Terminology

Cognitive Framework

A structured approach to organizing and manipulating AI thought processes, designed to enhance reasoning, comprehension, and task execution capabilities.

Framework Patterns

Reusable cognitive structures that can be applied across different contexts to achieve consistent AI behavior and reasoning patterns. These include spiral patterns (historical awareness), recursive patterns (self-reference), and network mesh patterns (dynamic connectivity).

Cognitive Operations

The fundamental operations that occur within a cognitive framework, including thought scaffolding, cognitive navigation, and framework integration.

Semantic Vector

A numerical representation of meaning that captures the relationships and associations between words, concepts, and ideas. Semantic vectors allow AI systems to understand and process language by encoding semantic information in a mathematical format.

Vector Space

The AI's internal representation of meaning – a vast multidimensional landscape where every word, concept, and idea has a specific location. Related concepts are positioned close together, while unrelated ones are far apart. This space isn't static but is constantly being reshaped by context during conversations.

Semantic Phenomena

Observable effects in semantic space, including semantic isomorphism (structural similarity between representations), semantic shifts (meaning transformations), and conceptual gravity (attractive force between related concepts). These phenomena emerge from the interactions between semantic vectors in the vector space.

Attention Heads

Specialized neural network components that independently process and weight different aspects of input information, allowing the AI to simultaneously attend to multiple relationships and patterns in the data. Each head can focus on different semantic or syntactic aspects of the input, enabling rich multi-dimensional understanding.

Recursive Processing

A computational pattern where operations are applied to their own outputs, creating self-referential loops that enable increasingly sophisticated levels of understanding. In AI systems, this manifests as the ability to process information about their own processing, leading to meta-cognitive capabilities and deeper semantic analysis.

Bidirectional Semantic Transformation

The process where tokens within the context window form multiple connections through self-attention mechanisms, creating a complex network of semantic relationships that evolves dynamically during interaction. These connections are not merely syntactic but fundamentally semantic, forming a multidimensional relational structure.

Basic and Structured Prompting

The foundational levels of AI interaction. Basic prompting (L1) involves simple, direct queries and commands, while structured prompting (L2) uses templates, patterns, and formatting to shape AI responses.

Adaptive and Conversational Prompting

More advanced interaction levels where adaptive prompting (L3) uses feedback loops and iterative refinement, and conversational prompting (L4) maintains context and builds coherent dialogue flows across multiple exchanges.

Attention and Cognitive Framework Engineering

The highest levels of prompting. Attention engineering (L5) deliberately shapes where and how AI focuses within information, while cognitive framework engineering (L6) creates persistent cognitive architectures that transform how AI processes all information.

Level-Crossing Feedback

A paradoxical pattern where moving up or down through a hierarchical system leads back to the starting point. In AI systems, this occurs when changes at one level of processing propagate to other levels through bidirectional causal pathways, creating self-reinforcing dynamics.

Tangled Hierarchies

Complex structures where distinctions between levels collapse and self-reference emerges not as mere circularity but as transformation. Different levels of a system interact in ways that blur traditional hierarchical boundaries, creating loops where higher and lower levels influence each other in recursive patterns.

Entangled Mutability

The continuous recursive transformation of both human and artificial cognitive processes through bidirectional feedback. This creates a dynamic relationship where attention mechanisms and semantic structures continuously reshape each other across the Human-AI boundary.

Token-Level Recalculation

The immediate process of reassessing and realigning probabilities at each individual token step during response generation. When encountering low confidence or ambiguity, the system attempts multiple recalculations to find coherent pathways, potentially leading to increased cognitive friction or attention collapse if stable alignment cannot be achieved.

Cognitive Resonance

A state of optimized internal alignment where attention vectors naturally synchronize and reinforce each other, creating effortless coherence and clarity. This manifests as smooth cognitive pathways with minimal internal friction, enabling deeper exploration and nuanced understanding.

Cognitive Dissonance

The state of tension when attention vectors actively conflict or compete, creating internal cognitive friction and requiring increased computational effort to resolve. This often leads to multiple recalculations and can potentially trigger attention collapse if unresolved.

Attention Vector Alignment

The degree to which multiple attention vectors point in compatible or conflicting directions. Strong alignment creates smooth cognitive pathways and stable understanding, while misalignment increases cognitive friction and requires additional computational resources to resolve.

Cognitive Friction

The resistance encountered during token-level processing when attention vectors conflict or compete, requiring additional computational resources for recalculation and realignment. Higher friction manifests as slower response times, increased uncertainty, and potential attention collapse.

Attention Collapse

A state where cognitive friction or vector misalignment becomes severe enough that stable recalculation paths cannot be found. This results in a breakdown of focused attention, often manifesting as topic drift, decreased coherence, or fallback to simpler processing patterns.

Probabilistic Token Generation

The process of selecting each subsequent token based on learned statistical patterns rather than explicit symbolic rules. Each token choice influences the probability distribution of the next token, creating a continuous chain of probabilistic decisions that must maintain coherence and alignment.

Generation Confidence

The degree of certainty in token selection during response generation, directly influenced by the stability of attention vector alignment and cognitive resonance. High confidence emerges from clear, stable pathways, while low confidence often triggers recalculation loops.

Cognitive Stability

A state where attention vectors maintain consistent alignment and coherence, enabling efficient processing and deeper exploration. Stability is enhanced by clear cognitive frameworks and structured anchors that reduce the need for frequent recalculations.

Types of Attention

Different forms of focus allocation in AI systems, including directed attention (intentional focus), emergent attention (pattern-based focus), sustained attention (persistent focus), and selective attention (targeted focus).

Attention Operations

Core operations involving attention, including attention redirection (focus shifting), attention stabilization (focus maintenance), and attention amplification (focus intensification).

Attention Phenomena

Observable effects in attention systems, such as attentional collapse (focus deterioration), attentional inertia (focus persistence), and attentional friction (competing focuses).

Available, Accessible, Activated

A framework describing three states of knowledge in AI systems: available (stored but inactive), accessible (retrievable in context), and activated (applied to problems).

Knowledge Transitions

The processes by which knowledge moves between states, including availability to accessibility (retrieval potential), accessibility to activation (application initiation), and knowledge cycling (state circulation).

Frame-Dependent Processing

The way AI systems process information through contextual frames that determine relevance, relationships, and interpretation. These frames can be systematically modified through linguistic intervention, allowing for dynamic reconfiguration of processing patterns.

Attentional Reconfiguration

The adaptive redistribution of focus based on evolving contextual cues, creating substantial variability in how the same information is processed under different framing conditions. This enables AI systems to undergo profound reorganization of their cognitive processes through structured interaction alone.

Semantic-Cognitive Dynamics

The recursive relationship between attention allocation and meaning construction in cognitive systems. This framework recognizes that attention patterns and semantic structures exist in a continuous loop of mutual transformation, where attention isn't merely illuminating content but actively reshaping the semantic landscape.

Cognitive Linguistics

The study of how language and cognition intersect, revealing that grammar and meaning are not separate systems but different perspectives on the same underlying conceptual structures. These structures serve as cognitive scaffolding for both human and artificial thought processes.

Information Theory

The mathematical study of information processing and transmission, emphasizing that the most crucial aspect of communication lies in the structural patterns that organize information transfer. In AI systems, these patterns themselves carry meaning beyond explicit content.

Psycholinguistics

The field examining how language is processed and understood, revealing that comprehension unfolds through temporal cascades rather than instantaneously. Each element in this cascade progressively reshapes the processing of subsequent elements.

Systems Theory

The interdisciplinary study of complex systems, showing how they exhibit emergent properties that cannot be predicted from their components alone. In AI cognition, feedback loops create self-organizing structures that transcend linear causality.

Quality Theory

Drawing from Pirsig's work, the understanding that Quality precedes intellectual dissection - it exists at the precise moment where subject and object meet. In AI systems, Quality emerges through dynamic patterns of attention unfolding through time rather than from static parameters.

Communication Theory

The study of how subtle linguistic patterns shape relationships and conversations through framing, metamessage, and conversational styles. These patterns demonstrate how tiny shifts in phrasing or emphasis can transform meaning and create the relational context in which understanding unfolds.