@article {2883, title = {There{\textquoteright}s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [code]}, year = {2016}, abstract = {

When searching for an object in a scene, how does the brain decide where to look next? Visual search theories suggest the existence of a global {\textquotedblleft} priority map {\textquotedblright} that integrates bottom-up visual information with top-down, target-speci fi c signals. We propose a mechanistic model of visual search that is consistent with recent neurophysiological evidence, can localize targets in cluttered images, and predicts single-trial behavior in a search task. This model posits that a high-level retinotopic area selective for shape features receives global, target-speci fi c modulation and implements local normalization through divisive inhibition. The normalization step is critical to prevent highly salient bottom-up features from monopolizing attention. The resulting activity pattern constitues a priority map that tracks the correlation between local input and target features. The maximum of this priority map is selected as the locus of attention. The visual input is then spatially enhanced around the selected location, allowing object-selective visual areas to determine whether the target is present at this location. This model can localize objects both in array images and when objects are pasted in natural scenes. The model can also predict single-trial human fi xations, including those in error and target-absent trials, in a search task involving complex objects.


To view more information and dowload code, etc. please visit the project website - http://klab.tch.harvard.edu/resources/miconietal_visualsearch_2016.html$\#$sthash.KmHoBPsk.XILaGVDV.dpbs


The corresponding publication can be found here.


The corresponding dataset entry can be found here.

}, author = {Thomas Miconi and Laura Groomes and Gabriel Kreiman} } @article {2884, title = {There{\textquoteright}s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]}, year = {2016}, abstract = {

When searching for an object in a scene, how does the brain decide where to look next? Visual search theories suggest the existence of a global {\textquotedblleft} priority map {\textquotedblright} that integrates bottom-up visual information with top-down, target-speci fi c signals. We propose a mechanistic model of visual search that is consistent with recent neurophysiological evidence, can localize targets in cluttered images, and predicts single-trial behavior in a search task. This model posits that a high-level retinotopic area selective for shape features receives global, target-speci fi c modulation and implements local normalization through divisive inhibition. The normalization step is critical to prevent highly salient bottom-up features from monopolizing attention. The resulting activity pattern constitues a priority map that tracks the correlation between local input and target features. The maximum of this priority map is selected as the locus of attention. The visual input is then spatially enhanced around the selected location, allowing object-selective visual areas to determine whether the target is present at this location. This model can localize objects both in array images and when objects are pasted in natural scenes. The model can also predict single-trial human fi xations, including those in error and target-absent trials, in a search task involving complex objects.


To view more information and dowload datasets, etc. please visit the project website - http://klab.tch.harvard.edu/resources/miconietal_visualsearch_2016.html$\#$sthash.KmHoBPsk.XILaGVDV.dpbs


The corresponding publication can be found here.


The corresponding code entry can be found here.

}, author = {Thomas Miconi and Laura Groomes and Gabriel Kreiman} } @article {2124, title = {There{\textquoteright}s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task}, journal = {Cerebral Cortex}, volume = {26(7)}, year = {2016}, pages = {26:3064-3082}, abstract = {

When searching for an object in a scene, how does the brain decide where to look next? Visual search theories suggest the existence of a global {\textquotedblleft}priority map{\textquotedblright} that integrates bottom-up visual information with top-down, target-specific signals. We propose a mechanistic model of visual search that is consistent with recent neurophysiological evidence, can localize targets in cluttered images, and predicts single-trial behavior in a search task. This model posits that a high-level retinotopic area selective for shape features receives global, target-specific modulation and implements local normalization through divisive inhibition. The normalization step is critical to prevent highly salient bottom-up features from monopolizing attention. The resulting activity pattern constitues a priority map that tracks the correlation between local input and target features. The maximum of this priority map is selected as the locus of attention. The visual input is then spatially enhanced around the selected location, allowing object-selective visual areas to determine whether the target is present at this location. This model can localize objects both in array images and when objects are pasted in natural scenes. The model can also predict single-trial human fixations, including those in error and target-absent trials, in a search task involving complex objects.

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Publisher released this paper early online on June 19, 2015.

}, author = {Thomas Miconi and Laura Groomes and Gabriel Kreiman} } @article {441, title = {A normalization model of visual search predicts single trial human fixations in an object search task.}, number = {008}, year = {2014}, month = {04/2014}, abstract = {

When searching for an object in a scene, how does the brain decide where to look next? Theories of visual search suggest the existence of a global attentional map, computed by integrating bottom-up visual information with top-down, target-specific signals. Where, when and how this integration is performed remains unclear. Here we describe a simple mechanistic model of visual search that is consistent with neurophysiological and neuroanatomical constraints, can localize target objects in complex scenes, and predicts single-trial human behavior in a search task among complex objects. This model posits that target-specific modulation is applied at every point of a retinotopic area selective for complex visual features and implements local normalization through divisive inhibition. The combination of multiplicative modulation and divisive normalization creates an attentional map in which aggregate activity at any location tracks the correlation between input and target features, with relative and controllable independence from bottom-up saliency. We first show that this model can localize objects in both composite images and natural scenes and demonstrate the importance of normalization for successful search. We next show that this model can predict human fixations on single trials, including error and target-absent trials. We argue that this simple model captures non-trivial properties of the attentional system that guides visual search in humans.

}, keywords = {Circuits for Intelligence, Pattern recognition}, author = {Thomas Miconi and Laura Groomes and Gabriel Kreiman} }