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# Lagun's law and the foundations of cognitive drive architecture: A first principles theory of effort and performance

Nikesh Lagun

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Nikesh Lagun. Lagun's law and the foundations of cognitive drive architecture: A first principles theory of effort and performance. International Journal of Science and Research Archive, 2025, 15 (2), pp.831-861. 10.30574/ijsra.2025.15.2.1425. hal-05076126

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# Lagun's law and the foundations of cognitive drive architecture: A first principles theory of effort and performance

Nikesh Lagun *

Kathmandu, Nepal.

International Journal of Science and Research Archive, 2025, 15(02), 831-861

Publication history: Received on 01 April 2025; revised on 11 May 2025; accepted on 13 May 2025

Article DOI: https://doi.org/10.30574/ijsra.2025.15.2.1425

## Abstract

Individuals often fail to initiate or sustain effort despite having clear intent and adequate capability. While existing frameworks in motivation, attention, and executive function describe relevant correlates, they do not formalize the internal structure that determines whether Drive is mechanically possible. Cognitive architectures, such as ACT-R or SOAR, simulate task execution once cognition is already active, but they do not model the conditions that allow cognitive effort to begin. Cognitive Drive Architecture (CDA) is proposed as a new field within cognitive psychology to address this foundational gap. It defines Drive as the emergent output of real-time system configuration, rather than as a motivational state or behavioral trait. At the core of CDA is Lagunian Dynamics, a structural theory formalized through Lagun's Law of Primode and Flexion Dynamics. This theory introduces six internal variables—Primode, CAP (Cognitive Activation Potential), Flexion, Anchory, Grain, and Slip—that function across three operational domains: Ignition, Tension, and Flux. Each variable is defined by its structural role in the production and disruption of effort, rather than by psychological traits or surface behaviors. The Drive Equation models engagement as the result of interactions among ignition readiness, cognitive adaptability, attentional tethering, internal resistance, and systemic variability. This framework yields falsifiable predictions related to initiation failure, dropout patterns, and intraindividual performance inconsistency. Each component of the system can be operationalized using behavioral and physiological proxies, making the theory testable in both experimental and applied settings. CDA is not a motivational theory but a structural field focused on the conditions that make cognitive effort possible. It supports cross-domain applications in cognitive science, education, clinical practice, and adaptive system design.

**Keywords:** Cognitive Drive Architecture; Effort Modeling; Lagunian Dynamics; Ignition Threshold; Volitional Structure; Lagun's Law

## 1. Introduction

### 1.1. Why does initiation fail, even when intention, understanding, and motivation are in place?

Across domains such as education, therapy, and professional practice, individuals regularly report the same failure mode. The task is known. The importance is understood. Emotional investment is present. Still, no action begins. This behavior is not marginal. It recurs under varying conditions and is internally consistent. The cognitive system appears operational, but entry into effort does not occur.

This paper treats that breakdown as a structural misconfiguration, not a motivational deficit. The problem is not limited to one domain or condition. It reflects a missing piece in cognitive theory, one that has not yet been modeled with precision. Specifically, there is no formal account of the internal conditions that make effort possible or impossible.
Existing frameworks fall short at the point where intention is expected to convert into execution. Theories of motivation such as Self-Determination Theory (Deci and Ryan) describe the antecedents of effort. They explain how factors like autonomy and competence support sustained action. However, they do not explain what initiates the act itself. Implementation intention models (Gollwitzer) improve consistency through pre-commitment scaffolds, but their effects depend on already active cognitive systems. They do not model the ignition event directly.

Attentional control frameworks, such as the Posner and Petersen network model define how attention is stabilized or redirected. They rely on executive modulation to explain focus. But attention itself must first be engaged. These theories do not explain why that engagement sometimes never begins, even in high-capacity individuals.

Behavioral economics uses constructs like temporal discounting to explain irrational delay. Intention-behavior gaps are statistically observable. However, the internal mechanics producing those gaps are not modeled. These explanations correlate external variables with performance drop-off, but they do not provide a causal structure for effort failure.

In each case, the models are based on behavior or preconditions. They do not define a structural system inside the mind that governs effort as a mechanical sequence. The absence is consistent. Across models, effort is treated either as reactive to context or emergent from high-level goals. It is not modeled as a structured process.

This paper proposes that effort is not reactive and not emergent. It is structural. It arises from the alignment of six internal variables that act within a cognitive system. These variables are not traits or states. They are structural functions. When aligned, they produce Drive. When misaligned, they produce effort failure, regardless of desire, knowledge, or commitment.

This model is called Lagunian Dynamics, a first-principles theory of Drive. It introduces a computable internal architecture composed of six variables: Primode, CAP, Flexion, Anchory, Grain, and Slip. Each variable operates in one of three domains: Ignition, Tension, or Flux. These domains define the phases of engagement: starting, sustaining, and varying effort over time.

Under this model, failure to act is reclassified. It is not lack of will. It is structural misalignment in the Drive system. Procrastination becomes a configuration error in the Ignition domain. Burnout emerges from tension overload in Flexion or Grain. Inconsistency reflects instability in the Flux domain. These are not abstract states. They are structural patterns.

The remainder of this paper defines the architecture of Drive, formulates its core equation, and derives theoretical laws from its structure. Each failure mode is mapped to its internal configuration. Measurement pathways and empirical proxies are introduced. The field is defined as Cognitive Drive Architecture, a field within cognitive science focused on the structure of effort, not its outcomes.

This model does not explain what effort looks like. It defines what must exist in the system before effort can occur.

## 2. Literature Integration: Positioning Lagunian Dynamics

Cognitive Drive Architecture (CDA) is proposed as a new structural field within cognitive psychology. It focuses on the internal system configuration that enables or disables cognitive effort. CDA does not operate at the level of behavior, emotion, or outcome but instead models the mechanics of Drive formation. Its core theory, Lagunian Dynamics, introduces a variable-driven architecture that models engagement, persistence, and disruption using structural equations and system variables. This section situates CDA within the current psychological landscape by identifying what existing models explain well and clarifying where structural gaps remain. It shows how CDA complements and extends existing perspectives without displacing them.

### 2.1. Motivation Science

Motivation theory addresses why individuals act. Self-Determination Theory (Deci & Ryan, 1985; Ryan & Deci, 2000) identifies psychological needs such as autonomy and competence as drivers of intrinsic motivation. Expectancy-Value Theory (Eccles & Wigfield, 2002) models task value and success expectancy as key inputs into decision behavior. Temporal Motivation Theory (Steel & König, 2006) integrates discounting, delay, and impulsivity into a utility function that governs action timing.
These models effectively predict motivational state, but they do not resolve the question of structural entry into action. High motivation often fails to convert into immediate task initiation. That failure is not well-modeled by scalar theories of value or urgency.

CDA introduces CAP (Cognitive Activation Potential) as a structural amplifier that modulates the expression of ignition (Primode). CAP is influenced by emotional intensity, urgency, and goal coherence, but its effect is exponential, not linear. It does not determine whether someone wants to act. It determines how much internal force that desire generates at the moment of potential entry. Motivation, in CDA, is not sufficient. It must structurally configure with other variables to initiate action.

## 2.2. Action Initiation Research

Intentional action has been examined through the lens of goal-setting, volition, and behavioral consistency. Gollwitzer (1999) showed that implementation intentions increase follow-through by linking tasks to cues. Ajzen (1991) outlined the Theory of Planned Behavior, where intention strength is a major predictor of execution. Baumeister and Heatherton (1996) addressed failures of self-regulation in contexts of high goal conflict.

These models locate the preconditions for action, but not the internal system transition that makes execution possible. In practice, clear intention often coexists with complete behavioral inertia. This remains unresolved by models that operate on probabilistic or contextual frameworks.

CDA models this transition structurally through the variable Primode. Primode governs ignition — the point at which attention and intention align to activate entry. Without Primode, intention remains inert, regardless of its strength. CDA treats the intention-action gap as a system alignment failure rather than a cognitive misjudgment. CAP and Primode interact within the Ignition domain to determine whether the system activates. This reframing allows intention-based models to be situated inside a structural system rather than treated as surface predictors.

## 2.3. Cognitive Control and Executive Function

Executive function encompasses task switching, working memory, and inhibitory control (Miyake et al., 2000; Diamond, 2013). These cognitive processes are central to performance in dynamic contexts. Braver (2012) proposed dual mechanisms of control, distinguishing between proactive and reactive cognitive regulation based on timing and anticipation.

These models describe how control functions operate once engaged. They assume that the control system is active and resourced. What is left unmodeled is the internal condition that makes executive function available and sustainable over time.

CDA places cognitive control within the Tension domain, governed by Flexion, Anchory, and Grain. Flexion refers to task elasticity — how well a task conforms to the mind's current state. Anchory models sustained focus as a tethering force, while Grain captures emotional or cognitive friction that weakens this tether. CDA does not treat executive capacity as static. It explains why control functions may be present yet still fail due to structural resistance or misalignment. It also accounts for within-task collapse, where performance erodes despite the presence of working memory or inhibitory ability.

## 2.4. Emotional Modulation

Emotion has been shown to influence attention, memory, and task appraisal. The affect-as-information hypothesis (Schwarz & Clore, 1983) models affect as a judgment input. The broaden-and-build theory (Fredrickson, 2001) shows that positive affect increases cognitive flexibility and exploration. Gross (1998) outlined mechanisms of emotional regulation that affect long-term cognitive functioning.

These models treat emotion as an environmental or contextual influence on cognition. They rarely specify how emotional energy becomes integrated into task performance at the system level. Most do not address why strong emotion sometimes initiates action and other times leads to overload or collapse.

CDA integrates emotion structurally. CAP is not just emotional activation. It is a voltage function that modulates how much energy is available to ignite Primode. High emotional energy does not guarantee action. It must be structurally integrated. Grain models negative emotional drag as internal resistance. Rather than treating affect as an interference
variable, CDA places it inside the system's core equations. Emotion becomes both a multiplier (through CAP) and a destabilizer (through Grain), depending on its configuration and timing.

## 2.5. Performance Variability

Variability in performance is often treated as noise. Attention fluctuates, emotional states shift, and outcomes change under similar conditions. Dual-process models (Evans & Stanovich, 2013) distinguish between automatic and reflective processing, but do not describe variability at the intra-individual level. Adaptive control models (Botvinick et al., 2001) focus on conflict monitoring, but still assume a relatively stable control system underneath.

CDA makes variability structural. Slip is a core variable in the Flux domain, not an error term. It represents endogenous variance, influenced by subconscious context, affective interference, and entropy. Slip is what explains why a prepared, focused, well-resourced individual can still experience sudden disruption. The model predicts this not as failure, but as system entropy — embedded fluctuation in performance due to internal instability. Variability becomes legible, not random.

## 2.6. Mapping Lagunian Variables to Established Constructs

<table><caption>**Table 1** Mapping of CDA system variables to related constructs in cognitive science, highlighting structural differentiations that distinguish each variable from its conventional psychological analog.</caption><thead><tr><td>CDA Variable</td><td>Related Constructs</td><td>Differentiation</td></tr></thead><tbody><tr><td>Primode</td><td>Intention formation, implementation intention (Ajzen, Gollwitzer)</td><td>Structural ignition threshold, not probability</td></tr><tr><td>CAP</td><td>Motivation, urgency, arousal (Ryan & Deci, Gross)</td><td>Exponential voltage function, not scalar input</td></tr><tr><td>Flexion</td><td>Cognitive flexibility (Miyake, Braver)</td><td>Task elasticity under internal state, not switching alone</td></tr><tr><td>Anchory</td><td>Sustained attention, attentional control (Posner)</td><td>Structural tether, not executive resource</td></tr><tr><td>Grain</td><td>Cognitive load, affective friction (Kahneman)</td><td>Internal friction, not demand-resource ratio</td></tr><tr><td>Slip</td><td>Variability, affective disruption (Botvinick, Evans)</td><td>Structured entropy, not random fluctuation</td></tr></tbody></table>

Cognitive Drive Architecture extends the field of cognitive psychology by introducing a system-level theory of effort. It defines effort not as motivation, not as control, not as emotional regulation, but as a product of six structurally interacting variables. These variables operate across three domains — Ignition, Tension, and Flux — and together produce or inhibit Drive.

Existing models explain behavior, intention, focus, and emotion. CDA does not contradict them. It provides the architectural conditions under which those systems either engage or fail. The field introduces a new unit of analysis: the internal configuration of Drive as a system. Lagunian Dynamics serves as its originating theory, defining both the variables and the core equation that governs their interaction.

CDA is not a subtheory within motivation, attention, or affect. It is a structural host field, designed to explain how these systems must align internally for action to occur. It offers psychology a formal architecture for effort. One that can be modeled, tested, and eventually instrumented.

## 3. Theoretical Framework: First-Principles Postulates

Cognitive Drive Architecture (CDA) is grounded in first-principles modeling. It defines Drive not as a scalar resource or trait but as a configuration of interacting structural variables. Each variable functions mechanistically, operates in real-time, and can shift dynamically under task and system load. The following postulates establish the foundational components of the Drive system. Each is independently falsifiable and derived from observed cognitive behavior.
**3.1. Postulate 1: Effort Requires Ignition**

**3.1.1. Variable: Primode**

Functional Definition

Primode represents the ignition point of Drive. It is the structural threshold at which cognitive intent and attentional focus align to initiate task engagement. Without Primode activation, action does not begin, regardless of motivation or external prompting. It is not volitional. It is a system entry gate.

Scientific Analogues

Comparable constructs include intention-behavior coupling (Gollwitzer, 1999), volitional readiness (Libet et al., 1983), and executive gatekeeping in working memory (Chatham & Badre, 2015). Unlike these, Primode does not model content or control. It models the binary switch that transitions passive cognitive readiness into active engagement.

Falsifiability

If initiation latency were uncorrelated with any observable threshold signal (neural, behavioral, or affective) across tasks of equal value and clarity, the existence of a Primode function would be called into question. Further, if engagement occurred without any structural change detectable in system configuration, the postulate would not hold.

**3.2. Postulate 2: Motivational Voltage Modulates Ignition**

**3.2.1. Variable: CAP (Cognitive Activation Potential)**

Functional Definition

CAP defines the emotional-volitional energy available at the moment of task engagement. It does not determine whether a task is valued. It determines how powerfully that value is amplified in the ignition process. CAP acts as a nonlinear modulator, shaping the intensity of system activation once Primode is triggered.

Scientific Analogues

CAP aligns conceptually with constructs such as motivational intensity theory (Brehm & Self, 1989), perceived task importance (Silvia, 2006), and emotional arousal in task initiation (Kuhl, 1985). Unlike scalar models, CAP operates structurally as an exponential multiplier of Primode.

Falsifiability

If motivational amplitude showed only linear effects on task engagement across affective conditions, CAP's proposed exponential function would be unsupported. If higher urgency consistently failed to alter entry latency or early momentum once Primode was active, CAP would lack predictive utility.

**3.3. Postulate 3: Tasks Must Fit Mental Structure to Sustain Engagement**

**3.3.1. Variable: Flexion**

Functional Definition

Flexion models the system's ability to cognitively adapt the task into an internally workable form. It is not task difficulty. It is task elasticity — the perceived fit between task form and current cognitive structure. When Flexion is high, tasks feel shapeable. When low, effort resists alignment.

Scientific Analogues

Related constructs include cognitive fluency (Alter & Oppenheimer, 2009), mental model alignment (Johnson-Laird, 1983), and perceived cognitive affordances (Norman, 1988). Flexion differs by embedding adaptability as a structural tension between system readiness and task geometry.
Falsifiability

If subjective task fit and ease of entry were uncorrelated with performance continuation or dropout rate, Flexion would lack structural validity. If users reported equal adaptability but showed divergent effort collapse under identical task load, Flexion as a differentiating variable would be unsupported.

**3.4. Postulate 4: Attention Is Tethered Against Resistance**

**3.4.1. Variables: Anchory and Grain**

Functional Definition

Anchory stabilizes attention by tethering it to the task environment. Grain introduces internal resistance that disrupts this tether. The balance of these two variables determines whether sustained focus holds or fractures. Attention is not a default. It is a dynamic result of equilibrium between stabilizing and destabilizing forces.

Scientific Analogues

Anchory is conceptually aligned with sustained attention constructs (Posner & Petersen, 1990; Kane & Engle, 2003). Grain maps to constructs like cognitive load (Sweller, 1988) and task-based affective resistance (Inzlicht et al., 2015). Unlike independent constructs, CDA treats Anchory and Grain as system-coupled, operating in structural tension.

Falsifiability

If increases in task-based friction do not produce proportional focus degradation when Anchory is held constant, or if attention collapse occurs under high Anchory with no rise in Grain or related resistance signals, the postulate would not hold. Anchory must show tethering stability predictive of sustained attention across neutral and loaded contexts.

**3.5. Postulate 5: All Cognitive Systems Contain Structured Variance**

**3.5.1. Variable: Slip**

Functional Definition

Slip models performance variability not as noise, but as a structural component of the system. It reflects stochastic internal variance driven by emotional residue, subconscious interference, or system-level entropy. Slip is not an error. It is a baseline condition of all cognitive performance.

Scientific Analogues

Slip shares properties with constructs like mind-wandering (Smallwood & Schooler, 2015), performance inconsistency (MacDonald et al., 2006), and internal state disruption (Kahneman, 1973). However, it is distinct in being modeled not as interference, but as a variable within the system's core architecture.

Falsifiability

If intra-individual performance inconsistency on cognitively controlled tasks showed no correlation with underlying emotional or contextual variance — even when motivational, attentional, and task-related inputs are held constant — Slip would lack systemic validity. If performance collapsed without variation in internal or contextual state, the structural function of Slip would not be supported.

**3.6. Six-Variable Architecture of CDA**

Cognitive Drive Architecture defines Drive as an emergent outcome of six system variables, each functioning within one of three domains:
<table><caption>**Table 2** Domain-specific organization of the six-variable Cognitive Drive Architecture, outlining the functional grouping of variables across Ignition, Tension, and Flux domains according to their roles in task initiation, sustained engagement, and performance variability.</caption><thead><tr><td>Domain</td><td>Variables</td><td>System Role</td></tr></thead><tbody><tr><td>Ignition</td><td>Primode, CAP</td><td>Determines task initiation and entry force</td></tr><tr><td>Tension</td><td>Flexion, Anchory, Grain</td><td>Regulates sustained engagement and focus</td></tr><tr><td>Flux</td><td>Slip</td><td>Introduces variability and instability</td></tr></tbody></table>

Each variable is measurable in principle and falsifiable through task, timing, or state-based experimentation. Together, they form a complete framework for modeling effort as a structural phenomenon rather than a motivational state.

# 4. Scope and Boundary Conditions

Cognitive Drive Architecture (CDA) is a structural field of effort. It does not claim universal applicability across all psychological phenomena. The framework is designed to model real-time configuration dynamics of Drive within cognitively engaged individuals. To ensure theoretical clarity and practical precision, this section defines CDA's functional envelope, identifies areas outside its model space, and delineates key assumptions underlying its structure.

## 4.1. Applicability Envelope

The CDA field is designed to explain intraindividual fluctuations in cognitive engagement during tasks that require self-initiated effort. The system models how internal variables shape the ignition, maintenance, and variability of Drive. It applies where cognitive effort is internally modulated, behavior is not externally mandated, and action depends on alignment between intent and attention.

### 4.1.1. CDA is directly applicable to the following domains:

* **Academic performance contexts**, including sustained reading, writing, learning, or exam preparation

* **Creative or cognitively complex work**, where ambiguity and volitional engagement fluctuate (e.g., design, programming, writing, problem-solving)

* **Clinical interventions for volitional breakdown**, such as cases involving procrastination, fatigue without clear cause, task avoidance, or perceived effort collapse

* **Human-computer interaction (HCI) and adaptive system design**, particularly in environments responsive to cognitive state (e.g., intelligent interfaces, neuroadaptive tools)

These domains share the following properties:

* effort is self-generated,

* success depends on sustained engagement, and

* performance is subject to internal state shifts.

CDA models these shifts structurally through real-time interaction between system variables.

## 4.2. Boundary Exclusions

CDA is not a model of social compliance, ethical evaluation, or cultural normativity. It does not simulate behavior arising from externally imposed authority, punishment-reward contingencies, or moral constructs. It does not evaluate the desirability of action, only the structure of engagement itself.

Specifically, CDA does not model:

* **Cultural value judgments about action**, such as whether effort is considered virtuous or expected in a given context

* **Ethical or philosophical evaluation** of task legitimacy, moral rightness, or interpersonal impact

* **Externally coerced behavior**, such as obedience under threat, compulsory participation, or manipulation-driven compliance
These domains require normative frameworks. CDA models structural causality only. Whether action is seen as good, fair, moral, or rational lies outside its explanatory range.

Additionally, CDA is not a personality theory. It does not model stable traits such as conscientiousness, openness, or introversion. However, trait-level tendencies may interact with CDA variables, for example by influencing baseline Flexion or average CAP levels. Trait-function interactions are acknowledged but not defined in CDA.

### 4.3. Model-Dependent Assumptions

The CDA field assumes a minimally functional cognitive substrate. Its equations and interactions rely on active attentional regulation, affective awareness, and self-referential cognition. While CDA may offer explanatory value across a wide spectrum of mental states, it was not designed for systems where foundational cognitive processes are severely disrupted.

Conditions that fall outside current model reliability include:

* **Late-stage neurodegenerative disorders**, where executive and attentional systems are no longer reliably functional (e.g., Alzheimer's, severe frontal damage)

* **Active psychotic states or severe trauma**, where internal consistency, attentional tethering, and affect modulation may be disorganized or fragmentary

* **Coercive or externally overdetermined environments**, such as high-surveillance incarceration, military command under duress, or behavioral programming via direct control

In these conditions, the architecture CDA depends on may be unavailable or fundamentally altered. The model is intended for voluntary cognitive systems with intact executive modulation.

### 4.4. Cross-Theory Positioning

CDA does not replace or override existing psychological models. It intersects with them by addressing the structure that underlies intraindividual engagement. The table below situates CDA within related theoretical domains, clarifying what CDA models and what is better explained by other frameworks:

<table><caption>**Table 3** Lagunian Dynamics across Ignition, Tension, and Flux Domains.</caption><thead><tr><td>Domain</td><td>CDA Position</td><td>Handled by Other Frameworks</td></tr></thead><tbody><tr><td>Task effort</td><td>Primary explanatory domain</td><td>--</td></tr><tr><td>Value and motivation</td><td>Emergent factor, not directly modeled</td><td>Self-Determination Theory, Expectancy-Value Theory</td></tr><tr><td>Affective dynamics</td><td>Modeled structurally via CAP and Grain</td><td>Emotion Regulation Theory (Gross), Affective Neuroscience</td></tr><tr><td>Social context</td><td>Indirect influence on system variables</td><td>Social-Cognitive Theory, Role Theory</td></tr><tr><td>Identity formation</td><td>Outside model scope</td><td>Narrative Identity Theory, Psychodynamic Models</td></tr></tbody></table>

CDA does not attempt to model every determinant of human behavior. It defines the internal mechanics through which Drive is activated, modulated, sustained, and disrupted. Its purpose is to render these mechanics visible and structurally testable within systems that are otherwise treated descriptively or post-hoc.
<figure><img src="image_5.png" /><figcaption>**Figure 1** Layered architecture of Cognitive Drive Architecture (CDA), illustrating the distinction between the fully modeled structural Drive engine, interacting dynamic modulators, and external ecological or philosophical contexts that influence behavior but remain outside CDA's simulation scope</figcaption></figure>

## 5. The Drive Equation: Formal Structural Mechanics

At the center of Cognitive Drive Architecture is a formal equation that models Drive not as a trait or intention, but as an emergent property of interacting internal variables. This equation, known as Lagun's Law of Primode and Flexion Dynamics, expresses cognitive engagement as a system output governed by ignition, adaptability, tension, and entropy. It is designed to explain moment-to-moment fluctuations in self-initiated effort based on internal system configuration.

$$Drive = \left( \frac{Primode^{CAP} \times Flexion}{Anchory + Grain} \right) + Slip$$

This formulation models the internal mechanics of effort generation. The numerator reflects the ignition system: Primode, the binary threshold for entry into action, is raised exponentially by CAP (Cognitive Activation Potential), which represents affective-volitional voltage at the moment of task onset. CAP does not simply add energy to the system; it modulates the intensity of Primode's expression, creating a nonlinear relationship between urgency and engagement.

When CAP is high and Primode is present, system ignition intensifies sharply. If Primode is zero, the system fails to ignite regardless of CAP or Flexion. If CAP is zero, Primode remains structurally neutral (Primode⁰ = 1), meaning the system may still activate but without amplification. This behavior reflects known asymmetries in effort mobilization, where high affective salience can accelerate engagement only if the entry condition is already structurally present (Cacioppo et al., 1999; Shenhav et al., 2013; Inzlicht et al., 2014).

Flexion operates as the internal measure of task adaptability. It reflects how well the cognitive structure can conform to the shape, ambiguity, or structure of the task. High Flexion indicates a good fit, where tasks feel mentally graspable and flow is possible. Low Flexion produces misalignment, increasing resistance despite motivation and ignition. This component modulates the system's ability to carry forward momentum after ignition occurs.

The denominator represents structural tension: Anchory tethers attention to the task. Grain introduces resistance, including emotional drag, dissonance, or overload. These forces are structurally opposed. When Anchory outweighs Grain, focus stabilizes. When Grain rises past Anchory's threshold, attention fractures. The model treats this balance not as a product of willpower, but as a tension equation between stabilizing and destabilizing forces.

Outside the primary equation is Slip, a stochastic entropy variable. Slip introduces non-deterministic variability into system behavior. It models internal noise, mood fluctuations, and subconscious interference not explained by the other variables. Slip is not a failure mode. It is a structural condition. All cognitive systems contain instability. CDA makes that instability explicit and trackable, rather than treating it as random error.
## 5.1. The structural roles of each term are as follows

<table><caption>**Table 4** Structural roles of the variables within the CDA Drive Equation, specifying how each component contributes to ignition, adaptability, attentional stability, and system-level variability.</caption><thead><tr><td>Component</td><td>Structural Role</td></tr></thead><tbody><tr><td>Pr i mode<sup>CAP</sup></td><td>Ignition engine modulated by affective voltage</td></tr><tr><td>Flexion</td><td>Adaptability of the task to internal cognitive structure</td></tr><tr><td>Anchory + Grain</td><td>Balance of attentional tethering vs internal resistance</td></tr><tr><td>Slip</td><td>System entropy introducing variability even under optimal conditions</td></tr></tbody></table>

This formulation allows the system to account for a wide range of performance outcomes. For example, when CAP is high but Primode is not triggered, the system remains inert. This often manifests as frustration: energy without ignition. When Primode and CAP are both high but Flexion is low, tasks may begin quickly but stall. A configuration with high Primode, strong CAP, high Flexion, low Grain, and stable Anchory approximates what is traditionally described as a flow state, though the term is not used symbolically here. Conversely, a rise in Grain or a drop in Anchory under load results in fatigue or dropout. Even under structurally optimal conditions, a sudden spike in Slip can produce apparent inconsistency in performance.

The nonlinearity embedded in the exponentiation of CAP over Primode is central to the model's predictive utility. It reflects the finding that urgency or emotional importance does not always create proportional increases in Drive. Instead, there is a threshold effect. Once Primode is available, even modest increases in CAP can rapidly increase system activation. But without that threshold crossing, no amount of emotional intensity produces entry. This is consistent with empirical models showing exponential interactions between salience and control investment (Shenhav et al., 2013; Inzlicht et al., 2014).

<figure><img src="image_6.png" /><figcaption>**Figure 2** Temporal curve of Drive generation and decline across the three CDA domains, illustrating the progression from Primode–CAP ignition, through sustained engagement governed by Flexion and Anchory, to eventual destabilization under rising Grain and Slip-induced entropy.</figcaption></figure>

The equation is also **mathematically necessary**, given the system constraints. Its structure is not arbitrary. It is derived from five internal requirements of the Drive system:

* **Drive must be zero if Primode is zero.** This ensures that ignition is a hard gate. Additive formulations fail this constraint.

* **CAP must amplify Primode, not add to it.** Exponentiation introduces the required nonlinearity. A product or sum fails to reproduce asymmetrical ignition behavior.
* **Flexion must increase Drive multiplicatively**, not autonomously. It has no initiating power alone but shapes the quality of motion after ignition.

* **Anchory and Grain must act as divisive resistors**, not subtractive ones. Division properly models reduced system yield under load. Subtraction could yield negative or unstable results.

* **Slip must introduce post-system variability**, structurally independent of the rest. It does not reduce Drive. It disrupts its regularity.

If any of these constraints are violated, the model produces false behavior. The final form:

$$ Drive = \left( \frac{Primode^{CAP} \times Flexion}{Anchory + Grain} \right) + Slip $$

is not a fitted expression. It is a structural consequence of the model's theoretical assumptions. It is the only form that satisfies ignition-dependence, urgency amplification, adaptability shaping, tension resistance, and entropy drift simultaneously.

Each variable is real-time and state-dependent. Drive is not fixed. It fluctuates based on momentary changes in affect, attention, friction, and system entropy. The equation is not intended to model long-term traits. It models within-task variability in performance under self-initiated effort conditions.

CDA positions this equation not as a metaphor but as a structural mechanism. Each term corresponds to a specific internal function. These functions can be inferred through behavioral proxies, tested through experimental manipulations, and eventually measured directly. The model makes the structural conditions for effort both legible and computable.

# 6. Domain Architecture: Ignition, Tension, and Flux

Cognitive Drive Architecture operates across three structurally distinct but temporally continuous domains. Each domain governs a different phase of effort generation and degradation. These domains are not stages in a linear sequence. They are operational contexts that shift dynamically based on real-time configuration. Each variable in the Drive Equation is localized within one of these three domains. The domains correspond to entry, sustainment, and stability of cognitive effort.

## 6.1. Ignition Domain

### 6.1.1. Variables: Primode, CAP

The Ignition domain governs task entry. It models the structural readiness of the system to initiate action. Primode acts as a binary threshold — ignition either occurs or it does not. CAP (Cognitive Activation Potential) exponentially modulates Primode, acting as a real-time amplifier of emotional urgency, salience, or task-relevance.

The critical dependency here is that Primode must be active for CAP to have any effect. High CAP with zero Primode results in inertial buildup with no output. The system does not enter engagement. Conversely, active Primode with low CAP leads to flat, minimal ignition. Only their combined interaction generates full-scale entry.

Healthy ignition occurs when Primode is triggered by internal intent or contextual cue, and CAP is available to power that activation. Failures in this domain present as task avoidance, stillness, or inertial delay — despite conscious intention. The system architecture remains powered but unengaged, like a computer waiting at a login screen without user input.

## 6.2. Tension Domain

### 6.2.1. Variables: Flexion, Anchory, Grain

Once ignition occurs, the Tension domain becomes dominant. This domain manages sustained engagement, real-time stability, and the adaptive flexibility of task processing. The variables in this domain operate in interactional tension. Flexion governs how well the task conforms to the current mental structure. Anchory maintains the attention tether. Grain introduces cognitive or emotional friction.
These variables interact as a stability triangle. High Flexion with strong Anchory and low Grain produces stable effort. A drop in Anchory or rise in Grain destabilizes focus. Even if ignition was successful, the system loses stability and collapses.

Example: A person begins a writing task (Primode and CAP are active). Flexion is low due to uncertainty in the topic. Anchory is moderate, but Grain increases due to emotional pressure. Result: dropout occurs not because of distraction but from internal destabilization.

The Tension domain reflects what appears externally as discipline, consistency, or flow, but it is governed structurally by real-time resistance balance. Failures here often present as burnout, stalling, or false focus (attention is present but no task adaptation occurs).

## 6.3. Flux Domain

### 6.3.1. Variable: Slip

The Flux domain governs systemic variability. It is not time-bound in the way Ignition or Tension are. Slip is omnipresent and continuous. It reflects stochastic interference, subconscious drift, or emotional noise that affects the consistency of performance across moments.

Unlike other domains, the Flux domain does not control Drive magnitude directly. It injects instability. When Slip is low, Drive follows expected trajectories based on the other variables. When Slip is high, outputs become unpredictable — even under optimal structural alignment.

Slip's role is similar to entropy in signal systems or thermal noise in circuits. It is not a malfunction. It is a native feature of real-time cognitive operation. In low-Slip states, the system exhibits high coherence. In high-Slip states, the system may misfire, waver, or overreact to minor inputs.

Examples include sudden mood shifts, disorientation during otherwise routine tasks, or sharp productivity spikes without external cause. Slip explains these deviations without relying on trait-based explanations or external stimuli.

Flux is the final modulator. It defines how clean or noisy the performance stream will be after ignition and tension have been structurally configured.

## 6.4. Domain Summary

<table><caption>**Table 5** Summary of CDA domain functions, associated variables, and characteristic failure signatures, delineating how structural misalignments within Ignition, Tension, or Flux domains manifest as distinct patterns of effort breakdown.</caption><thead><tr><td>Domain</td><td>Variables</td><td>System Role</td><td>Failure Signature</td></tr></thead><tbody><tr><td>Ignition</td><td>Primode, CAP</td><td>Entry activation</td><td>No start, inert readiness</td></tr><tr><td>Tension</td><td>Flexion, Anchory, Grain</td><td>Task stability and adaptation</td><td>Collapse, stalling, inconsistent attention</td></tr><tr><td>Flux</td><td>Slip</td><td>System variance and performance stability</td><td>Volatility, unpredictability, sharp dropouts</td></tr></tbody></table>

## 7. Failure Configurations: Outcome Classes

Cognitive Drive Architecture redefines performance failure not as weakness or disorder, but as output from structural misalignment in the Drive system. Each failure state corresponds to specific configurations of internal variables across the Ignition, Tension, or Flux domains. The model does not pathologize these outcomes. It interprets them as structural modes — resolvable by identifying and modifying the interacting components of the system.

This section classifies common breakdowns as functionally distinct configurations and shows how each may appear in real-world or clinical settings. The aim is diagnostic clarity: to make failures computable, observable, and structurally attributable.
## 7.1. Outcome Classification Table

**Table 6** Classification of structural failure configurations within CDA, detailing how distinct misalignments of internal variables across the Ignition, Tension, and Flux domains produce recognizable breakdown patterns in cognitive effort.

<table><thead><tr><td>Breakdown</td><td>Domain</td><td>Variable Misalignment</td></tr></thead><tbody><tr><td>Start failure</td><td>Ignition</td><td>Low Primode</td></tr><tr><td>Motivated stall</td><td>Ignition</td><td>High CAP, inactive Primode</td></tr><tr><td>Load collapse</td><td>Tension</td><td>Flexion ↓, Grain ↑</td></tr><tr><td>Anchor fatigue</td><td>Tension</td><td>Anchor ↓, Grain ↑</td></tr><tr><td>False focus</td><td>Tension</td><td>Anchor ↑, Flexion ↓</td></tr><tr><td>Volatility</td><td>Flux</td><td>High Slip</td></tr><tr><td>Inverted ignition</td><td>Ignition</td><td>High CAP, Primode = 0 (energy inversion)</td></tr></tbody></table>

## 7.2. Configuration 1: Start Failure

* **Domain:** Ignition

* **Structure:** Primode is inactive. CAP may be neutral or even moderate, but ignition threshold is not crossed. No engagement begins.

* **Applied Case:** A student understands a task, has no aversion, but remains inert for hours. There is no conscious resistance, only stillness.

* **Clinical Parallel:** Seen in volitional inertia under depressive flattening, though not reducible to mood state.

* **Structural Detection:** Task remains uninitiated across varying motivational inputs. Entry does not respond to urgency escalation unless Primode is externally triggered (e.g., environmental cue).

## 7.3. Configuration 2: Motivated Stall

* **Domain:** Ignition

* **Structure:** High CAP, low or zero Primode. This creates a high-voltage state with no ignition. Subject feels pressure or urgency but cannot act. Energy accumulates but remains uncoupled from activation.

* **Applied Case:** Creative workers reporting tension and urgency but unable to begin, often mistaking this for "procrastination."

* **Clinical Parallel:** May resemble anxious paralysis or cognitive congestion, but differs in that emotional activation is present.

* **Structural Detection:** CAP markers (arousal, salience) are high, but behavioral output is zero. Body posture or cognitive pre-loading may signal energy presence without entry.

## 7.4. Configuration 3: Load Collapse

* **Domain:** Tension

* **Structure:** Flexion drops; Grain rises. Task cannot be internally adapted, and resistance accumulates. Drive initiated but unsustainable.

* **Applied Case:** Complex problem-solving under stress — early engagement followed by sudden withdrawal.

* **Clinical Parallel:** Often mapped to burnout or executive fatigue. In CDA, it results from continuous tension overload.

* **Structural Detection:** Task is engaged initially, followed by rapid performance decay. Subject may show task aversion without emotional aversion.

## 7.5. Configuration 4: Anchor Fatigue

* **Domain:** Tension

* **Structure:** Anchor degrades over time, Grain rises. Sustained attention is not possible. The system drifts or fragments.

* **Applied Case:** Mid-task scrolling, tab-shifting, or environmental distraction after 20–40 minutes of work.

* **Clinical Parallel:** Frequently attributed to attentional disorders. CDA attributes it to tether decay under pressure.
* **Structural Detection:** Focus begins strong but attenuates. Effort markers drop predictably. Grain elevation may be emotional or cognitive in form.

## 7.6. Configuration 5: False Focus

* **Domain:** Tension

* **Structure:** Anchory is stable, but Flexion is low. Attention is sustained on an unadaptable task structure. No progress occurs.

* **Applied Case:** "Busy work" syndrome — sustained cognitive presence with no real movement.

* **Clinical Parallel:** Often mistaken for flow or diligence. Structurally, it is stalled adaptation masked by stable focus.

* **Structural Detection:** Subject reports concentration but no advancement. Engagement appears active but has no productivity yield.

## 7.7. Configuration 6: Volatility

* **Domain:** Flux

* **Structure:** High Slip, regardless of other variables. System instability overrides otherwise functional configurations.

* **Applied Case:** Irregular productivity spikes, emotional swings in performance. Start-stop cycles unrelated to motivation.

* **Clinical Parallel:** Sometimes misclassified as bipolar symptomatology. CDA isolates this as entropy overflow.

* **Structural Detection:** No correlation between effort conditions and performance. High moment-to-moment deviation under identical tasks.

## 7.8. Configuration 7: Inverted Ignition

* **Domain:** Ignition

* **Structure:** CAP is extremely high, but Primode remains at zero. System experiences overload. Energy is present but inaccessible. Often results in task aversion, avoidance behavior, or mental shutdown.

* **Applied Case:** High-stakes tasks where performance matters deeply, but action feels blocked.

* **Clinical Parallel:** Mistaken for perfectionism or fear of failure. Structurally, it is an overflow circuit condition.

* **Structural Detection:** Physiological arousal is high. Task salience is acknowledged. No forward movement occurs. Often accompanied by task displacement behavior.

## 7.9. Structural Detectability Across Configurations

Every failure state within CDA produces detectable system behaviors distinct from trait-level or motivational attributions. These include:

* **Time-pattern deviations** (initiation latency, collapse speed)

* **Behavioral signal dissociation** (e.g., urgency without action)

* **Resistance-pattern asymmetries** (e.g., increasing Grain without matching aversion)

* **Environmental reactivity loss** (task changes produce no reconfiguration)

CDA does not interpret these as deficiencies. It interprets them as configuration states. Just as CPU underload, overheating, or throttling are states within a functioning machine, Drive system failures are internal reconfigurations that can be read, adjusted, and recovered — without labeling the agent as impaired.

# 8. Operationalization of Variables: Mapping to Measurement

A theoretical system must ultimately map to observable, measurable structures. The Cognitive Drive Architecture (CDA) field specifies six structural variables that govern intraindividual effort variability. While each variable has conceptual ties to existing psychometric or behavioral proxies, a unified system for measuring them dynamically in applied settings does not yet exist. The following section outlines current proxies, identifies instrumentation gaps, and introduces a roadmap, VIRELICH, for developing a composite measurement system aligned with CDA.
## 8.1. Structural Mapping of CDA Variables

**Table 7** Structural mapping of CDA variables to observable proxy behaviors and existing measurement tools, illustrating how each theoretical component can be approximated using behavioral, psychometric, or physiological metrics.

<table><thead><tr><td>Variable</td><td>Proxy Behavior</td><td>Tool / Metric</td></tr></thead><tbody><tr><td>Primode</td><td>Time-to-start</td><td>Initiation latency (e.g., behavioral delay)</td></tr><tr><td>CAP</td><td>Emotional voltage</td><td>PANAS, urgency indices, arousal self-ratings</td></tr><tr><td>Flexion</td><td>Task fluidity</td><td>Flow State Scale, dynamic task-fit ratings</td></tr><tr><td>Anchory</td><td>Focus duration</td><td>Continuous Performance Test (CPT), eye-tracking fixation time</td></tr><tr><td>Grain</td><td>Perceived resistance</td><td>NASA-TLX, friction-based Likert items</td></tr><tr><td>Slip</td><td>Performance variability</td><td>RT variance, intraindividual SD (e.g., reaction time spread)</td></tr></tbody></table>

These proxies provide preliminary anchoring points for measurement. Most derive from well-validated instruments or established behavioral protocols. However, these are typically applied in isolation, not integrated into a single dynamic model. Furthermore, they operate on coarse time scales, limiting their use for moment-by-moment Drive diagnostics.

## 8.2. The Measurement Gap

Despite strong theoretical decomposition, there is currently no applied instrumentation system that captures the real-time interaction of all six CDA variables. Existing tools (e.g., CPT, NASA-TLX, PANAS) assess individual constructs intermittently, often retrospectively, and typically outside of high-resolution interaction contexts (e.g., live task environments, adaptive systems). This gap makes it difficult to test the full model empirically or apply it in real-world cognitive interfaces.

To address this, we propose a directional instrumentation framework — not as a product, but as a structurally derived research hypothesis.

## 8.3. Introducing VIRELICH: A Modular Instrumentation Framework

VIRELICH (Variable-Integrated Real-Time Latent Inference for Cognitive Heuristics) is a proposed scaffolding for a unified Drive-measurement system. It is designed to align with CDA's six-variable model using multimodal, real-time signals across behavioral, physiological, and self-report channels. VIRELICH is not a validated tool, but a proposed instrumentation architecture grounded in the formal logic of Lagunian Dynamics.

## 8.4. VIRELICH Measurement Matrix

**Table 8** VIRELICH measurement matrix outlining proposed signal types and modalities for empirically capturing CDA variables through multimodal behavioral, physiological, and affective indicators in real-time settings.

<table><thead><tr><td>Variable</td><td>Proposed Signal Type</td><td>Measurement Modality</td></tr></thead><tbody><tr><td>Primode</td><td>Task-initiation readiness</td><td>Eye-tracking latency, EEG readiness potential (RP), EMG pre-load</td></tr><tr><td>CAP</td><td>Emotional activation voltage</td><td>Skin conductance, HRV variation, facial microexpression AI</td></tr><tr><td>Flexion</td><td>Perceived task adaptability</td><td>Cursor smoothness, real-time fluency ratings, typing entropy</td></tr><tr><td>Anchory</td><td>Focus tethering stability</td><td>Gaze fixation length, blink rate, secondary task interference</td></tr><tr><td>Grain</td><td>Load-induced resistance</td><td>Self-reported friction bursts, HRV under pressure, facial tension micro-EMG</td></tr><tr><td>Slip</td><td>Stochastic performance drift</td><td>RT deviation, pupillometry fluctuation, cursor/mouse trajectory noise</td></tr></tbody></table>

Each measurement modality corresponds to a structural function within the CDA system. For example:
* **Primode** as a binary ignition threshold may correlate with **EEG readiness potentials** or **motor priming latency** — techniques already used in action-initiation research (Libet et al., 1983; Schurger et al., 2012).

* **CAP** maps to **autonomic markers of emotional salience** such as skin conductance reactivity (Dawson et al., 2007) and heart rate variability, frequently used in affective workload research.

* **Slip**, as a measure of internal system entropy, can be indirectly observed through **pupillary variance** (Laeng et al., 2012) and **motor inconsistency** patterns (e.g., keystroke deviation).

These modalities are drawn from existing neurocognitive instrumentation, including but not limited to:

* **NASA-TLX**: Subjective workload (Hart & Staveland, 1988)

* **EEG readiness potentials (RP)**: Neural indicators of motor preparation

* **HRV (heart rate variability)**: Proxy for affective-cognitive regulation cost

* **Facial EMG**: Subtle affective state expression under cognitive load

* **Typing dynamics**: Increasingly used in early detection of neuromotor disruption (Giancardo et al., 2016)

These analog systems validate the feasibility of decomposing Drive into physiological and behavioral signal domains. VIRELICH does not attempt to invent a new measurement ontology. It integrates existing modalities into a system-consistent structure.

## 8.5. VIRELICH as Instrumentation Hypothesis

It is essential to emphasize: VIRELICH is not a product. It is not presented here as an experimental system. It is a scientific hypothesis for how the CDA field could be empirically operationalized in adaptive environments, clinical diagnostics, or cognitive optimization systems.

"We do not claim VIRELICH as a validated tool, only as a logically emergent instrumentation roadmap derived from a structurally decomposed theory."

### 8.5.1. As such, the framework requires future work in the following areas

* **Signal synchronization**: Multimodal data must be time-aligned for system modeling

* **Construct validation**: Each proxy must correlate consistently with its variable function

* **Real-time interpretability**: Outputs must be computationally tractable at sub-minute resolution

## 8.6. Closing Position: Theory-Instrumentation Symmetry

If CDA is to advance beyond theoretical modeling, it must support both deductive formalism and empirical instrumentation. The Drive Equation is analytically complete, but structural models require observable grounding. VIRELICH serves as the instrumentation hypothesis that parallels the CDA field. It allows experimental and applied researchers to test whether system structure, as predicted by CDA, produces the behavioral, emotional, and attentional signatures it claims to govern.

VIRELICH remains in the domain of methodological design logic. Its development, validation, and deployment are future phases of this theoretical architecture.

# 9. Empirical Validation Pathways

The Cognitive Drive Architecture (CDA) and its formalized Drive Equation provide a structurally defined model of effort. To be theoretically valid, the model must also be empirically testable using currently available tools. While long-term instrumentation efforts (e.g., VIRELICH) will support real-time multimodal diagnostics, CDA does not require them to begin testing today.

The following experimental designs outline how core CDA variables and interactions can be tested using existing behavioral, psychometric, and physiological methods. The purpose is not to demonstrate effects per se, but to validate structural predictions made by the Drive Equation and its domain logic.

## 9.1. Experiment 1: Testing Ignition Threshold via Task Initiation Latency

* **Objective**: Validate Primode as a structural ignition variable distinct from motivational intensity.

* **Variables**: Primode (primary), CAP (moderator)
* **Method:**

* Participants complete a series of goal-aligned tasks (e.g., short writing prompts or problem-solving puzzles).

* Before each task, participants are primed with either a neutral cue (low CAP) or a motivationally salient one (high CAP), randomly assigned.

* Time-to-initiation (TTI) is recorded from cue to first task-relevant motor action (e.g., typing onset).

* Subjective motivation is recorded post-task to confirm high CAP presence.

* **Hypothesis:** When Primode fails to activate (operationalized as TTI exceeding pre-defined latency range), task initiation will not occur, even if CAP is high. Conversely, when Primode is active, high CAP will significantly reduce TTI, reflecting exponential modulation. A simple additive model (CAP alone reduces latency) will not explain the interaction.

## 9.2. Experiment 2: Flexion-Grain Interaction and Task Persistence

* **Objective:** Test Tension domain interaction — whether low Flexion and high Grain predict early dropout or perceived overload, even under high motivation.

* **Variables:** Flexion, Grain

* **Method:**

* Participants complete a computer-based logic puzzle with two conditions:

*   **Condition A:** Rules and visual layout closely match user expectations (high Flexion).

*   **Condition B:** Task design is semantically and spatially counterintuitive (low Flexion).

* During each condition, low-level interruptions (e.g., visual noise, irrelevant pop-ups) are introduced to simulate Grain.

* Task duration, number of attempts, and frustration ratings are collected.

* **Hypothesis:** Condition B with Grain stimuli will produce early disengagement or higher perceived load than Condition A, even if task difficulty is matched. Performance drop in low Flexion/high Grain condition validates Tension domain's structural interference model.

## 9.3. Experiment 3: Anchory Degradation Over Time Under Controlled Load

* **Objective:** Isolate Anchory as a structural tether distinct from attention span or willpower.

* **Variables:** Anchory (primary), Grain (secondary)

* **Method:**

* Participants engage in a sustained attention task (e.g., modified Continuous Performance Test) across 30 minutes.

* Load remains consistent, but low-frequency conflict stimuli are added in second half to simulate rising Grain.

* Gaze fixation, reaction time, and performance accuracy are monitored continuously.

* **Hypothesis:** Anchory degradation (measured by increasing fixation drift and declining performance) will correlate with the rising effect of Grain stimuli over time, independent of total task load or participant baseline attention score. This supports CDA's interpretation of attention as a structural tether susceptible to gradual erosion, not just capacity depletion.

## 9.4. Experiment 4: Slip as a Predictor of Performance Volatility in Identical Tasks

* **Objective:** Validate Slip as a structural entropy variable producing intraindividual performance variability not explained by external changes.

* **Variables:** Slip

* **Method:**

* Participants complete identical cognitive tasks in two matched sessions (e.g., math problems or working memory updates), scheduled across two consecutive days with controlled environmental conditions.

* In-session data collected: reaction time variance, pupillometry, motor drift (e.g., cursor path analysis), micro-lag spikes.

* Self-report questionnaires confirm stable motivation and emotional state.

* **Hypothesis:** Significant intraindividual performance variability between sessions (despite controlled external conditions and matched motivation) will correlate with entropy markers (e.g., increased RT variance, unstable cursor pathing). This supports Slip as a system variance function, rather than noise or error.
## 9.5. From Structural Theory to Measurable Effects

Each proposed design tests a different domain of the CDA system using established methods:

**Table 9** Empirical test designs aligned with CDA domains, specifying primary variables, methodological approaches, and associated domains to operationalize structural effects of Drive through measurable behavioral and physiological outcomes.

<table><thead><tr><td>Design</td><td>Primary Variable(s)</td><td>Test Type</td><td>Domain</td></tr></thead><tbody><tr><td>Ignition Threshold Latency</td><td>Primode, CAP</td><td>Behavioral latency</td><td>Ignition</td></tr><tr><td>Load Collapse Under Grain</td><td>Flexion, Grain</td><td>Behavioral + perceptual</td><td>Tension</td></tr><tr><td>Anchory Under Time Pressure</td><td>Anchory, Grain</td><td>Eye-tracking, sustained task</td><td>Tension</td></tr><tr><td>Intraindividual Drift</td><td>Slip</td><td>RT variance, motor instability</td><td>Flux</td></tr></tbody></table>

Each experiment is feasible with current cognitive science infrastructure — no novel equipment is required. Structural effects are observed through interaction terms, nonlinearity, or intraindividual dissociation from surface-level variables (e.g., motivation, task complexity).

These designs do not exhaust CDA's empirical potential. They serve as proof-of-testability for a theory claiming structural architecture rather than descriptive abstraction. CDA does not attempt to replace psychological models; it provides a substrate that can now be measured, modeled, and falsified using conservative, domain-aligned methods.

## 10. Implications and Positioning

Cognitive Drive Architecture (CDA) introduces a structurally decomposed field of intraindividual effort. It does not replace existing frameworks in motivation, executive control, or behavioral economics. Instead, it provides an underlying mechanistic substrate for explaining how Drive emerges, stabilizes, and destabilizes over time.

CDA is not a motivational framework. It is not a behavioral toolkit. It is a first-principles architecture that treats Drive as a computable system output, produced by internal variable alignment rather than surface intention or preference. Where motivational theories describe reasons for action, CDA explains the conditions under which action becomes structurally possible.

### 10.1. Comparison to Motivation Theory

Motivation theories, including Self-Determination Theory (Deci & Ryan, 1985), Expectancy-Value Theory (Eccles & Wigfield, 2002), and Temporal Motivation Theory (Steel & König, 2006), offer well-validated models for why individuals value certain actions and how these values influence intention strength.

CDA does not dispute motivational constructs. Instead, it treats motivation as a precursor signal that modulates variables within its structure — particularly CAP (Cognitive Activation Potential). In CDA, high motivation can exist without action if Primode remains inactive. The model accounts for empirical observations where intention and desire fail to produce behavior, not due to lack of motivation, but due to structural misalignment.

Motivation theories model value. CDA models ignition.

### 10.2. Comparison to Executive Function Theories

Executive function theories (Miyake et al., 2000; Diamond, 2013) focus on working memory, cognitive flexibility, and inhibitory control. These are critical for sustained cognitive performance. However, most executive models assume that the cognitive system is already engaged. They do not specify what initiates executive activation, nor why it fails to sustain under load, even when capacity is intact.

CDA embeds executive dynamics within its Tension domain, where Anchory, Flexion, and Grain interact to produce stability or collapse. It offers structural explanations for real-time failures of task persistence, even in high-capacity individuals. Rather than treating attention loss or inconsistency as depletion or pathology, CDA frames them as outcomes of measurable imbalance between stabilizing and resisting forces.
Executive theories model function. CDA models engagement thresholds and performance friction.

## 10.3. Comparison to Self-Regulation and Behavioral Economics

Self-regulation frameworks (e.g., Baumeister & Heatherton, 1996) describe how goals are translated into action through willpower, discipline, and internal monitoring. Behavioral economics adds a probabilistic and incentive-based layer, explaining deviations from rational action through constructs like temporal discounting, decision fatigue, or bounded rationality (Kahneman & Tversky, 1979; Ariely, 2008).

CDA diverges here in epistemology. These models are descriptive — they account for observed variance using behavioral patterns, often with no reference to internal architecture. CDA is structural — it predicts breakdown based on misalignment in measurable system variables. For example, procrastination under CDA is not a preference distortion but an ignition failure (low Primode, high CAP) or a friction overload (high Grain, low Flexion).

These frameworks model choice outcomes. CDA models system configuration that makes certain choices possible or impossible at a given moment.

## 10.4. Application Domains

Because CDA is mechanistic, not normative or trait-based, it can be applied across domains where effort variability matters — especially in systems requiring real-time Drive modeling or task-dependent engagement prediction.

### 10.4.1. Education

CDA can explain why some learners fail to initiate or sustain work, even when interest and ability are present. Unlike motivational interventions (e.g., goal-setting), CDA would allow educators to detect structural failure modes (e.g., low Primode or high Grain) and intervene accordingly. Applications include adaptive learning platforms, tutoring systems, and diagnostic task modeling.

### 10.4.2. Therapy

In therapeutic contexts, CDA offers a reclassification of volitional breakdown. It allows practitioners to distinguish between emotional inhibition, structural ignition failure, and entropy-based disruption (Slip). This may support new approaches to behavioral activation, executive function remediation, or effort-related disorders — though clinical applications remain future-facing.

### 10.4.3. Human-Computer Interaction (HCI)

CDA is directly relevant to interface design. If real-time approximations of CAP, Anchory, or Slip can be modeled (e.g., via eye-tracking or input rhythm), systems could dynamically adjust load, scaffold engagement, or surface interventions before effort collapse occurs. This supports intelligent tutoring systems, user modeling, and neuroadaptive interface development.

### 10.4.4. AI Modeling of Human Behavior

CDA offers a structured model for encoding human Drive fluctuations within artificial agents. Where reinforcement learning focuses on external rewards and planning, CDA-style modeling could simulate structural constraints on activation, allowing agents to more realistically model human-like engagement curves, hesitations, or inconsistencies.

## 10.5. Clarifying the Distinction

**Table 10** Conceptual distinction between CDA and adjacent psychological frameworks, specifying the modeling focus of each field and describing CDA's structural interaction or integration point within their explanatory scope.

<table><thead><tr><td>Field</td><td>What It Models</td><td>How CDA Interacts</td></tr></thead><tbody><tr><td>Motivation theory</td><td>Value, need satisfaction</td><td>Inputs CAP; may amplify ignition</td></tr><tr><td>Executive function</td><td>Working memory, inhibition, flexibility</td><td>Operates within Tension domain (Flexion, Anchory)</td></tr><tr><td>Self-regulation</td><td>Goal translation, delay of gratification</td><td>Observes outcome of structural alignment</td></tr></tbody></table>
<table><tr><td>Behavioral economics</td><td>Decision behavior under constraints</td><td>Describes output from structurally defined state</td></tr></table>

CDA does not compete with these frameworks. It functions as a structural host field—providing the internal mechanics through which the effects modeled by other theories become expressible or blocked in behavior.

## 10.6. Field Positioning

Cognitive Drive Architecture (CDA) is proposed as an independent and novel field within cognitive psychology. It is not defined by content domains such as memory, attention, or emotion, but by configuration — the structural alignment required for effort to emerge and sustain. Its focus is on real-time system dynamics, not symbolic representation, procedural memory, or modular control loops.

Some may ask whether CDA belongs to the tradition of cognitive architecture, referencing systems like ACT-R (Anderson et al., 2004), SOAR (Laird et al., 1987), or EPIC (Kieras & Meyer, 1997). The answer is no — while these frameworks and CDA are structurally adjacent in that they both model internal processes, they are not members of the same lineage.

Cognitive architectures aim to simulate general cognition computationally. They model how knowledge is encoded, retrieved, and manipulated through rule-based or hybrid systems. Their structure is representational and procedural. The objective is often functional simulation of cognition across domains, typically for implementation in artificial agents or adaptive models of reasoning.

CDA is not concerned with simulation or symbolic modeling. It does not encode rules, memory schemas, or decision trees. It does not assume perceptual pipelines or storage mechanisms. It models something fundamentally different: the internal structural conditions that determine whether engagement can occur at all. Where cognitive architectures model how minds execute tasks once activated, CDA models whether activation can occur, based on the real-time alignment of system variables.

## 10.7. In essence:

### 10.7.1. Cognitive architectures model what the mind does once active.

CDA models whether the mind can engage in the first place.

They are cousins, not extensions. Both are structural in nature, but they define structure differently. Cognitive architectures focus on symbolic-procedural structure; CDA focuses on affective-volitional and dynamic structure. In principle, CDA could be embedded within a cognitive architecture (for example, as a Drive-activation submodule), but it does not share the goals, assumptions, or modeling language of that tradition.

Therefore, CDA does not belong within computational cognitive architecture. It stands on its own theoretical foundation as a structural layer within cognitive psychology, focused solely on the mechanics of Drive. It is not a motivational framework. It is not a behavioral toolkit. It is a first-principles system that models what must align internally for cognitive effort to begin, persist, or fail — and renders that structure mathematically legible and empirically testable.
<figure><img src="image_7.png" /><figcaption>**Figure 3** Disciplinary positioning of Cognitive Drive Architecture (CDA) within cognitive psychology, illustrating its theoretical lineage and internal structure, with domains of Ignition, Tension, and Flux extending its functional decomposition.</figcaption></figure>

This positioning supports CDA as a host field — capable of integrating with, supporting, or extending motivational theory, executive function models, behavioral decision frameworks, and adaptive systems — without being reducible to any of them. It invites new empirical research, diagnostic modeling, and instrumentation pathways rooted in its unique structural assumptions.

# 11. Cross-Cultural and Contextual Reflection

Cognitive Drive Architecture (CDA) is designed as a structural field. It defines Drive as a function of internal system configuration rather than external behavior or motivational content. However, structural systems do not operate in a vacuum. Internal variables such as CAP, Grain, and Flexion are influenced by the meaning systems, norms, and expectations embedded in cultural and situational contexts. To ensure responsible application and theoretical clarity, CDA must explicitly account for this relationship.

Drive is not experienced uniformly across all populations. Cultural, developmental, and situational factors shape how urgency is interpreted, how resistance is normalized, and how engagement thresholds are perceived. CDA does not model these meanings directly — it models the mechanics of engagement once intent exists. But the input structures that feed the system may differ across environments.

Critically, CDA does not encode motivational value systems. It does not evaluate the ethics or desirability of any given task. It does not assume individualistic or collectivist goal structures. It defines only the internal structure of action once the system is configured for engagement. In this way, it avoids importing cultural assumptions about what constitutes valid effort. CDA is structurally agnostic to task purpose — but not blind to how context shapes internal variables.

For example, CAP (Cognitive Activation Potential) is a voltage variable. It reflects emotional-volitional urgency, not the source of that urgency. In collectivist settings, CAP may be activated by interpersonal obligation or group harmony cues, whereas in individualist settings, CAP may rise from personal ambition or autonomy threats. The input differs, but the structural function — modulation of Primode — remains identical.

Similarly, Grain may vary in expression depending on how resistance is framed. In high-discipline systems (e.g., martial arts, military academies), friction may be interpreted as meaningful and expected. Grain in these contexts may be present but reappraised — altering its disruptive potential. In contrast, in convenience-optimized settings, even low-
friction tasks may produce a disproportionately high Grain response due to lower tolerance thresholds. Again, the structural variable exists, but its thresholds may shift across cultural environments.

These observations suggest an important empirical direction: CDA variables may be universally structural but locally parameterized. That is, the shape of the system remains intact, but the input functions and variable sensitivities may require calibration depending on population, context, or cultural background. This is not unlike early psychometric models (e.g., IQ testing), which required statistical norming across demographics to maintain validity.

CDA invites empirical investigation across contexts. Example research questions include:

* Does CAP operate differently in collectivist versus individualist settings when urgency is derived from social obligation rather than personal salience?

* Is Anchory more stable in high-ritual, structured cultural environments?

* How does Slip present in high-monitoring vs. low-monitoring societies?

* Are task-fit expectations (Flexion) more rigid in educational systems emphasizing rote standardization than in systems valuing exploration?

These are not challenges to CDA's validity — they are opportunities for contextual refinement and measurement tuning. CDA provides a core system architecture, but it anticipates the need for parameterization to reflect developmental stage, sociocultural norms, and context-specific task environments.

To represent this formally, culture can be understood as an input-layer modulator to the CDA system. It does not alter the structure of the six variables, but it does influence the preconditions and framing through which those variables operate.

<figure><img src="image_8.png" /><figcaption>**Figure 4** Layered model of Drive generation in CDA, illustrating how contextual and cultural inputs modulate the core structural variables of the system, which in turn produce observable Drive behaviors such as task initiation, persistence, and engagement</figcaption></figure>

CDA is not a culturally specific theory. It is a structural theory that anticipates cultural interaction at the level of variable input, not system architecture. It remains testable, generalizable, and adaptable — provided that empirical work respects the local context through which structure is expressed.
## 12. Minimal Viable Application Layer

Cognitive Drive Architecture (CDA) defines effort as the result of structural alignment among six internal variables. While the full empirical instrumentation of these variables (e.g., via real-time neuroadaptive systems) remains an ongoing research pathway, CDA can already inform applied practice using low-tech, analog, or qualitative methods. Not all use cases require sensors, AI modeling, or lab instrumentation. Many applications only require structural awareness and functional mapping.

This section introduces the Minimal Viable Application (MVA) framework — the simplest level at which CDA can be used to support diagnostic insight, task design, or behavior-guided reflection in real-world environments.

**MVA Definition:** A low-tech or theory-light implementation of CDA principles that enables immediate, structured analysis of Drive failure using only existing tools or observational inputs.

### 12.1. MVA Table: Early-Stage Use Cases

**Table 11** Minimal Viable Application (MVA) use cases for early-stage CDA implementation across domains, highlighting diagnostic strategies and low-tech tools to identify structural misalignments in Drive without requiring advanced instrumentation.

<table><thead><tr><td>Domain</td><td>Use Case</td><td>Minimal Tools</td></tr></thead><tbody><tr><td>Coaching</td><td>Identify whether block is ignition (Primode) or tension (Flexion/Grain)</td><td>Self-report mapping, reflective journaling, session review</td></tr><tr><td>Therapy</td><td>Distinguish motivational dropout from structural inactivation</td><td>Narrative case analysis, CDA overlay of symptom reports</td></tr><tr><td>HCI / UX</td><td>Reduce interface-induced Grain</td><td>Task decomposition, usability heuristics, feedback forms</td></tr><tr><td>Education</td><td>Detect early warning signs of effort collapse</td><td>Timing drift, submission latency, engagement slope</td></tr><tr><td>Team Settings</td><td>Map Dropout to configuration mismatch (e.g., Primode⁰ with CAP↑)</td><td>Retrospective task walkthrough + peer debrief</td></tr></tbody></table>

None of these use cases require biometric input. They rely on pattern recognition, guided reflection, and basic timing or self-report tools. The difference CDA introduces is not in the data source, but in the interpretive model — shifting from behavior description to structural configuration analysis.

### 12.2. No-Tech CDA Profile: A Practical Flow

Practitioners can use a simplified diagnostic flow to apply CDA insights to individual cases — even without instrumentation. The flow involves three steps: identify, map, intervene.

#### 12.2.1. Step 1: Identify the Failure Type

Ask: What kind of effort breakdown is occurring?

* No action starts? → Possible Primode issue.

* Action starts but fades fast? → Tension domain collapse (Flexion, Anchory, Grain).

* Inconsistent performance across days? → Possible Slip-related volatility.

#### 12.2.2. Step 2: Map the Breakdown to CDA Configuration

Use the previously defined Failure Configuration Table to associate behavioral patterns with variable-level disruptions.

Examples

* "I want to do it, I know how, I just don't start." → CAP↑, Primode = 0

* "I start and suddenly feel blocked or confused." → Flexion↓, Grain↑
* "Some days I can do it, other days I can't explain why not." → Slip↑

Mapping can be done qualitatively using structured journaling, session reflections, or facilitated interviews. Even minimal data (e.g., timestamps, performance drop points, self-ratings) provide enough resolution to infer probable system misalignment.

### 12.2.3. Step 3: Intervene Structurally

Once a variable misalignment is suspected, interventions target the corresponding structure — not motivation or traits. Examples:

<table><caption>**Table 12** Structurally targeted intervention strategies in CDA, linking specific variable misalignments to their corresponding system focus and actionable modifications, independent of motivational framing or personality traits.</caption><thead><tr><td>If the issue is...</td><td>Structural Focus</td><td>Intervention Type</td></tr></thead><tbody><tr><td>Low Primode</td><td>Entry threshold</td><td>Use minimal cues, environmental nudges, task priming</td></tr><tr><td>CAP too low</td><td>Affective voltage</td><td>Tie task to consequence, reward, urgency symbol</td></tr><tr><td>Flexion low</td><td>Task-mind mismatch</td><td>Restructure task presentation (visuals, scaffolds)</td></tr><tr><td>Anchory unstable</td><td>Focus tether</td><td>Minimize open tabs, reduce multitasking</td></tr><tr><td>Grain too high</td><td>Internal friction</td><td>Lower ambiguity, reduce evaluative pressure</td></tr><tr><td>Slip high</td><td>System instability</td><td>Track sleep, emotional state, use lower-bandwidth tasks</td></tr></tbody></table>

These interventions are structurally matched, not motivationally imposed. They target system conditions, not effort "willpower."

### 12.3. Applied Example: CDA-Informed Coaching Session

A client in a coaching context reports chronic delays on creative work, despite interest and self-set deadlines.

* Observation: Energy is present, rumination occurs, but task does not begin.

* CDA mapping: CAP↑, Primode = 0 → Ignition failure.

* Intervention: Create low-friction ignition routines (e.g., 2-minute entry timer), external anchoring (e.g., cue-based start), reduce ambiguity at entry point (increase Flexion).

* Result: Session log tracks whether CAP-aligned cues modulate Primode without relying on mood or will.

Even without any technology, the coach applies CDA's logic to reroute structural failure into observable configuration change.

### 12.4. Framing CDA as a Low-Tech Diagnostic Field Approach

CDA can support

* Educators, detecting early warning signs of load collapse via class performance patterns

* Therapists, interpreting inactivity as a misaligned system, not emotional disinterest

* Designers, modeling attention disruption as Grain mismanagement, not user failure

* Students or users, self-mapping their states using a structured reflection template

To support this, CDA can be rendered into a simple worksheet or CDA Action Map — a diagnostic surface that walks individuals through variable-level engagement analysis. This can be deployed digitally or in paper-based formats across coaching, educational, or therapeutic environments.

CDA's complexity lies in its structural model, not in its required infrastructure. Even at the MVA level, it offers a shift in framing: effort is not a matter of discipline or desire — it is a structural condition that can be mapped, tracked, and realigned using currently available tools.
**13. Conclusion**

Cognitive Drive Architecture offers a first-principles theory of effort grounded in internal system structure. It treats Drive not as a feeling, trait, or motivational state, but as the output of real-time variable alignment — a measurable configuration. Across its six variables and three domains, CDA models the structural conditions under which engagement begins, stabilizes, fluctuates, or fails.

This paper presents CDA not as a descriptive model of behavior, but as a formal system for modeling internal mechanics. The Drive Equation expresses these mechanics mathematically. Its validity does not depend on subjective experience or post hoc rationalization. It can be tested through behavioral latency, performance collapse, entropy patterns, and variable interaction — using tools that already exist.

CDA is not a substitute for motivation theory, executive models, or decision frameworks. It is a structural host field — a proposed foundation for understanding effort not as intention, but as a function of engagement possibility. Where a person fails to act, CDA does not ask what they believe or value. It asks what internal structure prevented alignment.

The system is designed for falsifiability. Each variable is observable in principle and modifiable in application. Future work must determine how the model performs across populations, developmental stages, and cultural contexts. The variables may be universal, but their expression and thresholds are not assumed to be.

Longer term, CDA offers a structure for Drive-aware interfaces, cognitive load diagnostics, and adaptive environments. Whether in coaching, therapy, HCI, or neurofeedback, the ability to track structural readiness in real time could enable more accurate, responsive, and ethical support systems.

CDA is not an answer. It is an infrastructure — one that now requires rigorous testing, calibration, and scientific scrutiny.

**Compliance with ethical standards**

*Disclosure of conflict of interest*

No conflict of interest to be disclosed.

*Author Note*

This manuscript was developed independently by the author, Nikesh Lagun, without institutional affiliation or external funding. The work introduces Cognitive Drive Architecture (CDA) and Lagunian Dynamics, a structural field, first-principles theory of Drive intended for empirical testing and scientific refinement.

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# Appendix

## Terminology Rationale and Mapping

The Cognitive Drive Architecture (CDA) introduces a new class of terms that are not interchangeable with traditional psychological constructs. These terms are not stylistic replacements. They are structurally motivated labels that isolate function from description. Each term reflects a mechanical role within a dynamic system of Drive generation — not a felt state, diagnostic category, or externally observed behavior.

## The Core Problem

Existing psychological vocabulary often conflates observed behavior with inferred mechanism. For instance, terms like “attention,” “motivation,” or “volition” describe outcomes or experiences, not the dynamic structures that generate them. In CDA, that ambiguity is eliminated by assigning each variable a distinct functional role within the Drive equation. This demands a parallel terminology — one that separates structure from symptom.

## Systemic Term Mapping

<table><caption>**Table 13** Systemic term mapping for CDA variables, contrasting each construct with its closest psychological antecedents and clarifying the structural distinctions that differentiate CDA’s functional definitions from traditional descriptive terms.</caption><thead><tr><td>CDA Term</td><td>Closest Prior Term(s)</td><td>Why It's Not the Same</td></tr></thead><tbody><tr><td>Primode</td><td>Readiness potential, volition, initiation energy</td><td>Operates as a binary ignition gate; combines emotional and cognitive threshold logic not present in traditional initiation constructs.</td></tr><tr><td>CAP</td><td>Arousal, urgency, motivational salience</td><td>Treated as a nonlinear exponential amplifier of Primode, not an additive intensity signal. Adds dynamic scaling absent in most motivational models.</td></tr><tr><td>Flexion</td><td>Fluency, adaptability, task-fit</td><td>Measured structurally via real-time task-conformance. It is not a subjective experience of flow but a live-fit coefficient.</td></tr><tr><td>Anchory</td><td>Sustained attention, focus duration</td><td>Defined as a stabilizing force — the gravitational pull keeping cognition tethered. Not equivalent to concentration or time-on-task.</td></tr><tr><td>Grain</td><td>Friction, resistance, cognitive load</td><td>Models the emergent interference layer between task and system. It is not static “difficulty” but a compound of emotional and informational friction.</td></tr><tr><td>Slip</td><td>Noise, distraction, entropy</td><td>Defined not by attention lapses, but by stochastic instability in the system. Slip is not failure, but internal drift beyond prediction.</td></tr></tbody></table>

## Structural Heuristics

CDA terminology is modeled on system design logic, not phenomenological description. Think like a control systems engineer:

* Primode is a binary gate with a minimum ignition condition.

* CAP is a voltage-like multiplier on activation — a nonlinear modulator, not fuel.

* Flexion measures the system’s ability to deform internally to match external task structure.

* Anchory is the anchoring coefficient of the system’s feedback loop — maintaining coherence against drift.

* Grain is internal resistance at the interaction interface — not a static load, but emergent texture.

* Slip is the system’s entropy band — the degree of output instability even when all other conditions are optimized.

Each term reflects a mechanical role, not a psychological state. This framing allows CDA to produce variable-level diagnostics, compositional modeling, and ultimately predictive instrumentation.

## Why Not Reuse Existing Terms?
Reusing terms like "motivation," "focus," or "effort" would reintroduce the very ambiguity CDA aims to eliminate. Those terms:

* Vary by context (clinical, educational, workplace)

* Lack real-time decomposability

* Conflate feeling, outcome, and intention

By contrast, CDA terms are functionally bound and mathematically modeled. This supports empirical testing, engineering integration, and adaptive system design. The model cannot tolerate conceptual overlap between structural role and surface description — hence, the new lexicon.

## Appendix A: Glossary of Core Terms

<table><caption>**Table 14** Glossary of core CDA terms with formal definitions, outlining each variable's structural function within the Drive system and emphasizing their role as dynamic coefficients rather than descriptive traits or states.</caption><thead><tr><td>Term</td><td>Definition</td></tr></thead><tbody><tr><td>Primode</td><td>The ignition threshold condition; binary variable indicating whether the system is structurally capable of initiating engagement.</td></tr><tr><td>CAP</td><td>Cognitive Activation Potential; a non-linear affective-volitional voltage that modulates Primode intensity.</td></tr><tr><td>Flexion</td><td>Internal task adaptability coefficient; reflects structural compatibility between cognitive schema and task constraints.</td></tr><tr><td>Anchory</td><td>Attention tethering coefficient; determines stability and resistance to cognitive drift during task performance.</td></tr><tr><td>Grain</td><td>Emergent structural friction; comprises resistance from cognitive dissonance, task misfit, or emotional drag.</td></tr><tr><td>Slip</td><td>System entropy; the background variability or noise disrupting stable Drive output, even under optimal configurations.</td></tr></tbody></table>

## Appendix B: Variable-Measurement Table

<table><caption>**Table 15** Measurement table linking each CDA variable to observable proxy behaviors and associated empirical tools, supporting structural assessment through latency, physiological, perceptual, and performance-based metrics.</caption><thead><tr><td>Variable</td><td>Proxy Behavior</td><td>Measurement Tool / Metric</td></tr></thead><tbody><tr><td>Primode</td><td>Task initiation latency</td><td>Time-to-start, eye-tracking saccade delay, EEG RP</td></tr><tr><td>CAP</td><td>Emotional activation voltage</td><td>Skin conductance, HRV, urgency self-report</td></tr><tr><td>Flexion</td><td>Task adaptability perception</td><td>Real-time fluency rating, cursor entropy, click path tracing</td></tr><tr><td>Anchory</td><td>Focus tethering strength</td><td>Fixation duration, blink rate, secondary task interference</td></tr><tr><td>Grain</td><td>Cognitive resistance load</td><td>NASA-TLX subcomponents, subjective friction log</td></tr><tr><td>Slip</td><td>System instability</td><td>RT variance, typing drift, pupillometry spread</td></tr></tbody></table>

## Appendix C: Domain Diagram — Ignition, Tension, Flux

### Domain Map

* **Ignition Domain:** (Primode, CAP)
  Entry gate for self-initiated action. Defines whether Drive can begin.

* **Tension Domain:** (Flexion, Anchory, Grain)
  Mid-task structural interactions that support or break sustained effort.

* **Flux Domain:** (Slip)
  Post-stabilization variability. Governs unpredictability and entropy within an otherwise configured system.
<figure><img src="image_9.png" /><figcaption>**Figure 5** Flow diagram of the CDA Drive computation, showing how variables in the Ignition domain generate initial Drive force, which is modulated by the Tension domain's stability ratio, and ultimately shaped by Flux domain variability to yield the final Drive output</figcaption></figure>

## Appendix D: Experimental Design Sketches

### Experiment 1: Primode Activation Latency

* Test: Time-to-initiate under neutral vs urgency conditions

* Prediction: CAP alone insufficient without Primode

* Measurement: Initiation delay, affect scale, RP spike

### Experiment 2: Flexion-Grain Interaction

* Test: Puzzle layout fluency vs UI interference

* Prediction: High Grain reduces persistence only when Flexion is low

* Measurement: Task duration, frustration self-rating

### Experiment 3: Anchory Under Load

* Test: Focus degradation over time with increasing distraction

* Prediction: Anchory drop predicts dropout regardless of CAP

* Measurement: Eye tracking, performance accuracy slope

### Experiment 4: Slip-Induced Volatility

* Test: Intraindividual RT variation in stable vs emotionally unstable states

* Prediction: High Slip drives output inconsistency even with high CAP × Primode

* Measurement: RT SD, pupillary variance, entropy score
## Appendix E: CDA Self-Assessment Inventory (Prototype)

Instructions: Rate each item from 1-7 (Strongly Disagree to Strongly Agree)

<table><caption>**Table 16** Prototype items from the CDA Self-Assessment Inventory, mapping each structural variable to a subjective self-report prompt designed to capture intraindividual engagement dynamics across Drive domains.</caption><thead><tr><td>Variable</td><td>Sample Item</td></tr></thead><tbody><tr><td>Primode</td><td>"I feel like I can begin this task without delay."</td></tr><tr><td>CAP</td><td>"This task matters right now — I feel emotionally driven to act."</td></tr><tr><td>Flexion</td><td>"This task makes sense to me structurally — I know how to engage."</td></tr><tr><td>Anchory</td><td>"I can stay focused on this without losing track of what I'm doing."</td></tr><tr><td>Grain</td><td>"It feels like something is resisting me as I try to work."</td></tr><tr><td>Slip</td><td>"Even when I try, my output feels inconsistent or scattered."</td></tr></tbody></table>

Note: Not validated; proposed for future psychometric calibration.

## Appendix F: VIRELICH Variable-Instrumentation Matrix

<table><caption>**Table 17** VIRELICH instrumentation matrix for CDA variables, outlining signal types and corresponding physiological or behavioral modalities proposed for real-time measurement of system dynamics underlying task engagement.</caption><thead><tr><td>CDA Variable</td><td>Signal Type</td><td>Proposed Modality</td></tr></thead><tbody><tr><td>Primode</td><td>Task-initiation readiness</td><td>EEG RP, eye-tracking latency, EMG pre-load</td></tr><tr><td>CAP</td><td>Emotional activation voltage</td><td>GSR, HRV, facial expression micro-AI</td></tr><tr><td>Flexion</td><td>Perceived task adaptation</td><td>Cursor flow entropy, typing variability</td></tr><tr><td>Anchory</td><td>Focus tethering</td><td>Blink rate, fixation map stability</td></tr><tr><td>Grain</td><td>Friction & resistance</td><td>HRV changes under cognitive load, micro-frustration reporting</td></tr><tr><td>Slip</td><td>Entropy & variability</td><td>RT spread, mouse movement deviation, pupillary oscillation</td></tr></tbody></table>

Note: VIRELICH is a proposed instrumentation hypothesis grounded in CDA, not a validated system.