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The mechanisms of feature inheritance as predicted by a systems-level model of visual attention and decision making.

Hamker FH - Adv Cogn Psychol (2008)

Bottom Line: We find that the presence of feedback loops alone is sufficient to account for feature inheritance.Although our simulations do not cover all experimental variations and focus only on the general principle, our result appears of specific interest since the model was designed for a completely different purpose than to explain feature inheritance.We suggest that feedback is an important property in visual perception and provide a description of its mechanism and its role in perception.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Westf.-Wilhelms-Universität Münster, Germany.

ABSTRACT
Feature inheritance provides evidence that properties of an invisible target stimulus can be attached to a following mask. We apply a systemslevel model of attention and decision making to explore the influence of memory and feedback connections in feature inheritance. We find that the presence of feedback loops alone is sufficient to account for feature inheritance. Although our simulations do not cover all experimental variations and focus only on the general principle, our result appears of specific interest since the model was designed for a completely different purpose than to explain feature inheritance. We suggest that feedback is an important property in visual perception and provide a description of its mechanism and its role in perception.

No MeSH data available.


Related in: MedlinePlus

Model for visual attention. First, information about the content and its							low level stimulus-driven salience is extracted. (Stimulus-driven							saliency, however, will not be crucial for the results obtained here.)							This information is sent further downstream to V4 and to IT cells which							are broadly tuned to location. A target template is encoded in PF memory							(PFmem) cells. Feedback from PFmem to IT increases the strength of all							features in IT matching the template. Feedback from IT to V4 sends the							information about the target downwards to cells with a higher spatial							tuning. FEF visuomovement (FEFv) cells combine the feature information							across all dimensions and indicate salient or relevant locations in the							scene. The FEF movement (FEFm) cells compete for the target location of							the next eye movement. The activity of the FEF movement cells is also							sent to V4 and IT for gain modulation. However, in all simulations we							set the model to fixate, which results in a suppression of the FEF							movement activity. The IOR map is not used for the experiments simulated							here.
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Figure 1: Model for visual attention. First, information about the content and its low level stimulus-driven salience is extracted. (Stimulus-driven saliency, however, will not be crucial for the results obtained here.) This information is sent further downstream to V4 and to IT cells which are broadly tuned to location. A target template is encoded in PF memory (PFmem) cells. Feedback from PFmem to IT increases the strength of all features in IT matching the template. Feedback from IT to V4 sends the information about the target downwards to cells with a higher spatial tuning. FEF visuomovement (FEFv) cells combine the feature information across all dimensions and indicate salient or relevant locations in the scene. The FEF movement (FEFm) cells compete for the target location of the next eye movement. The activity of the FEF movement cells is also sent to V4 and IT for gain modulation. However, in all simulations we set the model to fixate, which results in a suppression of the FEF movement activity. The IOR map is not used for the experiments simulated here.

Mentions: The model consists of visual areas V4, inferotemporal (IT) cortex, prefrontal areas that contain the frontal eye field (FEF) for saccade planning and more ventrolateral parts for implementing functions of working memory (Fig. 1). If we present a visual scene to the model, features such as color, intensity and orientation are computed from the image. We will here consider only the orientation channel.


The mechanisms of feature inheritance as predicted by a systems-level model of visual attention and decision making.

Hamker FH - Adv Cogn Psychol (2008)

Model for visual attention. First, information about the content and its							low level stimulus-driven salience is extracted. (Stimulus-driven							saliency, however, will not be crucial for the results obtained here.)							This information is sent further downstream to V4 and to IT cells which							are broadly tuned to location. A target template is encoded in PF memory							(PFmem) cells. Feedback from PFmem to IT increases the strength of all							features in IT matching the template. Feedback from IT to V4 sends the							information about the target downwards to cells with a higher spatial							tuning. FEF visuomovement (FEFv) cells combine the feature information							across all dimensions and indicate salient or relevant locations in the							scene. The FEF movement (FEFm) cells compete for the target location of							the next eye movement. The activity of the FEF movement cells is also							sent to V4 and IT for gain modulation. However, in all simulations we							set the model to fixate, which results in a suppression of the FEF							movement activity. The IOR map is not used for the experiments simulated							here.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2864986&req=5

Figure 1: Model for visual attention. First, information about the content and its low level stimulus-driven salience is extracted. (Stimulus-driven saliency, however, will not be crucial for the results obtained here.) This information is sent further downstream to V4 and to IT cells which are broadly tuned to location. A target template is encoded in PF memory (PFmem) cells. Feedback from PFmem to IT increases the strength of all features in IT matching the template. Feedback from IT to V4 sends the information about the target downwards to cells with a higher spatial tuning. FEF visuomovement (FEFv) cells combine the feature information across all dimensions and indicate salient or relevant locations in the scene. The FEF movement (FEFm) cells compete for the target location of the next eye movement. The activity of the FEF movement cells is also sent to V4 and IT for gain modulation. However, in all simulations we set the model to fixate, which results in a suppression of the FEF movement activity. The IOR map is not used for the experiments simulated here.
Mentions: The model consists of visual areas V4, inferotemporal (IT) cortex, prefrontal areas that contain the frontal eye field (FEF) for saccade planning and more ventrolateral parts for implementing functions of working memory (Fig. 1). If we present a visual scene to the model, features such as color, intensity and orientation are computed from the image. We will here consider only the orientation channel.

Bottom Line: We find that the presence of feedback loops alone is sufficient to account for feature inheritance.Although our simulations do not cover all experimental variations and focus only on the general principle, our result appears of specific interest since the model was designed for a completely different purpose than to explain feature inheritance.We suggest that feedback is an important property in visual perception and provide a description of its mechanism and its role in perception.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Westf.-Wilhelms-Universität Münster, Germany.

ABSTRACT
Feature inheritance provides evidence that properties of an invisible target stimulus can be attached to a following mask. We apply a systemslevel model of attention and decision making to explore the influence of memory and feedback connections in feature inheritance. We find that the presence of feedback loops alone is sufficient to account for feature inheritance. Although our simulations do not cover all experimental variations and focus only on the general principle, our result appears of specific interest since the model was designed for a completely different purpose than to explain feature inheritance. We suggest that feedback is an important property in visual perception and provide a description of its mechanism and its role in perception.

No MeSH data available.


Related in: MedlinePlus