When vision and hearing are absent, intelligence does not disappear—symbols do. DeafBlind cognition reveals how meaning and thought arise before language.

Learning begins with prediction failure. When expected states do not occur, Prediction Feedback (PF) gates learning by shifting the system from exploitation to exploration. What stabilizes are not symbols, but predictive constraints—invariants that reliably reduce uncertainty across interaction. These constraints form functional ontologies: commitments about how the world behaves under action.

Thought is not inner speech. It is the serial traversal of stabilized ontological constraints, enabling internal simulation, comparison, and planning. In DeafBlind cognition, this traversal is tactile–motor; in hearing cognition, language may annotate it. Language does not generate thought.

Language is a structured, shared symbolic system that binds and transmits already-formed ontologies across individuals and generations. It enables society and collective intelligence, but it is not the biological source of meaning.

This ordering—prediction before symbol, ontology before language—has direct implications for AI. Biologically aligned intelligence must begin with embodied prediction and PF-gated ontology formation; language, if added, should function as an interface, not as a substrate.

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Why do we become the particular persons we are, rather than any of the countless alternatives we might have been? This question precedes morality, psychology, and law. It is asked whenever we judge, diagnose, forgive, or punish. Yet most answers assume that identity is chosen, inherited as character, or consciously learned. This essay argues otherwise. Human identity emerges before intention, self-reflection, or moral reasoning exist. During early development, the brain passively accumulates associations and stabilizes them through Prediction Feedback (PF), a pre-conscious signal of predictive coherence. The resulting noetic horizon silently defines what feels natural, possible, and “like oneself.” Within this framework, crime and so-called perversions are not moral failures or genetic defects but intelligible outcomes of how identity stabilizes under unbalanced PF conditions. We are not the authors of who we are; we are the outcome of what once made our inner world coherent.

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Emotions are not chemical reactions, neural firings, or conscious feelings. They are inherited semantic patterns that evolved to be expressed and recognized. Each basic emotion is instantiated through two distinct but coupled systems: an expression pattern that organizes the body into a meaningful social signal, and a recognition pattern that detects these signals in others and in oneself through visual, auditory, and interoceptive channels. Emotional feeling does not generate emotion; it emerges later as the perceptual recognition of the body’s own expressed state. By separating expression from recognition and locating emotion in embodied semantic patterns rather than in transmitters or brain regions, this framework explains emotional universality, infant emotional competence, cross-cultural recognition, and the persistent confusion between bodily signals and felt experience.

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This open letter challenges Mathias Döpfner’s concept of “Performance Patriotism” by arguing that performance, growth, and wealth are not foundations of societal strength but secondary signals that emerge from deeper structural coherence. Drawing on the framework of Eidoism, the text critiques Europe’s fixation on competitiveness and acceleration, warning that systems optimized for constant performance become fragile, dependent on external validation, and prone to instability. Instead of faster growth or louder demonstrations of power, the letter proposes a form-based perspective in which sovereignty, resilience, and cultural confidence arise from internal alignment, structural balance, and the ability to function sustainably without permanent pressure to outperform others.

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Nations are not held together by shared beliefs or unanimous agreement. They remain stable because citizens develop a shared sense of reality—a common set of expectations about what is real, what consequences will follow actions, and which futures are plausible. This sense of reality operates below ideology and opinion and is reinforced through institutions, rituals, and social consequences. The public return of North Korean soldiers from foreign deployment illustrates how societies actively repair and stabilize these shared expectations, absorbing potentially disruptive experiences into a coherent national order rather than allowing them to fracture it.

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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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Despite unprecedented global connectivity, modern societies are experiencing rising polarization, nationalism, and everyday cultural conflict. This essay argues that these tensions are not driven by ignorance, moral failure, or technological fear, but by a structural imbalance between the speed of change and the capacity of cultures to maintain shared expectations. The internet and artificial intelligence amplify dominant global reference frames without providing mechanisms for local integration, creating cultural compression and loss of predictability in daily life. In response, “Me First” politics and renewed nationalism emerge as rational stabilization strategies rather than ideological regressions. Understanding this dynamic reframes contemporary conflict as a systems-design problem—one that requires respecting cultural boundaries and integration limits if global cooperation is to remain viable.

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As Vietnam integrates global AI systems into ministries, cities, banks, and courts, the central challenge is not whether to use artificial intelligence, but how to prevent statistical optimization from becoming social authority. Restricting AI adoption until fully Vietnamese-trained models exist would be both impractical and counterproductive. Instead, Vietnam’s stability depends on conditioning AI use through governance overlays that encode peripheral and rural realities into decision-making processes.

This approach reframes AI as an advisory instrument rather than a normative judge. Cultural impact layers, regional context injection, and formally empowered human override ensure that efficiency does not eclipse legitimacy. By embedding Vietnamese social logic—particularly informal, relational, and region-specific norms—into AI-governed centers, the state can modernize without marginalizing large parts of society.

The future risk is not technological dependence, but predictive exclusion. Vietnam’s task is therefore to preserve agency over outcomes while leveraging global AI tools, ensuring that modernization strengthens cohesion rather than producing silent division and long-term unhappiness.

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Structural neuroimaging consistently reveals small but statistically significant differences in average brain morphology across human populations. These findings are often misinterpreted as evidence of inherent cognitive or behavioral divergence. This essay argues that such inferences are technically invalid. Macro-scale brain measures—such as volume, cortical thickness, and white matter integrity—operate within a functional vacuum: they lack a reliable causal mapping to cognition or behavior. Cognitive capacity arises not from physical bulk, but from the brain’s semantic–associative architecture and its regulation by internal Prediction Feedback. Observed structural differences are therefore best understood as biomarkers of environmental and socioeconomic disparity, not determinants of intelligence or behavioral potential.

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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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