Learning to Learn: Patterns and the Human Brain

As when building any mental or synthetic model, it is important to establish a baseline of thought that will allow for necessary abstract conceptualization and metatheoretical contextualization of the information presented — or to provide computational principles allowing for informational processes to build an understanding. Thus, it's imperative to discuss a design function of the DTBM as a pattern-seeking device. This functionality is twofold: it searches simultaneously for cause-based and solution-based algorithms or algorithmic information for the issues that are primary to Emotional Warfare. This structuring is designed to work for the human person or a synthetic agent in a manner similar to meta-learning, the formal concept in deep machine learning commonly referred to or understood as “learning to learn.” This broad topic is explored in more detail in the Essay Collection, Vol. 1, especially regarding reinforcement and deep reinforcement learning; while not directly discussed here, these points of interest also relate directly to relevant topics such as supervised, semi-supervised, and unsupervised learning (or self-organization), working memory, and issues central to intelligence and memory systems, e.g., procedural, semantic, episodic, and so on or the “corresponding varieties of consciousness (anoetic, noetic, and autonoetic).”1 Meta-learning has roots in the cognitive sciences and the technology-driven fields that produce cognitive architectures and, more broadly, artificial intelligence (AI).

To explore the remaining content from the most useful mindset, the focus here will be on meta-learning with the human person as the central figure but within premises based on AI: the human person or agent uses experience, which is denoted throughout the One Divide/Emotional Warfare platform as practice, to change elements or aspects of the person's own learning algorithm. The modified learner in this situation is the human person — with a purposive volition, a topic that is expanded on, especially regarding will and free will, in the Emotional Warfare Essay Collection, Vol. 1 — learning about Emotional Warfare and its Pattern(s)' intra-interplay, who with practice attains an understanding or levels of understanding (which, within One Divide, is considered a form of maturation of conceptualization) of Emotional Warfare and thus advances beyond the original version of the human person who was first learning. The same would apply to a synthetic agent.

This modification — or improved performance — takes place due to the experience(s) attained through One Divide's two-phase process of gaining first awareness and then an explicit understanding of Emotional Warfare and its Pattern(s) — whether in terms or perspectives of deepening (as in deep neural networks) or as a process and methodology of elevating or optimizing the capability or consciousness of the living system, with understanding of the issues of attention, meaning “the control that the organism, or environmental events, can exert over the direction of consciousness in the selection of ‘contents' of awareness.”2 Importantly, there are no restrictions to levels of meta-learning that can be attained, as the understanding continually matures with practice, allowing the person to go on reaching new earned levels and increased or deepened knowledge (i.e., emotional intelligence, social intelligence, and abstract intelligence), improved decision-making processes, and overall transferrable attributes that provide the agency and efficacy of a True Self state of being, which maps over to multiple domains. Such meta-learning expands agency and efficacy parameters rather than constraining agency and efficacy to domain-specific territories, for instance from the perspective of cognitive science, cognitive architecture, or deep machine learning. Consider briefly “multi-objective” learning or shared parameters and the like, which can also include task-specific learning.

In all, One Divide's structural diagram and resulting structural analytics provided by the DTBM, and One Divide's algorithmic information provided primarily through the binary structure of the EBSS, work as a learning algorithm or multiple learning algorithms that operate on both a base level and a meta level. From a philosophy of science perspective, the platform is thus situated within meta-learning and optimization. As elaborated on in the Essay Collection, this yields a psychological model flexibility and a type of program-modifying programming that offers both short- and long-term agent (and efficacy) optimization. Infusing a meta-learning design into One Divide's flexible psychological model provides an additional layer of utility that can be directed toward the living, psychological human person or synthetically when speaking of an AI agent, machine learning, or deep machine learning.

This cognitive architecture (or neuro-computational programming) leads to and enhances pattern identification, processing, and recognition of new pattern schemes the person or agent at first could not see at all, could not see easily, or could not see easily in totality (e.g., Emotional Warfare's gestalt), particularly in the specific context of Emotional Warfare's deceptiveness. This cognitive architecture therefore allows the person or agent to find effective algorithmic information — recognizing problems — and additional meta-learning algorithms that provide solutions to those problems. As outlined in more generalizable terms in all of One Divide's philosophical literature, this twofold structuring contextualizes One Divide's cause/solution approach, which is aimed toward accurately and consistently identifying the “true negative” (i.e., Emotional Warfare and its agent, the False Self) through pattern processing, pattern recognition, and additional pattern-seeking premises that allow for the information to be understood and addressed objectively, ultimately yielding a “true positive.”

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  1. Tulving, E. (1985). Memory and consciousness. Canadian Psychology/Psychologie canadienne, 26(1), 1–12. Quote from abstract.
  2. Ibid. Quote from abstract.