Above are papers related to the following paper: [Paper_ID] Paper #1119654. 	[Title] A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff [Abstract] We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff|one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #376704. 	[Title] Learning to Classify Observed Motor Behavior [Abstract] We present a representational format for observed movements The representation has a temporal structure relating components of a single complex movement. We also present OXBOW, an unsupervised learning system, which constructs classes of these movements. Empirical results indicate that the system builds abstract movement concepts with appropriate component structure allowing it to predict the latter portions of a partially observed movement. Please summarise all the given information.

Paper #1119654 has the title `A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff  
` and the abstract `We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff|one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. 
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Question: Which category should Paper #1119654 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1119654 has the title `A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff  
` and the abstract `We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff|one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. 
`.
Question: Which category should Paper #1119654 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: Case_Based