首页> 美国卫生研究院文献>Frontiers in Computational Neuroscience >The Embodied Brain of SOVEREIGN2: From Space-Variant Conscious Percepts During Visual Search and Navigation to Learning Invariant Object Categories and Cognitive-Emotional Plans for Acquiring Valued Goals
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The Embodied Brain of SOVEREIGN2: From Space-Variant Conscious Percepts During Visual Search and Navigation to Learning Invariant Object Categories and Cognitive-Emotional Plans for Acquiring Valued Goals

机译:SOVEREIGN2的丰富大脑:从视觉搜索和导航过程中的空间变体意识感知到学习不变的物体类别和获得重要目标的认知情感计划

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摘要

This article develops a model of how reactive and planned behaviors interact in real time. Controllers for both animals and animats need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once an environment becomes familiar. The SOVEREIGN model embodied these capabilities, and was tested in a 3D virtual reality environment. Neural models have characterized important adaptive and intelligent processes that were not included in SOVEREIGN. A major research program is summarized herein by which to consistently incorporate them into an enhanced model called SOVEREIGN2. Key new perceptual, cognitive, cognitive-emotional, and navigational processes require feedback networks which regulate resonant brain states that support conscious experiences of seeing, feeling, and knowing. Also included are computationally complementary processes of the mammalian neocortical What and Where processing streams, and homologous mechanisms for spatial navigation and arm movement control. These include: Unpredictably moving targets are tracked using coordinated smooth pursuit and saccadic movements. Estimates of target and present position are computed in the Where stream, and can activate approach movements. Motion cues can elicit orienting movements to bring new targets into view. Cumulative movement estimates are derived from visual and vestibular cues. Arbitrary navigational routes are incrementally learned as a labeled graph of angles turned and distances traveled between turns. Noisy and incomplete visual sensor data are transformed into representations of visual form and motion. Invariant recognition categories are learned in the What stream. Sequences of invariant object categories are stored in a cognitive working memory, whereas sequences of movement positions and directions are stored in a spatial working memory. Stored sequences trigger learning of cognitive and spatial/motor sequence categories or plans, also called list chunks, which control planned decisions and movements toward valued goal objects. Predictively successful list chunk combinations are selectively enhanced or suppressed via reinforcement learning and incentive motivational learning. Expected vs. unexpected event disconfirmations regulate these enhancement and suppressive processes. Adaptively timed learning enables attention and action to match task constraints. Social cognitive joint attention enables imitation learning of skills by learners who observe teachers from different spatial vantage points.
机译:本文开发了一个模型,该模型是反应性行为和计划性行为如何实时交互的方式。动物和动物的控制者都需要反应机制进行探索,并且一旦环境变得熟悉,就可以学会有效地达到目标物体的学习计划。 SOVEREIGN模型体现了这些功能,并在3D虚拟现实环境中进行了测试。神经模型具有SOVEREIGN中未包括的重要自适应和智能过程的特征。本文总结了一个主要的研究程序,通过该程序可以将它们始终合并到称为SOVEREIGN2的增强模型中。关键的新知觉,认知,认知情感和导航过程需要反馈网络来调节共振的大脑状态,以支持有见识,感知和了解的意识体验。还包括哺乳动物新皮质What和Where处理流的计算互补过程,以及用于空间导航和手臂运动控制的同源机制。其中包括:使用协调的平稳跟踪和扫视运动来跟踪不可预测的运动目标。目标和当前位置的估计值在“位置”流中计算,并且可以激活进近运动。运动提示可以引起定向运动,以使新目标可见。累积运动估计值来自视觉和前庭提示。任意航行路线通过转弯的角度和转弯之间的距离的标记图逐步学习。嘈杂和不完整的视觉传感器数据会转换为视觉形式和运动的表示形式。在What流中学习不变识别类别。不变对象类别的序列存储在认知工作记忆中,而运动位置和方向的序列存储在空间工作记忆中。存储的序列会触发对认知和空间/运动序列类别或计划(也称为列表块)的学习,该类别或计划控制计划的决策和向有价值的目标对象的移动。通过强化学习和激励性动机学习,选择性地增强或抑制了预测成功的列表块组合。预期事件与意外事件的不一致性规范了这些增强和抑制过程。自适应定时学习可以使注意力和行动与任务约束相匹配。社会认知联合注意力使学习者可以从不同的空间角度观察教师,从而模仿学习技能。

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