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首页> 外文会议>Eighth Neural Computation and Psychology Workshop; 20030828-30; University of Kent(GB) >APPLYING FORWARD MODELS TO SEQUENCE LEARNING: A CONNECTIONIST IMPLEMENTATION
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APPLYING FORWARD MODELS TO SEQUENCE LEARNING: A CONNECTIONIST IMPLEMENTATION

机译：将正向模型应用于序列学习：一种连接主义的实现

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摘要

The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed - and often implicit -sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations between successive stimuli, but also on sensitivity to the associations between successive responses, and on the predictive relationships that exist between these sequences of responses and their effects in the environment. In this paper, we offer an initial exploration of an alternative architecture for sequence learning, based on the principles of Forward Models.
机译：在时间和顺序上下文中处理事件的能力是通过与动态环境不断交互而强制执行的一项基本技能。序列学习研究表明，受试者对刺激流的顺序结构表现出详细的（通常是隐式的）敏感性。但是，当前在所谓的“串行反应时间任务”（SRT）中的表现主义连接模型未能捕捉到以下事实：序列学习不仅可以基于对连续刺激之间的顺序关联的敏感性，而且可以基于对关联的敏感性连续响应之间的关系，以及这些响应序列及其在环境中的影响之间存在的预测关系。在本文中，我们基于前向模型的原理，为序列学习的替代体系结构提供了初步的探索。

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