Toward a Unified Sub-symbolic Computational Theory of Cognition.
Toward a Unified Sub-symbolic Computational Theory of Cognition.
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迈向认知的统一次符号计算理论。
DOI：
10.3389/fpsyg.2016.00925
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发表时间：
2016
影响因子：
3.8
通讯作者：
Butz MV
中科院分区：
文献类型：
--
作者：
Butz MV
本文提出了如何将各种认知学科理论结合成一个统一的、亚符号的、计算的认知理论。考虑整合以下理论：心理学理论，包括事件编码理论，事件分割理论，预期行为控制理论和概念发展;人工智能和机器学习理论，包括强化学习和生成人工神经网络;以及理论和计算神经科学理论，包括预测编码和基于自由能的推理。鉴于这种潜在的统一，本文讨论了如何从积极收集的感觉运动经验中学习抽象的认知、概念化的知识和理解。这种统一建立在基于自由能的推理原理之上，这基本上意味着大脑建立了一个预测性的、生成性的环境模型。神经活动导向的推理导致当前活动预测编码的连续适应。神经结构导向的推理导致整个生成模型的长期适应。最后，主动推理努力维持内部稳态，导致目标导向的运动行为。然而，为了学习抽象的、层次化的编码，有人提出，基于自由能的推理需要用结构先验来增强，这会使认知发展偏向于形成特定的、行为上合适的编码结构。因此，人们假设抽象概念如何从感觉运动经验发展而来，以及它们如何由感觉运动经验构建并扎根于感觉运动经验。此外，它是勾勒出如何符号样的思想可以产生的一个暂时活跃的预测编码，这构成了一个分布式的神经吸引子的形式，一个互动的自由能最小。被激活的交互式网络吸引子本质上表征了概念或概念组合的语义，例如我们环境中的实际或想象情况。吸引子的时间序列然后编码展开语义，这可能是由与我们环境中的实际或想象情况的行为或心理交互产生的。本文最后讨论了这一理论的意义、进一步的预测、可能的验证和证伪，以及对一个完整的统一认知理论的潜在增强。
This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development toward the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification, and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper.