Emergence of Perceptual Gestalts in the Human Visual Cortex: The Case of the...

 

 

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Emergence of Perceptual Gestalts in the Human Visual Cortex: The Case of the Configural-Superiority Effect

via Psychological Science current issue by Kubilius, J., Wagemans, J., Op de Beeck, H. P. on 10/6/11

Many Gestalt phenomena have been described in terms of perception of a whole being not equal to the sum of its parts. It is unclear how these phenomena emerge in the brain. We used functional MRI to study the neural basis of the behavioral configural-superiority effect (i.e., visual search is more efficient when an odd element is part of a configuration than when it is presented by itself). We found that searching for the odd element in a display of four line segments (parts) was facilitated by adding two additional line segments to each of them (creating whole shapes). Functional MRI–based decoding of neural responses to the position of the odd element revealed a neural configural-superiority effect in shape-selective regions but not in low-level retinotopic areas, where decoding of parts was more pronounced. These results show how at least some Gestalt phenomena in vision emerge only at the higher stages of visual information processing and suggest that feed-forward processing might be sufficient to produce such phenomena.

 

 

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Constructing scenes from objects in human occipitotemporal cortex

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Constructing scenes from objects in human occipitotemporal cortex

per Nature Neuroscience - Issue - nature.com science feeds autorius Russell A Epstein 11.9.4

Constructing scenes from objects in human occipitotemporal cortex

Nature Neuroscience 14, 1323 (2011). doi:10.1038/nn.2903

Authors: Sean P MacEvoy & Russell A Epstein

 

 

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

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https://sites.google.com/site/gallantlabucb/publications/nishimoto-et-al-2011

 

 

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

via Gestalt Revision fMRI by Gestalt Revision on 9/23/11

This is really fun! I encourage everybody to look up their supplementary movie.

 

 

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

per CURRENT BIOLOGY 11.9.21

Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu, Jack L. Gallant. Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3–5]. Recent functional magnetic....

 

 

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

This is really fun! I encourage everybody to look up their supplementary movie.

 

 

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

per CURRENT BIOLOGY 11.9.21

Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu, Jack L. Gallant. Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3–5]. Recent functional magnetic....

 

 

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Neuroimaging reveals how brain uses objects to recognize scenes

 

 

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Neuroimaging reveals how brain uses objects to recognize scenes

via ScienceDaily: Mind & Brain News on 9/13/11

A new study by psychologists helps to explain how people quickly and accurately recognize complicated scenes such as playgrounds, kitchens and traffic intersections.

 

 

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Associative Learning Increases Trial-by-Trial Similarity of BOLD-MRI Patterns

http://www.jneurosci.org/content/31/33/12021.full.pdf+html

 

Associative Learning Increases Trial-by-Trial Similarity of BOLD-MRI Patterns

 

Rene´e M. Visser,

1,2

H. Steven Scholte,

2,3

and Merel Kindt

1,2

1

Department of Clinical Psychology,

2

Priority Program Brain and Cognition, and

3

Department of Brain and Cognition, University of Amsterdam,

1018 WB Amsterdam, The Netherlands

Associative learning is a dynamic process that allows us to incorporate new knowledge within existing semantic networks. Even after

years, a seemingly stable association can be altered by a single significant experience. Here, we investigate whether the acquisition of new

associations affects the neural representation of stimuli and how the brain categorizes stimuli according to preexisting and emerging

associations. Functional MRI data were collected during a differential fear conditioning procedure and at test (4 –5 weeks later). Two

pictures of faces and two pictures of houses served as stimuli. One of each pair coterminated with a shock in half of the trials (partial

reinforcement). Applying Multivoxel Pattern Analysis (MVPA) in a trial-by-trial manner, we quantified changes in the similarity of

neural representations of stimuli over the course of conditioning. Our findings show an increase in similarity of neural patterns throughout the cortex on consecutive trials of the reinforced stimuli. Furthermore, neural pattern similarity reveals a shift from original categories (faces/houses) toward new categories (reinforced/unreinforced) over the course of conditioning. This effect was differentially

represented in the cortex, with visual areas primarily reflecting similarity of low-level stimulus properties (original categories) and

frontal areas reflecting similarity of stimulus significance (new categories). Effects were not dependent on overall response amplitude

and were still presentduring follow-up. We conclude thattrial-by-trial MVPA is a useful tool for examining how the human brain encodes

relevant associations and forms new associative networks.

 

-----------------------------------------------------------

Lee de-Wit (PhD), Post-Doctoral Researcher

Gestalt ReVision Project, University of Leuven.

https://sites.google.com/site/leehdewit/

http://ppw.kuleuven.be/labexppsy/gestaltrevision/

lee.dewit@psy.kuleuven.be, ++32 16 326 143

-----------------------------------------------------------

 

Decoding of coherent but not incoherent motion signals in early dorsal visual cortex

http://www.sciencedirect.com/science/article/pii/S1053811910004052#bb0190

 

NeuroImage
Volume 56, Issue 2, 15 May 2011, Pages 688-698
Multivariate Decoding and Brain Reading

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doi:10.1016/j.neuroimage.2010.04.011 | How to Cite or Link Using DOI

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Decoding of coherent but not incoherent motion signals in early dorsal visual cortex

Sponsored Article

Dietrich Samuel Schwarzkopf^a, b, , , Philipp Sterzer^c, 3 and Geraint Rees^{a, b, 1, 2}

^a UCL Institute of Cognitive Neuroscience, 17 Queen Square, London WC1N 3AR, UK

^b Wellcome Trust Centre for Neuroimaging at UCL, 12 Queen Square, London WC1N 3BG, UK

^c Department of Psychiatry, Charité Campus Mitte, Charitéplatz 1, D-10117 Berlin, Germany

Available online 10 April 2010. 

 

Abstract

When several scattered grating elements are arranged in such a way that their directions of motion are consistent with a common path, observers perceive them as belonging to a globally coherent moving object. Here we investigated how this coherence changes the representation of motion signals in human visual cortex using functional magnetic resonance imaging (fMRI) and multivariate voxel pattern decoding, which have the potential to reveal how well a stimulus is encoded in different contexts. Only during globally coherent motion was it possible to reliably distinguish fMRI signals evoked by different directions of motion in early visual cortex. This effect was specific to the retinotopic representation of the visual field quadrant in V1 traversed by the coherent element path and could not simply be attributed to a general increase in signal strength. Decoding was more reliable for cortical areas corresponding to the lower visual field. Because some previous studies observed poorer speed discrimination when motion was grouped, we also conducted behavioural experiments to investigate this with our stimuli, but did not reveal a consistent relationship between coherence and perceived speed. Taken together, these data show that neuronal populations in early visual cortex represent information that could be used for interpreting motion signals as unified objects.