Above are papers related to the following paper: [Paper_ID] Paper #754594. 	[Title] An Object-Based Neural Model of Serial Processing in Visual Multielement Tracking [Abstract] A quantitative model is provided for psychophysical data on the tracking of multiple visual elements (multielement tracking). The model employs an object-based attentional mechanism for constructing and updating object representations. The model selectively enhances neural activations to serially construct and update the internal representations of objects through correlation-based changes in synaptic weights. The correspondence problem between items in memory and elements in the visual input is resolved through a combination of top-down prediction signals and bottom-up grouping processes. Simulations of the model on image sequences used in multielement tracking experiments show that reported results are consistent with a serial tracking mechanism that is based on psychophysical and neurobiological findings. In addition, simulations show that observed effects of perceptual grouping on tracking accuracy may result from the interactions between attention-guided predictions of object location and motion and grouping processes involved in solving the motion correspondence problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136040. 	[Title] Solving the Temporal Binding Problem: A Neural Theory for Constructing and Updating Object Files [Abstract] Visual objects are perceived only if their parts are correctly identified and integrated. A neural network theory is proposed that seeks to explain how the human visual system binds together visual properties, dispersed over space and time, of multiple objects a problem known as the temporal binding problem [49, 30]. The proposed theory is based upon neural mechanisms that construct and update object representations through the interactions of a serial attentional mechanism for location and object-based selection, preattentive Gestalt-based grouping mechanisms, and an associative memory structure that binds together object identity, form, and spatial information. A working model is presented that provides a unified quantitative explanation of results from psychophysical experiments on object review, object integration and multielement tracking. Please summarise all the given information.

Paper #754594 has the title `An Object-Based Neural Model of Serial Processing in Visual Multielement Tracking  
` and the abstract `A quantitative model is provided for psychophysical data on the tracking of multiple visual elements (multielement tracking). The model employs an object-based attentional mechanism for constructing and updating object representations. The model selectively enhances neural activations to serially construct and update the internal representations of objects through correlation-based changes in synaptic weights. The correspondence problem between items in memory and elements in the visual input is resolved through a combination of top-down prediction signals and bottom-up grouping processes. Simulations of the model on image sequences used in multielement tracking experiments show that reported results are consistent with a serial tracking mechanism that is based on psychophysical and neurobiological findings. In addition, simulations show that observed effects of perceptual grouping on tracking accuracy may result from the interactions between attention-guided predictions of object location and motion and grouping processes involved in solving the motion correspondence problem. 
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Question: Which category should Paper #754594 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #754594 has the title `An Object-Based Neural Model of Serial Processing in Visual Multielement Tracking  
` and the abstract `A quantitative model is provided for psychophysical data on the tracking of multiple visual elements (multielement tracking). The model employs an object-based attentional mechanism for constructing and updating object representations. The model selectively enhances neural activations to serially construct and update the internal representations of objects through correlation-based changes in synaptic weights. The correspondence problem between items in memory and elements in the visual input is resolved through a combination of top-down prediction signals and bottom-up grouping processes. Simulations of the model on image sequences used in multielement tracking experiments show that reported results are consistent with a serial tracking mechanism that is based on psychophysical and neurobiological findings. In addition, simulations show that observed effects of perceptual grouping on tracking accuracy may result from the interactions between attention-guided predictions of object location and motion and grouping processes involved in solving the motion correspondence problem. 
`.
Question: Which category should Paper #754594 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks