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首页> 外文期刊>Advances in Experimental Medicine and Biology >Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation.
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Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation.

机译:感知行动学习作为一种识别的自我更新认知表示的识别思想 - 一致的模型。

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摘要

As well as having the ability to formulate models of the world capable of experimental falsification, it is evident that human cognitive capability embraces some degree of representational plasticity, having the scope (at least in infancy) to modify the primitives in terms of which the world is delineated. We hence employ the term 'cognitive bootstrapping' to refer to the autonomous updating of an embodied agent's perceptual framework in response to the perceived requirements of the environment in such a way as to retain the ability to refine the environment model in a consistent fashion across perceptual changes.We will thus argue that the concept of cognitive bootstrapping is epistemically ill-founded unless there exists an a priori percept/motor interrelation capable of maintaining an empirical distinction between the various possibilities of perceptual categorization and the inherent uncertainties of environment modeling.As an instantiation of this idea, we shall specify a very general, logically-inductive model of perception-action learning capable of compact re-parameterization of the percept space. In consequence of the a priori percept/action coupling, the novel perceptual state transitions so generated always exist in bijective correlation with a set of novel action states, giving rise to the required empirical validation criterion for perceptual inferences. Environmental description is correspondingly accomplished in terms of progressively higher-level affordance conjectures which are likewise validated by exploratory action.Application of this mechanism within simulated perception-action environments indicates that, as well as significantly reducing the size and specificity of the a priori perceptual parameter-space, the method can significantly reduce the number of iterations required for accurate convergence of the world-model. It does so by virtue of the active learning characteristics implicit in the notion of cognitive bootstrapping.
机译:除了能够制定能够进行实验伪造的世界模型的能力,显然人类认知能力拥有一定程度的代表性可塑性,具有范围(至少在婴儿期)以修改世界的原语划定。因此,我们采用了“认知自动启动”一词,以指的是响应于环境的感知的感知框架的自主更新,以便在感知中以持续的方式保持改进环境模型的能力因此,我们将争辩说,除非存在能够保持经验性区分的先验感知分类和环境建模的固有不确定性,否则认知举止的概念是认知的,除非存在能够维持经验性的经验性,本想法的实例化,我们将指定能够紧凑地重新参数化的感知行动学习非常一般,逻辑归纳模型。作为先验的感知/动作耦合的结果,如此产生的新型感知状态转换始终存在于与一组新颖的动作状态的基础相关性中,从而产生了对感知推断的所需经验验证标准。在逐步验证的透析动作同样验证的逐步更高级别的住客猜想方面相应地完成了环境描述。这种机制在模拟的感知 - 动作环境中的应用表明,并显着降低了先验感知参数的大小和特异性 - 空间,该方法可以显着减少世界模型准确融合所需的迭代次数。它凭借在认知引导概念中隐含的主动学习特征来实现。

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