Abstract

AToM proposes that coherence—the capacity of a system to maintain integrated, low-entropy structure across perturbations—is a cross-domain invariant of cognition, trauma, interpersonal regulation, organizational dynamics, and cultural evolution. Trauma is modeled as a collapse in the topology or geometry of coherence fields, and entrainment is identified as the primary class of dynamics that generates coherence across scales. Physics is not used as a template but as a boundary case whose high symmetry constraints clarify AToM’s scope. Drawing from predictive processing (Friston, Clark), information geometry (Amari, Chentsov), dynamical systems (Strogatz), trauma science (Porges, Herman, Schore), attachment theory (Bowlby, Main), and cultural evolution (Boyd, Henrich), AToM outlines a unified structural framework for meaning that is mathematically grounded, empirically testable, and compatible with contemporary computational methods.

Introduction: Meaning as a Dynamical Property

For more than a century, theories of meaning have been anchored to three major traditions: the introspective phenomenology of Husserl, the narrative and culturally mediated frameworks of Bruner, and the symbolic-computational paradigm exemplified by Fodor. Each proposes that meaning resides primarily in content—either the content of conscious experience, the content of stories and cultural symbols, or the content of internal mental representations. A Theory of Meaning (AToM) departs from all three not by rejecting them, but by reframing meaning at a deeper level of description: meaning is best understood as a dynamical systems property, not a static property of symbols or internal states. In AToM, meaning is defined as coherence under constraint. This definition relocates the discussion from the semantics of representations to the stability patterns of systems that generate, predict, regulate, and interpret experience. Instead of asking “What does this symbol stand for?” AToM asks, “What patterns of constraint allow a system to remain coherent as it encounters uncertainty, novelty, or perturbation?” This reframing places meaning squarely within the domain of systems that self-organize under pressure—from neurons to narratives, from embodied agents to entire cultures.

This dynamical reframing aligns AToM with several of the most influential contemporary movements in cognitive science. Predictive processing and the free-energy tradition (Clark, Hohwy) already treat cognition not as passive representation but as continuous, hierarchical prediction. In these models, meaning emerges not from symbolic content but from the organism’s ability to reduce prediction error across multiple time-scales. AToM simply makes explicit what is implicit in those frameworks: the throughline is coherence. Likewise, active inference (Friston) understands organisms as systems that must maintain internal counterfactual coherence to survive. They do not merely perceive; they act to preserve a workable, self-consistent internal model. The system’s survival depends on continuously maintaining coherence under metabolic, environmental, and informational constraints. From the enactivist perspective (Varela, Thompson, Rosch), meaning is not inside the head but enacted through sensorimotor coupling. Here again, AToM provides the structural backbone: the organism enacts meaning by maintaining a coherent world/agent loop as it navigates constraints imposed by embodiment, environment, and cultural niche.

Information geometry offers a formal complement to this picture. Work by Amari, Ay, Crutchfield, and others frames cognition as the traversal of curved information manifolds. Systems seek stable trajectories through high-dimensional spaces defined by constraints—energetic, informational, social, and ecological. The curvature of these manifolds encodes the constraints that shape viable interpretations. In this language, meaning is simply the system’s ability to sustain geodesic coherence despite perturbation. AToM reinterprets these models through a single organizing claim: coherence is not a metaphor but a measurable structural property. Meanwhile, complex-systems approaches to cognition (Kelso; Thelen & Smith) emphasize metastability, phase transitions, and attractor dynamics. Infants learning to walk, groups coordinating behavior, or neural ensembles synchronizing around a task all display the same underlying logic: systems stabilize around patterns that satisfy their constraints, and those patterns are the meaningful structures through which experience becomes interpretable. Meaning, in this view, is neither purely subjective nor purely computational; it is the emergent pattern of a system resolving constraints across scales.

By framing meaning as coherence under constraint, AToM provides a unifying scaffold that explains how micro-level neural processes scale into macro-level cultural structures without invoking homuncular intermediaries. The same principles that govern neural synchronization govern narrative plausibility. A story feels meaningful when its arcs remain coherent under the constraints of character motivation, world-logic, and genre expectations. A culture feels cohesive when its norms, symbols, and institutions maintain coherence amid environmental and historical pressures. Even personal identity—often treated as an introspective or narrative construct—can be recast as a dynamical equilibrium: the most stable attractor a person can maintain under the constraints of trauma history, social commitments, cognitive architecture, and predictive needs. AToM shows that these levels of organization are not metaphorically linked but structurally isomorphic. The brain, the body, the self, and the culture all maintain coherence by navigating constraint landscapes. Meaning is what it feels like from the inside when those coherence conditions hold.

In this sense, AToM does not dissolve traditional theories but integrates them. Husserl’s phenomenology captures the first-person texture of coherence loss or restoration. Bruner’s narrative psychology describes how cultures transmit constraint-patterns that shape what counts as coherent experience. Fodor’s computationalism formalizes how internal models constrain interpretation. But each captures only a slice. AToM provides the generative geometry that holds them together. To say “meaning = coherence under constraint” is to assert that meaning is not the content of thought but the stability of sense-making. Meaning is the system’s success in remaining itself while the world changes. And when coherence fails—through trauma, contradiction, or sociocultural disruption—meaning collapses, sending the system into new attractor states. This is why coherence becomes the throughline linking neural dynamics, behavior, narrative, culture, and collective identity. Meaning is not an abstract philosophical property but the lived signature of dynamical stability across scales.

2. Coherence as a Cross-Domain Invariant

If meaning is coherence under constraint, then coherence itself must be understood as a cross-domain invariant—a structural property that recurs in psychology, neuroscience, culture, and complex organizations. Across scientific traditions, “coherence” already signals a system’s ability to integrate diverse components into functional unity. In predictive coding and variational approaches to mind, coherence appears as the minimization of variational free energy (Friston): a system maintains coherence when its generative model accurately predicts sensory input and keeps prediction error within tolerable bounds. In interpersonal neurobiology, Siegel defines mental health as the integration of differentiated neural processes into stable, flexible wholes; here too, coherence is the signature of well-regulated, non-chaotic functioning. Organizational theory, especially Weick’s work on sensemaking, identifies coherence with tight coupling, reduced entropy, and the organization’s ability to maintain a shared interpretive frame under stress. Meanwhile, cultural cognition traditions—from Geertz’s interpretive anthropology to Henrich’s cultural evolution—frame coherence as the persistence of stabilizing narratives, norms, and shared models that keep a group aligned across time. And in network neuroscience, Sporns and others argue that coherent systems exhibit a balanced modularity-to-integration ratio: components must remain specialized yet tightly coordinated, forming a topology optimized for both stability and adaptive reconfiguration.

AToM brings these diverse perspectives into a single structural construct: coherence is the degree to which a system maintains internally consistent predictions and stable cross-scale coupling. Whether we are describing neurons synchronizing across gamma and theta bands, families regulating emotional states, organizations aligning incentives, or cultures stabilizing meaning through myth and ritual, the same underlying logic applies. A coherent system reduces internal contradiction, balances flexibility with constraint, and ensures that local actions remain compatible with global structure. Crucially, this is not metaphorical alignment across domains but structural isomorphism: every system that must resist entropy while adapting to changing conditions converges on coherence as a core dynamical requirement. It is the invariant that makes sense of everything from trauma responses to institutional collapse.

Mathematically, this structural concept of coherence can be formalized using tools drawn from information geometry, dynamical systems theory, and topological data analysis. Entropy gradients describe how far a system has drifted from low-entropy, high-stability attractor states, capturing the “cost” of incoherence. Fisher information metrics quantify a system’s sensitivity to perturbation and the precision of its predictions—high coherence corresponds to smooth, low-curvature regions of the information manifold. Kullback–Leibler curvature measures how internal models deform when confronted with new data, giving a geometric readout of prediction error and adaptive strain. Topological persistence diagrams provide a shape-based view of coherence, revealing stable homological features that persist across scales and distinguishing signal from noise in complex trajectories. Diffusion embeddings illuminate latent structure by showing how information flows through the system, identifying whether subsystems remain integrated or drift into fragmentation. And manifold reconstruction techniques allow high-dimensional behavioral, neural, or cultural data to be mapped onto lower-dimensional geometric surfaces, making coherence measurable as the smoothness, curvature, or connectivity of the resulting space.

Together, these tools extend AToM beyond philosophy or qualitative synthesis. They turn coherence into a quantifiable, geometry-level property of real systems. This allows AToM to unify disparate fields not by analogy but by giving them a shared analytic vocabulary. Whether one studies neurons, relationships, teams, markets, or cultures, coherence is the structural invariant that determines stability, adaptability, and meaning-making. By centering coherence as a mathematically inspectable construct, AToM positions meaning not as subjective interpretation alone but as a measurable feature of dynamical systems navigating constraint.

3. Trauma as a Collapse in Coherence Geometry

AToM defines trauma not as a story but as a structural transformation in a system’s coherence geometry.

Trauma disrupts:

• prediction stability (Hohwy)
• autonomic regulation (Porges)
• integrative neural pathways (Schore)
• memory consolidation (Brewin)
• relational attunement (Tronick)

AToM models these disruptions using concepts from information geometry and topology:

3.1 Loss of Dimensionality

Trauma constrains functioning to fewer degrees of freedom (analogous to manifold collapse in TDA).

3.2 Increased Curvature

Local information geometry becomes sharply curved (high Fisher curvature), reflecting hypersensitivity and hypervigilance.

3.3 Bottlenecking

Persistent homology reveals narrowed pathways between cognitive/affective regions—analogous to attractor entrapment.

3.4 Hysteresis and Irreversibility

Once collapsed, the coherence geometry resists returning to its prior configuration (dynamical hysteresis).

3.5 Dissociation as Boundary Formation

Dissociation acts as a variable boundary that prevents integration across high-curvature regions.

These structures appear in many nonlinear systems and offer a rigorous mathematical vocabulary without overextending physics analogies.

4. Entrainment as the Primary Mechanism of Coherence Formation

AToM treats trauma not primarily as a narrative event or a psychological category but as a structural transformation in a system’s coherence geometry. Trauma is the point at which the system’s capacity to maintain internally consistent predictions and integrated cross-scale coupling collapses. Across scientific literatures, this collapse is already visible, though described in discipline-specific terms. Predictive processing accounts identify trauma as a failure of prediction stability (Hohwy): the generative model becomes unable to regulate prediction error, and the system enters a state of chronic volatility. Polyvagal theory (Porges) frames the same process as disruption in autonomic regulation, where ventral vagal pathways lose dominance and defensive circuits begin dictating global coherence conditions. Affect regulation research (Schore) shows how trauma compromises integrative neural pathways, reducing the organism’s ability to coordinate left–right, cortical–subcortical, and limbic–prefrontal processes. Memory research (Brewin) demonstrates that trauma fragments consolidation processes, producing disjointed, intrusive, and sensory-dominant memory traces that the system cannot fully integrate. Infant–caregiver studies (Tronick) reveal how trauma fractures relational attunement; the dyadic regulatory loops needed for coherence fail, and the system defaults to self-stabilization strategies that are rigid, narrow, and costly. AToM unifies these disparate descriptions through a single claim: trauma is what happens when coherence geometry collapses and the system must reorganize around fewer, sharper, and more energetically expensive attractors.

To make this precise, AToM draws on tools from information geometry and topological data analysis (TDA), allowing trauma to be described in terms of measurable changes in the system’s underlying manifold. First, trauma triggers a loss of dimensionality. The system constrains itself to fewer degrees of freedom, analogous to manifold collapse in TDA. Behavioral flexibility narrows; perceptual priors harden; relational options shrink. What looks clinically like rigidity, avoidance, or numbing is mathematically a reduction of the system’s accessible state-space. Second, trauma increases local curvature. In information geometry, regions of high Fisher curvature correspond to hypersensitivity—small perturbations generate disproportionately large prediction errors. Clinically, this is hypervigilance: the system is trapped in a sharply curved region of its information manifold where tiny cues produce cascading defensive responses. Third, trauma induces bottlenecking. Persistent homology, a core tool in TDA, reveals how trauma narrows viable pathways between cognitive, affective, and relational regions. In dynamical systems terms, this is attractor entrapment: the system can enter certain states easily but struggles to exit them. Rumination, intrusive memories, and sudden shutdowns all map onto these narrowed topological corridors.

Fourth, trauma exhibits hysteresis and partial irreversibility. Once the system’s geometry collapses into a lower-dimensional, high-curvature configuration, it does not simply return to its earlier shape when the threat subsides. Dynamical hysteresis means the system requires significantly more energy or supportive scaffolding to regain its original configurational freedom. This elegantly explains the clinical observation that “safety alone is not enough.” Even in calm environments, the system remains in the trauma-shaped geometry until sufficient relational, somatic, and cognitive integration re-expands its manifold. Fifth, dissociation is modeled as boundary formation. Rather than treating dissociation as absence, AToM frames it as the creation of topological boundaries that isolate high-curvature regions from the rest of the system. These boundaries prevent destabilizing cross-talk between subsystems but at the cost of integration. Dissociated states are thus protective geometric partitions—dynamic boundary conditions the system uses to preserve minimal coherence when global coherence is impossible.

Crucially, these geometric transformations—collapse, curvature, bottlenecking, hysteresis, and boundary formation—are common features of nonlinear, self-organizing systems under extreme constraint. They appear in materials science, neural synchronization models, ecological networks, and even economic collapse dynamics. AToM’s contribution is not to stretch physics analogies beyond their domain but to use the mathematical vocabulary of coherence geometry to unify established clinical, developmental, and neuroscientific findings. Trauma becomes legible not as a mysterious break in narrative but as a predictable, analyzable reconfiguration of a system struggling to maintain coherence under overwhelming constraint. This provides a rigorous bridge between subjective experience, neural mechanism, and complex-systems mathematics—without reducing any level to another.

5. Physics as a Boundary Case: High Symmetry, Low Variability

AToM takes physics seriously precisely by not misusing it. Instead of treating physics as a universal metaphor for human meaning-making, AToM positions physics as a boundary case: the extreme end of the constraint spectrum where variability is minimized and theoretical freedom is sharply restricted. In fundamental physics, the primitive is not narrative, representation, or experience—it is invariance under symmetry groups. The Poincaré group, Lorentz invariance, and gauge symmetries define what counts as a physically meaningful transformation. These symmetries are so constraining that only a very narrow class of legal theories can exist at all. Noether’s theorem enforces this rigidity: every symmetry necessarily generates a conserved quantity, and any violation of symmetry collapses the entire theoretical structure. Action principles are not stylistic choices but forced outcomes of the requirement that physical systems evolve in ways that preserve consistency across frames, scales, and transformations. Most possible theories are instantly ruled out; only a tiny family of consistency-preserving formulations—quantum field theories, general relativity, gauge-invariant models—survive the elimination process. Because physics operates under such extreme constraint, it exhibits minimal recurrence options and no mirrored explanatory ladders: there is no “alternate cultural physics,” no “interpretive physics,” no “neuro-physics,” because the space of viable solutions is too tightly bounded to support pluralistic coherence structures.

This is not a weakness of physics—it is what makes physics physics. But it also clarifies the proper scope of AToM. Meaning systems, unlike physical laws, operate under softer constraints, allowing many coherence-preserving arrangements rather than a single mathematically rigid one. Human cognition, social systems, cultural meaning, and narrative structures retain internal stability not by enforcing symmetry invariance but by navigating flexible constraint landscapes: relational, emotional, ecological, symbolically encoded, and historically contingent. Where physics has a single geodesic because the metric is fixed, meaning-systems permit multiple geodesics because the metric itself adapts, bends, fragments, and reorganizes under experience. Coherence in meaning systems is something maintained, negotiated, sometimes improvised—not something guaranteed by symmetry enforcement. This difference is foundational: physics describes systems whose coherence is externally specified by invariance structures, whereas meaning-systems maintain coherence through self-organizing dynamics in contexts suffused with uncertainty, ambiguity, and competing constraints.

AToM therefore does not attempt to derive culture, psychology, or narrative from physics. Instead, it treats physics as the limiting ideal: the far pole where constraints become absolute and meaning reduces to invariance. Everything above that pole—including neural dynamics, interpersonal regulation, and cultural narrative—lives in a regime where constraints are softer and recurrence options proliferate. Meaning systems differ from physics not because they lack structure, but because they permit many more viable coherence geometries. By situating physics as a boundary case rather than a master analogy, AToM both honors the rigor of physical theory and protects the integrity of cognitive and cultural domains. This allows AToM to integrate systems-level mathematics without collapsing meaning into mechanics, preserving both precision and domain-appropriate complexity.

**6. Where AToM Properly Applies:

AToM applies most powerfully not in rigid, low-variance domains such as fundamental physics, but in high-variance meaning systems—the human domains characterized by heterogeneity, plasticity, and structurally complex histories. Human development does not follow a single trajectory; it is marked by branching pathways, sensitive windows, and idiosyncratic adaptations that differ radically from person to person. Trauma histories introduce nonlinear alterations in prediction dynamics, regulatory pathways, and relational patterns, generating individualized coherence geometries that cannot be captured by one-size-fits-all models. Cultural variability produces divergent symbolic systems, normative structures, and interpretive styles, meaning that coherence must be achieved under different constraint landscapes across societies. Relational complexity—dyads, families, teams, communities—creates multi-layered coupling where micro-level perturbations cascade into macro-level meaning shifts. Institutional dynamics add another layer, as organizations, markets, and governance structures exhibit their own attractor patterns, feedback loops, and coherence failures. And across all these levels, narrative evolution shapes identity formation: individuals and groups continuously revise the stories through which coherence is maintained, threatened, or restored.

These are precisely the kinds of systems in which AToM is most explanatory. Human meaning systems are defined by multiple stable and unstable attractors, where the same individual can occupy radically different coherence states depending on stress, context, or relational support. They exhibit collapse–recovery cycles, especially visible in trauma responses, therapeutic change, cultural renewal, institutional reform, or collective crisis. They show entrainment failures and restorations, as seen in affective misattunement, social synchrony, group coordination, and large-scale cultural shifts. They display structural hysteresis, where change is not simply reversible: once a coherence geometry collapses, the system does not automatically return to its former state without significant scaffolded integration. Their high-dimensional transformations of coherence geometry reveal the complexity of human adaptation, where systems reorganize themselves across cognitive, affective, relational, institutional, and symbolic dimensions simultaneously. And they display fractality across scales, in line with Barabási’s work on scale-free networks: patterns of stability, rupture, and reorganization recur at levels ranging from neurons to nations, from attachment dyads to global cultural flows.

These features—heterogeneity, nonlinearity, path dependence, multi-scale coupling, and fractal dynamics—are exactly the features that make coherence a central explanatory variable. Where physics operates under maximal constraint and minimal variability, human meaning systems operate under soft constraint and maximal expressive variability. AToM is therefore not an all-purpose explanatory theory but a theory targeted to the domain where it is needed: systems whose stability depends on the ongoing, adaptive negotiation of coherence under ever-shifting constraints. In these domains—development, trauma, culture, narrative, identity, organizations—AToM provides the conceptual geometry that allows us to analyze how coherence is gained, lost, restored, and transformed. This is the natural home of AToM: the world of human complexity, where coherence is neither guaranteed nor singular, but plural, emergent, and dynamically maintained.

7. Neurodivergence as Precision Coherence Sensing

Here is a fully expanded, publication-ready paragraph in the established voice and density:

AToM interprets neurodivergence—especially autistic cognition—not as deficit but as a precision coherence-sensing phenotype. Decades of research by Mottron, Pellicano, Happé, Baron-Cohen, and others converge on several core findings: autistic perception involves reduced Bayesian smoothing, meaning the system applies less top-down averaging to incoming sensory or social data; it shows enhanced detection of local irregularities, picking up discontinuities and pattern breaks that neurotypical smoothing would discard; it demonstrates atypical weighting of prediction errors, treating discrepancies as meaningful data rather than noise; it displays resistance to social conformity heuristics, maintaining internally derived coherence rather than importing group-level priors; and it relies on pattern-first reasoning, privileging structural regularities over narrative, convention, or social expectation. AToM integrates these features into a single structural claim: autistic cognition is a form of high-resolution coherence detection, a system that samples the coherence geometry of the environment with finer granularity and less filtering than neurotypical cognition.

In AToM’s coherence framework, this means autistic systems operate with narrower smoothing kernels, lower tolerance for internal inconsistency, and higher sensitivity to breaks in constraint satisfaction. In a stable environment, this trait confers exceptional pattern fidelity: the system notices the deviation in the data long before others do—anomalies in logic, shifts in relational tone, breakdowns in institutional procedure, inconsistencies in narrative, or micro-perturbations in sensory fields. Under conditions of trauma, however, these same traits become amplified through hypervigilance. Trauma-induced curvature increases the system’s prediction sensitivity; autistic precision then magnifies the detection of local coherence fractures, producing overwhelm, shutdown, or repetitive attempts to re-stabilize the geometry. What appears clinically as “rigidity” can be reframed mathematically as the system’s effort to maintain coherence in a manifold with steep curvature and high prediction volatility. Conversely, what appears as “detail-fixation” or “overfocus” is simply the system sampling its information environment at higher resolution because coarse global priors are unreliable or unavailable.

This reframing positions neurodivergence not as a malfunction but as an instrumentation layer—an evolutionary and cognitive specialization that detects early fractures in coherence across human systems. Autistic sensitivity to inconsistency in communication, logic, rule-structures, or group norms becomes legible as a functional signal rather than noise. In relational systems, autistic individuals often detect micro-ruptures in attunement that others overlook until they escalate. In institutions, they identify process failures, misaligned incentives, or incoherent policy structures well before they produce visible breakdown. In cultural systems, they notice mismatches between stated values and enacted practices. In narrative and symbolic environments, they detect structural incongruities—plot holes, moral inconsistencies, broken metaphors—long before neurotypical readers. AToM treats these not as quirks but as coherence-diagnostic capacities.

Seen through this lens, neurodivergence becomes central—not peripheral—to understanding complex meaning systems. High-variance environments benefit from agents with different coherence sensitivities. Neurotypical cognition excels at smoothing, norm alignment, and maintaining group-level coherence; autistic cognition excels at identifying failures, contradictions, and irregularities. Together they form a complementary dual-system architecture. AToM thus provides a structural explanation for why neurodivergent traits often become more vivid in contexts of trauma, misattunement, or institutional incoherence: the system is doing precisely what it is built to do—detect, with precision, where coherence has fractured and where meaning systems are no longer holding. This moves the discourse beyond pathology and places autistic cognition within the geometry of meaning itself: a high-resolution sensor for coherence gradients in complex human systems.

8. The Coherence Sensor Stack: Contemporary Measurement Infrastructure

AToM’s claim that coherence is a structural, measurable property is not merely theoretical; it aligns with a rapidly emerging coherence sensor stack in contemporary technology, allowing the geometry of meaning to be empirically tracked across linguistic, physiological, and topological modalities. Modern large language models provide powerful tools for quantifying linguistic coherence. At the token level, they compute entropy gradients, revealing points where predictive stability breaks down. Their embedding spaces provide syntactic and semantic manifold mappings, making it possible to identify where texts drift from regular, low-curvature regions of language space into ambiguous or unstable zones. Transformer attention patterns capture phase-coherence signatures, indicating whether discourse segments remain internally coordinated or fracture into local attractors. Narrative analytics—rooted in work adjacent to Tishby’s Information Bottleneck principle—yield narrative curvature metrics, quantifying how sharply a story deviates from expected narrative geodesics. In combination, LLMs offer a linguistic window into coherence geometry that scales from micro-level token transitions to macro-level narrative arcs.

Physiological sensing adds a second layer. Wearables now monitor heart-rate variability (HRV), a well-established index of autonomic flexibility and coherence; electrodermal activity (EDA), tracking sympathetic arousal; breathing synchrony, measuring interoceptive coupling and emotional regulation; and vagal tone, indexing parasympathetic regulation. These signals enable real-time modeling of autonomic coherence, capturing the system’s ability to maintain integrated, stable functioning across bodily subsystems. Polyvagal-informed metrics reveal how coherence fluctuates with stress, safety cues, relational attunement, and trauma triggers—offering a physiological counterpart to the linguistic coherence metrics derived from LLMs.

A third layer emerges from topological data analysis (TDA). Using methods such as persistent homology, mapper graphs, and Vietoris–Rips filtrations—pioneered by Carlsson, Edelsbrunner, and others—researchers can identify holes, bottlenecks, loops, and attractor basins within high-dimensional behavioral or neural data. TDA uncovers multi-scale coherence structure, revealing how patterns persist across levels of granularity. For example, a persistent 1-dimensional cycle may correspond to a recurring oscillatory pattern in neural or affective dynamics; a collapsing homology class can indicate trauma-induced dimensionality loss; a bottleneck in the persistence diagram may map onto narrowed cognitive pathways or attractor entrapment. These tools make it possible to visualize coherence and its breakdowns without imposing arbitrary parametric assumptions.

Finally, cross-modal fusion architectures, such as DeepMind’s Perceiver and Perceiver IO, allow heterogeneous data streams—text, audio, physiological signals, relational data, behavioral sequences—to be unified into a single latent space. These architectures function as coherence reconstruction engines, integrating disparate modalities into a shared manifold where cross-scale coupling can be analyzed directly. They allow researchers to assess whether linguistic coherence, autonomic coherence, and behavioral coherence align or diverge, providing a multi-channel picture of system stability. In practical terms, these models can track whether a person’s physiological state is entrained with their relational environment, whether narrative stability mirrors autonomic regulation, or whether institutional discourse aligns with organizational behavior.

Together, these layers—linguistic embedding geometries, physiological synchrony metrics, topological invariants, and multimodal integration architectures—form a robust empirical infrastructure that grounds AToM experimentally. The coherence sensor stack transforms coherence from a theoretical construct into a measurable, multi-dimensional variable, enabling quantitative research on meaning, trauma, neurodivergence, narrative evolution, group dynamics, and cultural stability. It closes the loop: meaning, understood as coherence under constraint, becomes not only philosophically rigorous but empirically observable.

9. Information-Geometric Foundations 

AToM’s commitment to precision requires that its mathematical foundations draw only from legitimate information-geometric constructs or, where new ideas are introduced, that they be explicitly labeled as proposed extensions, not smuggled-in physics analogies. The core formal scaffold begins with well-established structures in information geometry. The Fisher Information Metric provides a measure of local curvature on the statistical manifold representing cognitive, linguistic, or behavioral states. In AToM, this curvature corresponds to the system’s sensitivity to perturbation—low curvature indicating stable coherence, high curvature indicating volatility or hyperreactivity. Next, the Amari–Chentsov Tensor, a canonical higher-order geometric object, captures how information flows and deforms across the manifold. It enables rigorous modeling of what AToM informally describes as “coherence curvature,” grounding the idea in an existing mathematical lineage rather than metaphor. The third established component is KL divergence curvature, a measure derived from how Kullback–Leibler divergence behaves under infinitesimal change. This is essential for detecting sudden divergence spikes, bottlenecks, or collapse regions in coherence geometry—particularly relevant for modeling trauma, dissociation, and attractor entrapment.

To extend these foundations, AToM introduces two explicitly future-facing constructs, both labeled as proposed research directions rather than settled mathematics. The first is the Coherence Operator \hat{C}, a functional intended to compute a system’s coherence score by integrating entropy gradients, Fisher curvature profiles, and topological persistence measures. This operator is not a physical observable but a computational construct intended for multi-modal, multi-scale analysis—an analogue to operators used in quantum information theory or in manifold learning, but oriented toward cognitive and cultural coherence rather than physical states. The second is the Trauma Sensitivity Constant \tau, a proposed scalar describing how responsive a coherence manifold is to small perturbations. High-\tau systems would show steep curvature increases and rapid coherence collapse under minor stressors, while low-\tau systems would exhibit resilience and shallow deformation. This construct is intentionally speculative, serving as a conceptual placeholder for future mathematical formalization that may emerge from empirical modeling using TDA, physiological synchrony metrics, and narrative curvature analytics.

Crucially, AToM is careful not to conflate these proposals with established physical theory. They are not physics analogies, not repurposed field equations, and not attempts to extend general relativity or gauge theory into the human sciences. They are conceptual invitations: formal hypotheses about how coherence might be operationalized using tools from information geometry, computational linguistics, multimodal sensing, and topological analysis. By clearly separating established quantities (Fisher metric, Amari–Chentsov tensor, KL curvature) from proposed constructs (\hat{C}, \tau), AToM maintains intellectual rigor and avoids the pitfalls of metaphor-driven overreach. The goal is not to physics-ify psychology but to build a precision vocabulary capable of bridging phenomenology, neuroscience, culture, and computation. This explicit boundary-setting ensures that AToM’s mathematical foundations remain credible, extensible, and grounded in legitimate cross-disciplinary science.

10. Empirical Tests and Falsifiability

AToM is not merely a conceptual synthesis; it produces empirically falsifiable predictions that distinguish it from metaphorical or purely interpretive theories of mind. If coherence is a measurable property of cognitive, relational, and cultural systems, then coherence metrics should outperform traditional psychological constructs in predictive accuracy. One central prediction is that coherence scores will correlate with HRV variance more strongly than standard psychometric instruments, because HRV indexes autonomic integration rather than self-report narratives. A second testable claim is that linguistic curvature—derived from LLM-based entropy gradients, narrative arc smoothness, and semantic manifold geometry—will shift prior to observable therapeutic breakthroughs. In other words, changes in coherence geometry should precede changes in explicit self-understanding, providing a measurable leading indicator of clinical progress or impending rupture. A third prediction is that autistic + LLM collaborative teams will detect coherence fractures that neurotypical raters routinely miss, due to the combined precision-sensing of autistic cognition and the high-resolution pattern detection of language models. If AToM is correct, these hybrid systems should reliably identify inconsistencies in discourse, institutional protocols, relational dynamics, or narrative structures that neurotypical smoothing obscures.

Similarly, AToM predicts that entrainment breakdowns—in physiological synchrony, conversational pacing, affective resonance, or organizational alignment—will forecast relational or institutional collapse earlier than conventional indicators. This applies equally to couples drifting out of attunement, teams losing alignment, or institutions approaching crisis; coherence should disintegrate at micro-scales before macro-failure becomes visible. A further prediction is that topological bottlenecks detected through persistent homology will forecast trauma relapse risk, capturing the narrowing of cognitive–affective pathways before a system re-enters a maladaptive attractor. These bottlenecks should be detectable across modalities: physiological data (HRV/EDA), linguistic manifolds, behavioral trajectories, or neurocognitive task performance.

Each of these predictions is directly falsifiable. If coherence metrics fail to outperform psychometric constructs, the theory is weakened. If linguistic curvature does not shift before therapeutic change, the proposed leading-indicator role collapses. If autistic–LLM detection does not exceed neurotypical accuracy, the high-resolution coherence-sensing model is incorrect. If entrainment breakdowns do not precede relational or institutional failure, then coherence cannot function as a cross-scale stability predictor. And if topological bottlenecks do not correlate with trauma relapse, then the manifold-collapse model of trauma lacks empirical grounding. To evaluate these claims, AToM requires interdisciplinary datasets—linguistic corpora annotated with therapeutic outcomes, physiological synchrony recordings from relational or group settings, TDA analyses of behavioral or neural trajectories, and collaborative studies involving neurodivergent raters. These tests move AToM out of the realm of speculative synthesis and into the domain of empirically adjudicable science.

11. Conclusion: Meaning as Coherence

Here is a polished, expanded concluding paragraph that integrates the full logic of the manuscript and maintains the tone, precision, and theoretical seriousness of the prior sections:

AToM ultimately advances a unified claim: coherence is the structural invariant that organizes cognition, experience, culture, and social systems. Meaning does not arise from static symbols, introspective content, or narrative alone, but from the system’s ongoing ability to maintain, lose, and restore coherence across multiple scales of organization. Trauma, in this framework, is not simply a narrative wound or psychological category—it is a collapse in coherence geometry, a deformation of the underlying manifold that constrains freedom, increases curvature, creates bottlenecks, and produces hysteresis. Entrainment—physiological, interpersonal, linguistic, behavioral, and cultural—is the cross-scale dynamical class that generates and restores coherence, allowing meaning to propagate through loops of prediction, regulation, and mutual influence. Neurodiversity, far from being an outlier or deficit model, provides essential coherence-sensing capacity, offering high-resolution detection of pattern breaks, constraint fractures, and structural inconsistencies that neurotypical smoothing often obscures. Modern sensing and modeling tools—from LLM-derived linguistic curvature to physiological synchrony metrics, from topological persistence diagrams to multimodal fusion architectures—make coherence empirically measurable, enabling a science of meaning grounded in data rather than metaphor.

Crucially, physics serves not as a template but as a boundary case: the limiting regime where constraints are maximal, symmetries rigid, and recurrence options minimal. AToM respects this boundary by avoiding the misuse of physical theory while drawing inspiration from the mathematical architectures—information geometry, invariance principles, manifold curvature—that remain domain-appropriate. In contrast to physics, human meaning systems operate under soft constraints, high variability, developmental path-dependence, and fractal cross-scale organization. It is within these high-variance domains—relationships, identity formation, cultural evolution, institutional behavior, trauma processing—that coherence becomes the central explanatory variable. AToM therefore situates itself not as a grand unifying theory of everything but as a foundational research program for the emerging science of meaning: empirically tractable, mathematically grounded, interdisciplinary, and attuned to the specific dynamics of complex human systems.

In this view, meaning is not an esoteric phenomenon but a dynamically maintained property of systems striving to remain coherent under constraint. AToM offers the conceptual and mathematical language needed to study those dynamics rigorously—how coherence emerges, how it fails, how it can be restored, and how diverse cognitive phenotypes contribute to its maintenance. By bringing together predictive processing, enactivism, information geometry, complex systems theory, neurodiversity research, and modern computational infrastructure, AToM outlines a precise, falsifiable, and generative framework for building a mature science of meaning.

References

Amari, S.-I. (1987). Differential-geometrical methods in statistics. Springer.

Amari, S.-I., & Nagaoka, H. (2000). Methods of information geometry. American Mathematical Society.

Ay, N., & Knauf, A. (2017). Maximizing multi-information. Kybernetika, 53(2), 221–236.

Baron-Cohen, S. (2009). Autism: The empathizing–systemizing (E-S) theory. Annals of the New York Academy of Sciences, 1156(1), 68–80. https://doi.org/10.1111/j.1749-6632.2009.04467.x

Bowlby, J. (1969). Attachment and loss: Vol. 1. Attachment. Basic Books.

Brewin, C. R. (2014). Episodic memory, perceptual memory, and their interaction: Foundations for a theory of posttraumatic stress disorder. Psychological Bulletin, 140(1), 69–97. https://doi.org/10.1037/a0033722

Bruner, J. (1990). Acts of meaning. Harvard University Press.

Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46(2), 255–308. https://doi.org/10.1090/S0273-0979-09-01249-X

Clark, A. (2016). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.

Crutchfield, J. P. (2012). Between order and chaos. Nature Physics, 8(1), 17–24.

Edelsbrunner, H., & Harer, J. (2010). Computational topology: An introduction. American Mathematical Society.

Feldman, R. (2007). Parent–infant synchrony and the construction of shared timing. Advances in Consciousness Research, 75, 159–181.

Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787

Geertz, C. (1973). The interpretation of cultures. Basic Books.

Henrich, J. (2015). The secret of our success: How culture is driving human evolution. Princeton University Press.

Hohwy, J. (2013). The predictive mind. Oxford University Press.

Husserl, E. (1913/1983). Ideas pertaining to a pure phenomenology and to a phenomenological philosophy: First book. Kluwer.

Kelso, J. A. S. (1995). Dynamic patterns: The self-organization of brain and behavior. MIT Press.

Lakatos, P., Gross, J., & Thut, G. (2019). A new unifying account of the roles of neuronal entrainment. Nature Reviews Neuroscience, 20(6), 347–363.

Lawson, R. P., Mathys, C., & Rees, G. (2017). Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience, 20(9), 1293–1299.

Levine, P. A. (2010). In an unspoken voice: How the body releases trauma and restores goodness. North Atlantic Books.

Main, M., & Solomon, J. (1990). Procedures for identifying infants as disorganized/disoriented during the Ainsworth Strange Situation. In M. T. Greenberg, D. Cicchetti, & E. M. Cummings (Eds.), Attachment in the preschool years (pp. 121–160). University of Chicago Press.

McAdams, D. P. (2001). The psychology of life stories. Review of General Psychology, 5(2), 100–122.

Mottron, L., Dawson, M., Soulières, I., Hubert, B., & Burack, J. (2006). Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 36(1), 27–43.

Noether, E. (1918). Invariante Variationsprobleme. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, 1918, 235–257.

Ogden, P., Minton, K., & Pain, C. (2006). Trauma and the body: A sensorimotor approach to psychotherapy. W. W. Norton.

Pellicano, E., & Burr, D. (2012). When the world becomes “too real”: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16(10), 504–510.

Pickering, M. J., & Garrod, S. (2013). An integrated theory of language production and comprehension. Behavioral and Brain Sciences, 36(4), 329–347.

Porges, S. W. (2011). The polyvagal theory: Neurophysiological foundations of emotions, attachment, communication, and self-regulation. W. W. Norton.

Schneider, E. D., & Kay, J. J. (1994). Life as a manifestation of the second law of thermodynamics. Mathematical and Computer Modelling, 19(6–8), 25–48.

Schore, A. N. (2012). The science of the art of psychotherapy. W. W. Norton.

Siegel, D. J. (2012). The developing mind: How relationships and the brain interact to shape who we are (2nd ed.). Guilford Press.

Sporns, O. (2011). Networks of the brain. MIT Press.

Strogatz, S. H. (2018). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering (2nd ed.). CRC Press.

Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. MIT Press.

Tishby, N., & Polani, D. (2011). Information theory of decisions and actions. In V. Cutsuridis et al. (Eds.), Perception-action cycle (pp. 601–636). Springer.

Tronick, E. Z. (2007). The neurobehavioral and social-emotional development of infants and children. W. W. Norton.

van der Kolk, B. A. (2014). The body keeps the score: Brain, mind, and body in the healing of trauma. Viking.

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. MIT Press.

Weick, K. E. (1995). Sensemaking in organizations. Sage.

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686–688.