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One graph language aims to unify every major theory of mind

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2026-07-08 17:48 UTC pith:WWVAB5YH

load-bearing objection NEST is an ambitious, well-organized graph ontology for cognitive states, but its central unification claim is currently vacuous: the core operators are defined as arbitrary functions of the correct type, so every cognitive architecture is trivially an instance of the framework. the 2 major comments →

arxiv 2607.06055 v1 pith:WWVAB5YH submitted 2026-07-07 cs.HC cs.CL

Nested Episodic State Topology (NEST): A Graph-Theoretic Architecture of Cognitive States

Ishant This is my paper
classification cs.HC cs.CL
keywords beliefgraphsstatecognitionconflictcoreempiricalepisodic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper proposes NEST (Nested Episodic State Topology), a graph-theoretic representational ontology intended to serve as a shared formal language for cognitive science. The central claim is that cognitive states—concepts, episodes, percepts, task contexts—can be modeled as typed, weighted, recursive graphs whose nodes can carry internal subgraph payloads, with edges partitioned into six relation classes: causal, containment, temporal, associative, evidential, and spatial. By separating durable belief graphs from capacity-limited working-memory graphs, and by defining a reusable toolkit of operators for activation, conflict detection, belief update, and awareness, the paper argues that major cognitive frameworks—ACT-R, Soar, Sigma, the Common Model of Cognition, Global Workspace Theory, semantic networks, and Theory-Theory—can be expressed as constrained regions of a single formal language without introducing new primitives for each. The paper's contribution is explicitly foundational rather than empirical: it offers a transparent substrate on which later computational, empirical, and domain-specific work could be built. The author is trying to establish that what appears as deep theoretical fragmentation in cognitive science is partly a representational artifact—different theories expressed in incompatible formal systems—and that a sufficiently expressive graph ontology can expose their structural commonalities and differences on equal footing.

Core claim

The paper's central technical contribution is the construction of a recursive graph ontology in which nodes may themselves contain subgraphs (graph payloads), edges are typed under six disjoint relation classes each governed by structural constraints (e.g., causal edges are antisymmetric and acyclic; containment edges require existential dependency; evidential edges carry signed weights), and a separation between durable belief graphs and capacity-limited working-memory graphs is enforced. On this substrate, the paper defines a compact operator toolkit—activation maps, graph-property functionals (fragmentation, distraction, cognitive load), working-memory transition operators, awareness and控

What carries the argument

The load-bearing machinery is the recursive base graph G=(V,E,R,τ,w) with six edge-type classes (causal, containment, temporal, associative, evidential, spatial); the separation of belief graph B from working-memory graph W_t; the incompatibility constraint (F,S) and conflict catalog constructor BuildConflictCatalog; the belief-update operator Δ• with micro and macro application interfaces; the awareness functional A selecting accessed subgraphs; the control functional K emitting control operations; and the chunking operator U_χ compressing co-active nodes into recursive graph payloads. Framework mappings in Section 5 are the culminating technical step.

Load-bearing premise

The paper assumes that mapping the high-level architectural variables of existing cognitive frameworks (ACT-R buffers, Soar problem spaces, GWT workspaces) to generic graph-theoretic constructs (designated subgraphs, transition operators, access-privileged regions) constitutes a meaningful theoretical unification rather than a relabeling. If the mapped graph operators lack the computational specificity to reproduce the actual dynamics of the original architectures, the unific

What would settle it

A concrete falsifier would be selecting a well-studied cognitive phenomenon with established empirical signatures in two different frameworks (e.g., a chunking task modeled in both ACT-R and Soar), implementing both within NEST, and showing that the graph-theoretic diagnostics fail to reproduce the quantitative predictions either architecture makes—demonstrating that the variable-level mappings in Section 5 are not sufficiently fine-grained to capture the dynamics they claim to subsume.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If NEST's mappings are computationally realized, two cognitive architectures (e.g., ACT-R and Soar) could be compared by inspecting which graph objects they treat as primitive, which transitions they license, and which conflict patterns they forbid—enabling direct structural rather than merely informal comparison.
  • The graph-theoretic definitions of cognitive load (intrinsic as task-schema size, extraneous as avoidable working-memory complexity, germane as productive belief-graph restructuring) could, if implemented, yield quantitative load predictions from structural graph properties rather than from behavioral proxies alone.
  • The conflict catalog mechanism—where transient working-memory content is tested against stored belief via collation graphs—provides a formal language for modeling phenomena like cognitive dissonance, failed recognition, and confusion as graph-theoretic incompatibility events, which could be tested against behavioral or neuroimaging data.
  • Conceptual change, modeled as macro-scale belief-graph restructuring under Δ, could be studied empirically as learnable graph-rewrite behavior, potentially connecting to education research on schema formation and theory revision.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The paper's unification claim would be substantially strengthened if a structure-preserving simulation were demonstrated: e.g., running a simple ACT-R production system and a Soar problem-space task within the NEST substrate and showing that the graph-theoretic diagnostics (fragmentation, conflict count, coherence) reproduce known behavioral predictions from each architecture. The paper acknowledg
  • The separation of awareness functional A from the full working-memory graph W_t implies that cognitive failures can arise not from missing content but from content being present in W_t yet outside A(W_t)—a formal account of access-limited control failure. This connects to debates about conscious versus unconscious processing, but the paper does not draw out the empirical consequences.
  • The six edge-type partition is presented as canonical, but the paper does not argue for its completeness. A natural extension would test whether any cognitive relation central to existing frameworks resists classification into these six classes, which would either validate the taxonomy or reveal its boundaries.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. The paper presents NEST (Nested Episodic State Topology), a graph-theoretic representational ontology for cognitive science. The core formalism (Section 2) defines typed, weighted, recursive graphs with six edge classes, separating durable belief graphs (B) from capacity-limited working-memory graphs (W_t). A toolkit of generic functionals and operators—activation maps, graph-property functionals, conflict catalogs, coherence measures, a working-memory transition operator U, a belief-update operator Δ, an awareness functional A, and a control functional K—is introduced. Section 3 derives graph-theoretic signatures for phenomena including fragmentation, recognition, distraction, confusion, cognitive load, and conceptual change. Section 4 presents a task-instantiation schema for action selection and failure-mode reasoning. Section 5 provides compatibility mappings to ACT-R, Soar, Sigma/CMC, GWT, semantic networks, Theory-Theory, and graph-based AI. The paper is explicitly foundational: it offers a representational substrate rather than a computational implementation or empirical model.

Significance. The paper addresses a genuine gap in cognitive science: the lack of a shared representational language for comparing theories. The formal apparatus is internally consistent at the level of type signatures, and the six-edge-class taxonomy with recursive node payloads is a reasonable design choice for a representational substrate. The separation of belief and working-memory graphs, the incompatibility/conflict catalog machinery (Definitions 19–28), and the encoding gate (Remark 31) constitute a coherent framework for reasoning about belief revision under conflict. The cognitive load decomposition (Section 3.5) and the task-instantiation schema (Section 4) are thoughtful applications. However, the significance is substantially limited by the fact that the central operators (U, Δ, A, K) are defined as arbitrary functions of the appropriate type, which makes the unification claim difficult to evaluate as stated. No computational implementation, simulation, or worked example with concrete dynamics is provided to verify that the framework reproduces the behavior of the architectures it claims to unify.

major comments (2)
  1. §2.4, Definition 36 (p. 12): The working-memory transition operator U is defined as 'any function U: W × B → W.' Similarly, Definition 42 defines Δ as 'any function Δ: B × W* → B,' Definition 60 defines A as 'any mapping A: W → 2^W,' and Definition 72 defines K as 'any trajectory functional K: W* → O.' When the core operators are unconstrained function spaces, the claim in §5 that existing frameworks (ACT-R, Soar, etc.) are 'constrained regions of one language' is at risk of being trivially true: any computation is an instance of an unconstrained function. The structural constraints NEST does impose (six edge types, incompatibility constraints (F,S), capacity limits) constrain the representational substrate but not the transition or update dynamics. This is load-bearing for the paper's central unification claim. The authors should either (a) add non-trivial constraints on U and Δ (e.g.,U
  2. §5 (Mappings to Existing Frameworks, pp. 33–37): The mappings presented are variable-level correspondences—e.g., 'ACT-R buffers correspond to designated active subgraphs of W_t' (§5.1), 'Soar operators correspond to candidate graph transitions' (§5.2). These mappings do not demonstrate that NEST's operators can reproduce the actual dynamics of the target architectures. For example, ACT-R productions have specific matching, conflict resolution, and firing semantics; the paper states that 'productions correspond to constrained transition templates: condition-sensitive instances of U' but does not specify what the constraints are or how production matching is realized. Without at least one worked example showing that a specific ACT-R production (or Soar operator) can be faithfully represented as a NEST graph transition with specified constraints, the unification claim remains unsupported. A
minor comments (7)
  1. The paper uses a large number of symbols with similar notation (e.g., C for conflict catalogs vs. C_V, C_E for capacity; S for relational incompatibility vs. S_self for self-image; σ for grounding scores vs. σ_Q for signing functions; τ for edge-type maps vs. τ_trans for transition strictness). A notation table would aid readability.
  2. Definition 39 (p. 12): The parameter bundle Θ includes θ_inv and θ_coh, but these thresholds are not referenced in the definitions of involvement (Definition 32) or coherence (Definition 30) themselves—they appear only later in application contexts (e.g., Definition 84). This makes the definition feel forward-referencing and disconnected from the point of definition.
  3. Remark 31 (p. 11): The encoding gate is introduced in a remark rather than a definition, despite being a load-bearing constraint on belief update. Consider promoting it to a formal definition.
  4. §3.5, Proposition 87 (p. 24): The trade-off constraint L_int + L_ext + L_ger ≤ C_CM is stated as following 'directly from modeling load measures as counts or weighted counts of nodes and edges within a capacity-limited working-memory graph.' However, L_ger is defined as δ_coh(B, B'), a coherence-change measure over belief graphs, not a count of WM elements. The proof sketch does not address this mismatch and needs justification.
  5. Figure 1 (p. 3): The caption states 'specific relation names are domain-dependent and belong in captions or instantiations, not in schematic figures,' but the figure itself shows generic labels r_1, ..., r_4 without indicating which edge classes they represent. Adding a brief note on which classes are illustrated would improve the figure's pedagogical value.
  6. §5.7 (Graph-Based AI, pp. 36–37): This subsection reads more as a literature survey than a compatibility mapping. Unlike §5.1–§5.6, it does not specify which NEST variables correspond to which graph-based AI constructs. Consider either adding specific correspondences or trimming to keep parallel structure with other subsections.
  7. The paper has no empirical validation, simulation, or even a small worked numerical example. While the authors explicitly acknowledge this as a deliberate foundational contribution (§6.2), at least one concrete instantiation—even a toy example showing a few time steps of W_t evolution under a specified U, with conflict detection and belief update—would substantially strengthen the paper and demonstrate that the operators compose as intended.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for a careful and constructive reading of the manuscript. The two major comments both concern the gap between NEST's representational substrate and its dynamic operators: the referee observes that U, Δ, A, and K are defined as unconstrained function spaces, and that the Section 5 mappings are variable-level correspondences rather than demonstrations of faithful dynamical reproduction. We agree that this gap is real and that the paper must do more to address it. Below we respond to each comment and describe the revisions we will make.

read point-by-point responses

  1. Referee: §2.4, Definition 36: U is defined as 'any function U: W × B → W.' Similarly, Δ (Def. 42), A (Def. 60), and K (Def. 72) are unconstrained function spaces. When core operators are unconstrained, the unification claim in §5 is at risk of being trivially true. The authors should add non-trivial constraints on U and Δ, or otherwise address the triviality concern.

    Authors: The referee is correct that defining U, Δ, A, and K as arbitrary functions of the appropriate type does not, by itself, constrain dynamics. We accept this point and will revise the manuscript to address it. Our response has two parts. First, a concession: we agree that the current definitions are too permissive to support the unification claim as stated, and we will add a new subsection (§2.4.2) that imposes non-trivial structural constraints on each operator. Specifically: (a) U will be constrained to respect capacity bounds (C_V, C_E), the activation threshold θ_W for WM membership, and the encoding gate of Remark 31 (κ = 0 at commit); (b) Δ will be constrained to produce only well-formed graphs under (F_B, S_B) at commit, with ApplyMicroUpdate requiring κ(C_wb) = 0 and ApplyMacroUpdate requiring well-formedness of the committed output even when τ_trans = permit allows transient violations; (c) A will be constrained to select subgraphs whose nodes are WM-active (α ≥ θ_W); (d) K will be constrained to emit only operations from the admissible set O (Definition 73) and to be awareness-conditioned (Definition 75) when the architecture requires it. These constraints are already present in scattered form across Definitions 37, 42, 46, 73, 75, and Remark 31, but we agree they have not been consolidated into operator-level admissibility conditions, and their absence from the operator definitions themselves creates the impression of unconstrained function spaces. Second, a clarification of scope: even with these constraints, NEST does not uniquely specify dynamics—it specifies a constrained operator space within which particular architectures (ACT-R, Soar, etc.) correspond to particular choices of U, Δ, A, K, and Θ. The unification claim is therefore not that NEST reproduces a revision: no

  2. Referee: §5 mappings are variable-level correspondences and do not demonstrate that NEST's operators can reproduce the actual dynamics of target architectures. Without at least one worked example showing that a specific ACT-R production or Soar operator can be faithfully represented as a NEST graph transition with specified constraints, the unification claim remains unsupported.

    Authors: We agree that at least one worked example is needed, and we will add one. In the revised §5.1, we will include a fully specified ACT-R production example—specifically, a production that matches a retrieval buffer containing a chunk of type 'addition-fact' with slots augend and result, fires, and updates the goal buffer. We will show: (1) the buffer contents as a designated active subgraph of W_t with typed nodes and edges; (2) the production as a constrained instance of U whose preconditions are specified as a graph-pattern match (using sim_match from Definition 80 over the buffer subgraph); (3) the conflict-resolution semantics as a selection among multiple candidate U-instances ranked by activation-weighted priority; (4) the firing as the selected U-instance producing W_{t+1} with the goal buffer subgraph updated. We will specify the constraints on U that make this work: the production's LHS is a graph pattern P_lhs, the match is an injective partial match f_match preserving edge types, and the RHS is a graph rewrite rule that adds, deletes, or retypes nodes and edges in the designated buffer subgraph. This makes the production a graph-rewrite instance of U constrained by pattern-matching preconditions and buffer-designated scope. We acknowledge that this single example does not prove that all of ACT-R's dynamics can be faithfully reproduced—that would require a full formal encoding, which is beyond the scope of a foundational paper. But it does show concretely how the mapping works and what the constraints are, which we agree is necessary to make the unification claim evaluable rather than merely asserted. We will also add a briefer parallel example for Soar (operator selection in a problem space as candidate graph transitions with impasse as a failure state under §4 revision: no

standing simulated objections not resolved
  • We cannot, within the scope of this paper, provide a full formal proof that NEST faithfully reproduces the complete dynamics of ACT-R, Soar, or any other target architecture. Such a proof would require encoding the full operational semantics of each architecture and demonstrating bisimulation or equivalent preservation—this is a substantial research program, not a revision. The referee's concern about the strength of the unification claim is therefore partly a standing limitation: the paper can make the claim more precise and illustrate it with worked examples, but cannot fully discharge it here.

Circularity Check

0 steps flagged

No significant circularity; the framework is self-contained with standard graph-theoretic definitions and no self-citation chains. The main concern is vacuity of operator definitions, which is a correctness issue, not circularity.

full rationale

The paper defines NEST as a foundational graph-theoretic ontology using standard mathematical definitions (typed weighted graphs, activation maps, conflict catalogs, transition operators). The derivation chain is: (1) define base graphs and recursive nodes (Definitions 1–4), (2) define belief/working-memory graphs (Definitions 8–12), (3) define generic functionals and operators (Definitions 15–72), (4) instantiate these for cognitive phenomena (Section 3), task schemas (Section 4), and framework mappings (Section 5). No step in this chain reduces to its inputs by construction. The operators U, Δ, A, K are defined as arbitrary functions of the appropriate type signature (e.g., Definition 36: 'U: W × B → W' as 'any function'; Definition 42: 'Δ: B × W* → B' as 'any function'). This makes the Section 5 mappings (ACT-R productions as 'constrained instances of U,' Soar operators as 'candidate graph transitions') trivially true rather than substantively derived — but this is a vacuity/correctness concern, not circularity. The paper does not define X in terms of Y and then derive Y from X. It does not fit parameters to data and then predict the same data. There are no self-citations (single author, all references are to external work). The cognitive-phenomenon definitions (distraction as Shannon entropy over component masses, confusion as a disjunction of disconnection/incoherence/conflict) are new constructions from the paper's own primitives, not renamings presented as derivations. The paper is transparent that Section 5 mappings are 'compatibility mappings' and 'variable-level sketches rather than executable comparative simulations.' Score 1 reflects the absence of circularity with a minor note that the breadth of operator definitions makes some 'unification' claims vacuous — a correctness risk rather than a circularity defect.

Axiom & Free-Parameter Ledger

9 free parameters · 4 axioms · 4 invented entities

The axiom ledger captures the core assumptions and invented formal constructs of the NEST framework. The free parameters are all thresholds and weights that would need to be fitted in any computational implementation. The axioms represent the domain assumptions that make the graph-theoretic approach viable. The invented entities are the core formal operators that constitute the NEST ontology, none of which have independent empirical validation yet.

free parameters (9)
  • C_V, C_E
    Working-memory capacity bounds on nodes and edges, stated as architecture-specific constants (Definition 10).
  • theta_W
    WM membership activation cutoff (Definition 15).
  • theta_gnd
    Grounding correspondence threshold for WM-belief collation (Definition 21).
  • theta_sim
    Recognition threshold for graph similarity (Definition 81).
  • theta_dist
    Distraction threshold (Definition 83).
  • theta_coh
    Coherence threshold for confusion predicate (Definition 84).
  • lambda_match
    Weighting parameter for graph similarity match (Definition 80).
  • beta_Q
    Salience coefficients for designated subgraphs (Definition 37).
  • lambda
    Edge complexity weight in cognitive load measures (Definition 85).
axioms (4)
  • domain assumption Cognition can be adequately modeled as structured state formation and transformation using finite, typed, weighted graphs.
    This is the foundational premise of the paper, stated in the Abstract and Introduction, assuming graph theory is the appropriate substrate for cognitive representation.
  • domain assumption The six relation classes (causal, containment, temporal, associative, evidential, spatial) are sufficient to capture the necessary structural commitments of major cognitive theories.
    Stated in Definition 4, this taxonomy is assumed to be comprehensive enough for the mappings in Section 5 to hold.
  • domain assumption A clear separation between durable belief graphs and capacity-limited working-memory graphs maps meaningfully onto human cognitive architecture.
    Stated in Section 2.2-2.3, this separation is a core architectural commitment that the paper assumes is valid.
  • ad hoc to paper Variable-level correspondences between existing frameworks and NEST constructs constitute meaningful theoretical unification.
    Section 5 relies on the assumption that mapping concepts like 'ACT-R buffers' to 'designated active subgraphs' preserves the theoretical content of the original framework.
invented entities (4)
  • Recursive node with representational payload no independent evidence
    purpose: To allow nodes in a graph to contain internal subgraphs, enabling nested representation of concepts, episodes, and percepts.
    A formal construct of the NEST ontology (Definition 3), not yet empirically validated as a cognitive mechanism.
  • WM-belief collation graph no independent evidence
    purpose: To test transient working-memory content against stored knowledge by combining WM and belief graphs with grounding edges.
    A formal construct (Definition 21) invented to model conflict detection between memory systems.
  • Awareness functional A no independent evidence
    purpose: To model the capacity of the system to have access to any part of working memory.
    A formal operator (Definition 60) mapping working memory states to accessed subgraphs, lacking empirical validation.
  • Control functional K no independent evidence
    purpose: To regulate subsequent working-memory evolution by emitting control operations based on trajectory history.
    A formal operator (Definition 72) acting as a regulatory mechanism, not yet tied to specific neural or behavioral data.

pith-pipeline@v1.1.0-glm · 33444 in / 2947 out tokens · 561534 ms · 2026-07-08T17:48:55.001445+00:00 · methodology

0 comments
read the original abstract

We present NEST (Nested Episodic State Topology), a foundational graph-theoretic representational ontology for modeling cognition as structured state formation and transformation rather than as a finished empirical model. Concepts, episodes, percepts, and task contexts are represented as typed, weighted graphs whose nodes may carry internal subgraph payloads; edges are typed under six relation classes -- causal, containment, temporal, associative, evidential, and spatial. Durable belief graphs are separated from capacity-limited working-memory graphs that may host transient non-belief content. WM-belief grounding, conflict catalogs, and belief-update operators specify how transient structure is tested against stored knowledge and how belief is revised. A reusable operator toolkit -- activation, graph-property functionals, working-memory transitions, awareness and trajectory functionals, and belief update -- organizes the formal core. Derived diagnostics such as fragmentation, involvement, signed evaluation, coherence, and active conflict define familiar phenomena in the same ontology; self-related processing is modeled through designated self-image subgraphs within belief. Subsequent sections instantiate this core without new primitives: phenomena signatures, a task-instantiation schema for action selection and failure modes, and compatibility mappings that embed ACT-R, Soar, Sigma, the Common Model of Cognition, Global Workspace Theory, semantic networks, Theory-Theory, and chunking as constrained regions of one language. Mappings constitute the culminating technical section; discussion addresses scope, limitations, and open research directions. The contribution is intentionally foundational: a transparent representational substrate for later empirical, computational, and domain-specific work.

Figures

Figures reproduced from arXiv: 2607.06055 by Ishant.

Figure 1
Figure 1. Figure 1: Base graph G = (V, E, R, τ, w): nodes vi ∈ V , directed edges (vi , vj ) ∈ E, and edge labels rk ∈ R illustrating τ (e) for representative edges e (specific relation names are domain-dependent and belong in captions or instantiations, not in schematic figures). 3. a perceptual payload, such as a visual, auditory, tactile, or motor chunk, 4. a structured perceptual payload, i.e., a perceptual payload whose … view at source ↗
Figure 2
Figure 2. Figure 2: Recursive node v = (cv, Pv) with the five representational payload types from Definition 2: (1) empty Pv = ∅; (2) linguistic Pv (e.g. a word token ⟨w⟩); (3) atomic perceptual Pv; (4) structured perceptual Pv as an internal subgraph; (5) graph payload Gv with internal nodes ui [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A mental model M as a connected subgraph of the belief graph B [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Fragmented working memory with multiple weakly related components C1, C2, C3. Definition 80 (Graph similarity). Let Gq = (Vq, Eq) be a query graph and M = (VM, EM) a candidate graph with typed edges. A partial match from Gq to M is an injective mapping fmatch : Vq → VM preserving a specified subset of edge types from the edge-type taxonomy (Definition 4). Let mV = |fmatch(Vq)| and let mE be the number of e… view at source ↗
Figure 5
Figure 5. Figure 5: Recognition as partial graph matching between Wt and a stored mental model M. Definition 83 (Distracted predicate). The distracted predicate holds when F(Wt) > 1 and Φdist(Wt) > θdist for the distraction threshold θdist in the parameter bundle Θ (Definition 39). Distraction is distinct from partial awareness: when Wt is fragmented, A(Wt) may cover one component, several components, or any other subgraph of… view at source ↗
Figure 6
Figure 6. Figure 6: Intrinsic load Lint = |V ∗ | + λ|E∗ | for minimal task schemas S ∗ = (V ∗ , E∗ ) of increasing size and interconnectivity (left to right: |V ∗ | = 2, |E∗ | = 1; |V ∗ | = 3, |E∗ | = 2; |V ∗ | = 4, |E∗ | = 5). Highlighted nodes and edges denote the task-relevant subgraph that must be coordinated in working memory. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Extraneous load Lext(Wt) = |VWt \Rt|+λ|EWt \ Esal t | in working memory Wt: green nodes lie in the task-relevant set Rt; muted nodes and edges contribute avoidable complexity (left to right: |VWt \Rt| = 0; = 1; = 3 with a separate extraneous subgraph) [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Germane load Lger = δcoh(B, B′ ): belief graph B updated to B′ via productive restructuring (left to right: chunk formation by adding internal links among previously isolated nodes; integration via a new cross-link; alignment by bridging a mental-model subset M = {v1, v2} into the wider belief graph) [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Context graph Ct with action branches, outcomes, and failure-mode nodes [PITH_FULL_IMAGE:figures/full_fig_p029_9.png] view at source ↗

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