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主权2的具身大脑:从视觉搜索和导航过程中的空间可变意识感知到学习不变的物体类别以及获取有价值目标的认知情感计划。

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.

作者信息

Grossberg Stephen

机构信息

Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Departments of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering, Boston University, Boston, MA, United States.

出版信息

Front Comput Neurosci. 2019 Jun 25;13:36. doi: 10.3389/fncom.2019.00036. eCollection 2019.

Abstract

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 , 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.

摘要

本文建立了一个关于反应性行为和计划性行动如何实时交互的模型。动物和动物机器人的控制器都需要用于探索的反应机制,以及在环境熟悉后能够高效到达目标物体的习得计划。SOVEREIGN模型体现了这些能力,并在3D虚拟现实环境中进行了测试。神经模型已经刻画了一些重要的适应性和智能过程,但这些过程并未包含在SOVEREIGN模型中。本文总结了一个主要的研究计划,通过该计划将这些过程持续整合到一个名为SOVEREIGN2的增强模型中。关键的新感知、认知、认知情感和导航过程需要反馈网络,这些网络调节支持视觉、感觉和认知的有意识体验的共振脑状态。还包括哺乳动物新皮层“什么”和“哪里”处理流的计算互补过程,以及空间导航和手臂运动控制的同源机制。这些包括:使用协调的平稳跟踪和扫视运动来跟踪不可预测移动的目标。在“哪里”流中计算目标和当前位置的估计值,并可激活接近运动。运动线索可引发定向运动,以使新目标进入视野。累积运动估计值来自视觉和前庭线索。任意导航路线作为转弯角度和转弯之间行进距离的标记图逐步学习。嘈杂和不完整的视觉传感器数据被转换为视觉形式和运动的表示。在“什么”流中学习不变识别类别。不变物体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。存储的序列触发认知和空间/运动序列类别或计划(也称为)的学习,这些计划控制朝向有价值目标物体的计划性决策和运动。通过强化学习和激励动机学习,预测成功的列表组块组合被选择性地增强或抑制。预期与意外事件的不一致调节这些增强和抑制过程。自适应定时学习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能够通过从不同空间有利位置观察教师来进行技能的模仿学习。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a06a/6620614/22849f57d2fc/fncom-13-00036-g001.jpg

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