从神经阐述的时间动态看身份、代理和意识的产生

Riccardo Fesce
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摘要

身份--将自我与外部现实区分开来--代理--是自己行为的始作俑者--通常被认为是意识的固有属性,是由意识产生的心理建构。在此,我们提出一种不同的观点。所有生理系统都显示出复杂的随时间变化的调节机制,以适应或预测外部变化。为了与快速变化进行互动,动物需要一个能够模拟和预测(而不仅仅是表现)变化的神经系统。必须采用不同的算法,才能根据感官信息(接收延迟)预测物体的瞬间位置,或提前设计和指导运动轨迹。因此,外部事件和行动的时间动态必须以不同的方式加以处理,从而产生自我与非我("身份")之间的区别,作为神经元阐述的内在计算结构。处理时间并非神经元的设计初衷。神经元电路基于并行处理:每一点信息都在许多神经元上发散,每个神经元都将其与许多其他数据相结合。尖峰点燃报告了神经元所要响应的特定模式在传入数据中出现的可能性。这种组织结构似乎是为了处理同步数据集而设计的。然而,由于神经网络在处理过程中会产生延迟,因此可以将时间序列转换为同步模式,并对其进行分析。这样就可以实施预测算法,并通过神经元的可塑性不断加以改进。为了成功地与外部现实进行互动,神经系统必须建模和预测,而且还要通过记录不同时间点的信息,对感知功能或运动活动进行不同的处理。此外,为了通过正/负强化进行学习,建模必须在运动控制及其后果之间建立因果关系:感知中的相位滞后与运动程序中的相位领先(和控制)之间的对比产生了身份(从周围环境中辨别自我)和代理(对行动的控制),它们是模拟现实所必需的计算构造。这并不需要任何形式的意识。在能够产生意识的大脑中,这些构造可能会从单纯的计算要求演变为心理(意识)构造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The emergence of identity, agency and consciousness from the temporal dynamics of neural elaboration
Identity—differentiating self from external reality—and agency—being the author of one’s acts—are generally considered intrinsic properties of awareness and looked at as mental constructs generated by consciousness. Here a different view is proposed. All physiological systems display complex time-dependent regulations to adapt or anticipate external changes. To interact with rapid changes, an animal needs a nervous system capable of modelling and predicting (not simply representing) it. Different algorithms must be employed to predict the momentary location of an object based on sensory information (received with a delay), or to design in advance and direct the trajectory of movement. Thus, the temporal dynamics of external events and action must be handled in differential ways, thereby generating the distinction between self and non-self (“identity”) as an intrinsic computational construct in neuronal elaboration. Handling time is not what neurons are designed for. Neuronal circuits are based on parallel processing: each bit of information diverges on many neurons, each of which combines it with many other data. Spike firing reports the likelihood that the specific pattern the neuron is designed to respond to is present in the incoming data. This organization seems designed to process synchronous datasets. However, since neural networks can introduce delays in processing, time sequences can be transformed into simultaneous patterns and analysed as such. This way predictive algorithms can be implemented, and continually improved through neuronal plasticity. To successfully interact with the external reality, the nervous system must model and predict, but also differentially handle perceptual functions or motor activity, by putting in register information that becomes available at different time moments. Also, to learn through positive/negative reinforcement, modelling must establish a causal relation between motor control and its consequences: the contrast between phase lag in perception and phase lead (and control) in motor programming produces the emergence of identity (discerning self from surrounding) and agency (control on actions) as necessary computational constructs to model reality. This does not require any form of awareness. In a brain, capable of producing awareness, these constructs may evolve from mere computational requirements into mental (conscious) constructs.
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