Human personality does not originate in moral choice or conscious reasoning. Long before the brain can think symbolically, it evaluates. From birth, inherited neural comparators continuously distinguish comfort from discomfort, safety from threat, and coherence from instability. These evaluations regulate early prediction patterns through Predictive Feedback (PF), while emotions function as broadcast signals of the brain’s internal regulatory state—coordinating action internally and communicating condition externally.

During early childhood, repeated emotional and social interactions calibrate these comparators and stabilize specific predictive pathways. This process shapes the developing prefrontal cortex and biases how the individual later restores internal balance. What societies eventually label as “good” or “bad” personality traits are not moral properties encoded in the brain, but observable outcomes of this early regulatory development. Understanding personality in this way shifts the question from judgment to development, and from ethics to neurobiological regulation.

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Robots are unlikely to enter history first as helpers, caregivers, or household assistants. They will enter as weapons.
Throughout history, transformative technologies—from metallurgy to aviation to computing—reached scale through warfare before reshaping civilian life. Robotics follows the same trajectory. Civil society resists failure, liability, and disruption; warfare rewards speed, scale, and expendability.

The China–Taiwan conflict sits at the intersection of this technological shift. China’s industrial capacity, growing autonomy in AI and navigation, cooperation with Russia’s battlefield experience, and a stabilizing BRICS environment together reduce the traditional costs of escalation. In this setting, robotic warfare is not an exotic option but the most rational first use case.

If large-scale autonomous systems are deployed anywhere as a primary instrument of force, Taiwan is one of the most likely places where this new era of warfare will begin.

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Current robotic AI systems excel in perception and manipulation, yet they remain fundamentally non-autonomous. The missing element is not computational power or data, but an internal regulatory mechanism equivalent to biological Predictive Feedback (PF). PF is a continuous, inherited comparator that evaluates predicted versus actual internal activations, driving self-learning, self-correction, and intrinsic motivation. This essay argues that without PF, robotic systems cannot develop genuine cognitive autonomy. It proposes a biologically grounded four-layer architecture—Pattern Repository, Entity Generator, Associative Pointer Matrix, and Predictive Feedback Loop—that transforms robots from externally instructed executors into self-organizing predictive agents capable of internal reasoning, adaptive exploration, and robust behavior in novel environments.

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Love is not an emotion in the classical or neuroscientific sense, nor is it a hormone-driven state or a learned social script. Within a Predictive Feedback (PF)–based model of cognition, love emerges as a resonance phenomenon: a self-stabilizing loop between sustained positive PF and its rendering in perceptual awareness. Emotions, in this framework, are blind, non-directed broadcasts of the organism’s current mental state, implemented through inherited physiological patterns and recognized by equally inherited perceptual comparators. Feelings arise only when awareness interprets these broadcasts using learned entities and contextual associations. Love, therefore, is neither broadcast nor comparator output, but a persistent PF-positive resonance that awareness repeatedly reifies as a coherent feeling. When prediction confirmation collapses, love dissolves—not because an emotion has ended, but because the PF resonance that sustained the feeling has broken.

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Happiness is not a goal the brain actively pursues, nor is unhappiness a reliable trigger for self-improvement. In a Predictive Feedback (PF) framework, happiness emerges when prediction error is low and stable—when the system no longer needs to escalate corrective effort. Unhappiness, by contrast, appears in two fundamentally different regimes: PF escalation, which produces anxiety, restlessness, and motivation to change; and PF collapse, in which persistent, unsolvable prediction error leads to withdrawal, apathy, and the loss of initiative commonly labeled depression. The widespread belief that suffering should automatically generate growth reflects a category error. Motivation depends not on negative feeling, but on whether PF still judges prediction as solvable. Depression is therefore not failed happiness-seeking, but predictive disengagement.

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