sets allowing for quantitative comparisons (Figure 2). See Peters et al (2010) for further details.…”

Section: Integration Of Computational and Experimental Findings In Cbsmentioning

confidence: 99%

“…In the current paper, we highlight recent developments in object recognition research and put forward a “Common Brain Space” framework (CBS; Goebel and De Weerd, 2009; Peters et al, 2010) in which empirical data and computational results can be directly integrated and quantitatively compared.…”

Section: Introductionmentioning

confidence: 99%

Modeling invariant object processing based on tight integration of simulated and empirical data in a Common Brain Space

Peters¹,

Reithler²,

Goebel³

2012

Front. Comput. Neurosci.

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Recent advances in Computer Vision and Experimental Neuroscience provided insights into mechanisms underlying invariant object recognition. However, due to the different research aims in both fields models tended to evolve independently. A tighter integration between computational and empirical work may contribute to cross-fertilized development of (neurobiologically plausible) computational models and computationally defined empirical theories, which can be incrementally merged into a comprehensive brain model. After reviewing theoretical and empirical work on invariant object perception, this article proposes a novel framework in which neural network activity and measured neuroimaging data are interfaced in a common representational space. This enables direct quantitative comparisons between predicted and observed activity patterns within and across multiple stages of object processing, which may help to clarify how high-order invariant representations are created from low-level features. Given the advent of columnar-level imaging with high-resolution fMRI, it is time to capitalize on this new window into the brain and test which predictions of the various object recognition models are supported by this novel empirical evidence.

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“…For example Golfinopoulos et al (2010) employ computational models to advance the employment of functional neuroimaging data for the purposes of understanding speech acquisition and production, whereas Schwabe et al (2010) consider models of neuronal firing rates in the visual cortex to explain non-classic extra-receptive field effects of visual contrast. Peters et al (2010) also address the issue of extra-receptive field visual effects and propose a neuroinformatics platform for a common representational "brain space" for the purpose of optimising the confluence of computational and experimental neuroscience. Rigotti et al (2010) consider a mapping between the sequence of attractors expressed in the amygdala and orbito-frontal cortex and a routinized pattern of sensory inputs as the basis for contextual representation of spaces.…”

Section: From Dynamics To Computation and Functionmentioning

confidence: 99%