A Hierarchical Energy-Based Model for Multimodal Cognition

Subir Varma

REVIEW 5 major objections 6 minor 5 references

A Hierarchical Energy-Based Model for Multimodal Cognition

T0 review · 5 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper claims that a hierarchical, energy-based hub-and-spoke architecture, with per-modality diffusion-based predictive-coding pipelines converging on an amodal hub, provides a mechanistic account of attention, perceptual…

desk verdict A clear, ambitious theoretical proposal that deserves a serious referee but not acceptance on its current evidence: the claimed 'recoveries' of surprisal and ERP components are mostly definitional or analogical, pending implementation and quantitative tests. read the letter →

arxiv 2608.12398 v1 pith:EFEMMCFL submitted 2026-08-08 q-bio.NC cs.AI

Subir Varma This is my paper · ORCID

classification q-bio.NCcs.AI

keywords predictiveprocessingcodingenergy-basedmodelsdiffusioneffectivetheorymultimodalcognitionhub-and-spokearchitecturecomputationalneuroscience

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model that treats cognition as latent states flowing through learned energy landscapes, analogous to statistical mechanics describing thermodynamics. It extends a single-modality predictive model to vision and language by giving each modality its own diffusion-based predictive-coding pipeline and routing all pipelines through a shared amodal hub modeled on the anterior temporal lobe. The paper's central claim is that this architecture mechanistically accounts for attentional phenomena such as inattentional blindness and Necker-cube bistability, and that its mathematical structure recovers independently established psycholinguistic findings, including surprisal theory, the N400 and P600 ERP effects, and garden-path reanalysis. A reader should care because the model attempts to unify perception, attention, language, and memory under one energy-minimization principle, and it yields specific, falsifiable contrasts with transformer-based language models.

What carries the argument

The central object is the hub-and-spoke energy landscape: per-modality predictive-coding pipelines (visual objects, scene, phonemes/characters, words) converge on a shared amodal hub state, modeled on the anterior temporal lobe. The load-bearing identity is the predictive-coding energy $E_{pc} = \frac{1}{2}\epsilon_s^\top \Pi_s \epsilon_s + \frac{1}{2}\epsilon_z^\top \Pi_z \epsilon_z$, where $\epsilon_s$ is the sensory prediction error, $\epsilon_z$ is the prior/state error, and $\Pi_s, \Pi_z$ are precision matrices whose reduction implements attentional suppression. Predictions are generated by a diffusion-based energy model that stochastically anneals a candidate next state to low energy, with an energy function conditioned jointly on the local latent state and the current shared hub state. This machinery carries the argument: precision reduction gives attention and inattentional blindness, warm-started annealing over a two-well landscape gives bistability with non-memoryless dwell times, and the two error terms map onto surprisal, N400, and trajectory-reorientation costs in language.

What would settle it

Fit the model's two energy terms, summed level-1 character cross-entropy and level-2 word-latent prior mismatch, as separate predictors of N400 amplitude in existing ERP datasets: if one term alone accounts for the effect, the claimed two-level decomposition of the N400 fails.

Watch

Extended reading notes

Core claim

The discovery claimed is that the binding problem in the brain, the integration of separate sensory streams into a single experience, can be solved by a hub-and-spoke hierarchy of energy-minimizing predictive-coding pipelines. In IM-LEPP, each object, scene, and word has its own pipeline, and their latent states are integrated at a vision hub and then at a central amodal hub; that central state is fed back into each pipeline as a conditioning variable in the diffusion-based prediction energy, so every prediction reflects the full multimodal context while preserving each modality's identity. The same precision-weighting machinery that implements attention also shapes the energy landscape: reducing precision on an unattended pipeline suppresses its contribution, producing inattentional blindness, while a genuinely ambiguous stimulus creates a two-well landscape whose stochastic annealing produces Necker-cube switching. In language, the two-level pipeline makes surprisal fall out of character-level cross-entropy, maps level-1 cross-entropy onto lexical-access and level-2 prior-mismatch onto integration accounts of the N400, and explains garden-path reanalysis as an escape from a locally wrong interpretation triggered by new evidence. The paper further claims this architecture is fundamentally different from transformer models because it actively maintains a persistent latent state with momentum, whereas transformers recompute fresh from context at each step.

Load-bearing premise

The load-bearing premise is that a single shared hub state can be fed back to condition every modality's prediction without destroying modality identity, so that the brain's semantic integration can be modeled as energy minimization in hub-and-spoke latent spaces.

Editorial extensions

If this is right

If IM-LEPP is correct, attention is precision-weighting: an unattended object's pipeline is suppressed by lowering its sensory precision, which directly explains inattentional blindness and change blindness.
Necker-cube bistability follows from stochastic escape over a fixed, two-well energy landscape, predicting gamma-distributed dominance durations and longer dwells for ecologically preferred interpretations.
Surprisal emerges from character-level cross-entropy, so reading time costs are neural settling time in the lowest-level predictive-coding pipeline.
The N400 decomposes into two architecture-level terms (lexical access vs. discourse integration), and the P600 indexes garden-path escape-and-resettle; each is testable against ERP data.
Language acquisition is data-efficient because words map onto an already-grounded hub; hearing a familiar word with attention withdrawn should drive mental imagery via the same open-loop generative mechanism as vision.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the hub-and-spoke design holds, the same architecture should accommodate additional spokes (action/praxis, non-linguistic sound) without redesign, making it a candidate unified substrate for embodied semantic cognition.
The model predicts that true multimodal binding requires a single amodal hub with top-down conditioning, not just early input fusion; this suggests architecture-level targets for testing multimodal LLMs against human brain data.
The N400 decomposition implies that lesioning the hub (as in semantic dementia) should differentially affect the integration term but not the lexical-access term, a prediction that could be tested in patient ERP studies.
The warm-started annealing account of Necker switching implies that any intervention that changes the energy barrier (e.g., depth cues) should change dwell times exponentially, which existing psychophysics could reanalyze without new experiments.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Subir Varma's IM-LEPP paper is worth taking seriously, but not as a validated model. It's a clear, ambitious theoretical synthesis—hub-and-spoke ATL semantics, diffusion-based predictive coding in vision and language, discrete/categorical predictive coding, and mapping to surprisal, N400/P600, and garden-path reanalysis. The best parts are the way it situates itself against Heeger, tPC, JEPA, and the Barenholtz trajectory result, and the explicit list of experimental predictions. It also says plainly that a trained implementation is the prerequisite, which is more honest than most theory papers.

That said, the main 'recoveries' are weaker than the abstract claims. As the stress-test note says, the surprisal 'recovery' is by construction: the char-level energy is cross-entropy, so -log y_true is surprisal by definition. And the sentence that connects high energy to longer settling time—hence reading time—is asserted, not derived. In gradient descent or Langevin dynamics, convergence time is not a function of the final energy value. So the model motivates surprisal; it does not explain it. Similarly, the N400/P600 mapping is just a declaration that two energy terms are 'natural candidates' for two ERP components. No neural or dynamical mechanism is given. The inattentional blindness and Necker cube explanations are plausible stories, not derivations, though the Kramers-rate prediction for dwell times is a real, testable consequence.

The central hub assumption—that a single amodal zzz_t can condition each modality's diffusion prediction without destroying modality identity—remains unexamined, and the paper itself flags this. There is no simulation, no data, and no free parameters are fit. So the low soundness score is fair, as is the conditional verdict: the architecture is not internally incoherent, and the predictions could in principle be tested.

I disagree with any reading that dismisses this as mere hand-waving. It is a serious, well-informed proposal with a clear research program. It deserves a careful referee, not a desk reject, but also major revision: tone down the abstract, separate what is definitional from what is derived, and provide at least one worked simulation or quantitative fit before the 'recovers' language is justified. I'd engage with it and would cite the Barenholtz discussion if I worked in that area.

Referee Report

5 major / 6 minor

Summary. This paper proposes IM-LEPP, a hierarchical energy-based model of multimodal cognition that extends the author's earlier LEPP framework to integrate vision and language. The architecture is a hub-and-spoke design in which per-modality predictive-coding pipelines (visual objects and scenes, phonemes/characters and words) converge on a central amodal hub modeled on the anterior temporal lobe, with each level conditioned by, rather than overwritten by, the hub state. The paper claims that this architecture provides a mechanistic account of inattentional blindness, change blindness, and Necker-cube bistability, and that its mathematical structure recovers or motivates surprisal theory, the N400/P600 ERP components, garden-path reanalysis, and a falsifiable contrast with transformer language models. It also discusses data-efficient word learning, an outline of semantic/episodic memory, and positions the model relative to predictive coding, the free-energy principle, JEPA, and Hierarchical Temporal Memory. The manuscript contains no trained implementations or simulations; the mathematical derivations are limited to the standard predictive-coding energy, and the psycholinguistic 'recoveries' are qualitative identifications with terms of that energy.

Significance. If the central claims were established, IM-LEPP would offer a valuable unifying framework connecting energy-based predictive processing, multimodal integration, and a wide range of psycholinguistic and attentional phenomena. The paper is strong in its breadth of engagement with existing literature (e.g., Heeger, tPC, JEPA, HTM), its explicit specification of the architecture and update equations, and its concrete, falsifiable predictions in Section 9 (e.g., the diffusion-predictor residual test, the N400 decomposition, dwell-time distribution shape, and memory write-gating criteria). The authors also deserve credit for being transparent about the lack of a working implementation and for explicitly stating that trained dynamics are needed to test whether the proposed behaviors emerge rather than being read into the architecture by construction.

major comments (5)

[Section 5.4 (and Section 5.1, equation for E_char^(1))] The claim that the model's mathematical structure 'recovers' surprisal theory is circular: the character-level predictive-coding energy E_char^(1) = −Σ T_i log y_i + ½ ε_z^T Π_z ε_z reduces, at its optimum, to −log y_true, which is exactly the categorical cross-entropy (surprisal) of the observed character. This is true by construction for any softmax classifier and does not follow from the diffusion-based prediction module or from any other architectural principle unique to IM-LEPP. The paper should either reframe this as 'the model uses surprisal as its training objective by construction' or provide an independent derivation that does not presuppose the cross-entropy form.
[Section 5.4 (reading-time link)] The step that a large predictive-coding energy value makes 'the predictive coding energy pipeline take longer to settle down for each character, and thus for the entire word, which maps naturally onto longer reading times' is asserted without any derivation from the model's dynamics. In gradient descent or Langevin sampling, convergence time depends on the curvature of the loss landscape, learning rates, and the noise schedule, not on the scalar energy value at the optimum. A high-surprisal word can in principle converge as quickly as a low-surprisal word. Without an analysis of the update dynamics (or a simulation), the energy-to-processing-time link is an extra assumption, not a consequence of the architecture, and therefore cannot be claimed as a recovery of surprisal effects.
[Section 9 versus Abstract, Section 4, and Section 5.4] Section 9 explicitly states that 'a trained implementation... is a prerequisite for much of the experimental program' and that it is needed to test whether the qualitative behaviors 'actually emerge from trained dynamics rather than being read into the architecture by construction.' This directly undercuts the abstract's assertion that the architecture 'gives a mechanistic account' of inattentional blindness and Necker-cube bistability, and the similar claims in Sections 4 and 5.4. As written, those sections provide verbal narratives that map existing phenomena onto architectural components, but no simulation, quantitative derivation, or data fitting demonstrates that the proposed mechanisms produce the phenomena. The authors should either provide a proof-of-concept simulation for at least one phenomenon or systematically soften 'mechanistic account' and 'recovers' to 'is consistent with' or 'motivates' throughout.
[Section 4.1 (Necker cube)] The Necker-cube analysis invokes a double-well energy landscape, a Kramers escape rate, and a warm-start argument, but the barrier height ΔE and the noise schedule are free parameters and no quantitative predictions are derived from the model's own parameters. The claim that the model 'explains' bistable perception is therefore not supported: the non-memoryless dwell-time distribution and the asymmetry prediction are attributed to generic properties of Langevin dynamics in a double well, not to anything specific to IM-LEPP. To substantiate the mechanistic claim, the authors would need to either simulate the proposed diffusion process and fit its dwell-time statistics or provide a formal connection from the model's energy functions and precision parameters to the observed gamma-like distributions.
[Section 5.4 (N400/P600 mapping)] The identification of the level-1 summed cross-entropy term with the lexical-access side of the N400 and the level-2 prior-mismatch term with the integration side, with P600 as the signature of 'escape-and-resettle,' is a post-hoc assignment of two energy terms to two ERP components. No functional model is given for how energy values translate into EEG amplitudes or latencies, and no quantitative comparison with ERP data is reported. This is a plausible interpretive suggestion, but it does not constitute a recovery of the N400/P600 effects from the architecture; the authors should present it as a hypothesis to be tested rather than as a result of the model.

minor comments (6)

[Abstract] The abstract ends with 'to test its central claims Key Words:...' which appears to be a formatting error; the key-words header should be separated from the preceding sentence with proper punctuation.
[Section 2] There is a duplicated word in 'on the the ATL area of the brain' (under the description of Ralph et al.'s model).
[Throughout] The article repeatedly uses 'an hierarchical,' 'an latent,' 'an energy based,' and similar constructions where 'a' is the correct article; a careful proofread for articles and hyphenation is needed.
[Section 5.5] In the sentence 'maps onto the predictive-coding energy in the IM-LEPP model by using the energy equation,' the phrase 'the energy equation' is preceded by a duplicated 'the' in the original text; also the equation number itself is not cited, which makes it hard for the reader to locate.
[Notation, Sections 3-5] The notation z_t, zz_t, zzz_t, xx_t, yy_m, and E_p/E_char is heavily overloaded and the subscripts are inconsistently typeset (e.g., u_t^(t+1) = zzz_t). A notation table or a figure with a clear legend would greatly improve readability.
[References] The LEPP model (reference [70], 'Varma, 2026') is a self-published blog-style source; since the entire paper builds on this model, the authors should either provide an archival reference (arXiv/DOI) or include a self-contained summary of the LEPP energy and diffusion dynamics in an appendix, so that the reader can verify the core assumptions.

Circularity Check

1 steps flagged · score 6.0 of 10

The 'recovery' of surprisal in §5.4 is definitional: the character-level energy is cross-entropy by construction, and the energy-to-settling-time link needed for reading-time predictions is asserted, not derived.

self definitional [Section 5.4, Surprisal Theory paragraph, using the emission energy defined in Section 5.1]
"Assuming that the ground truth for the i-th character is given in 1-hot form by t=(t_1,…,t_K), the previous section showed that the predictive coding pipeline for the i-th character is driven by the minimization of the energy term −Σ_j t_j log y_j which reduces to the definition of surprisal −log y_true."

In §5.1 the energy E_char^(1) = −Σ_i T_i log y_i + ½ ε_z^T Π_z ε_z is the model's chosen emission loss, with y = softmax(z^(1)). The first term is exactly the standard categorical cross-entropy (negative log-likelihood) used to train the classifier. Surprisal theory is then 'recovered' by observing that word surprisal decomposes via the chain rule into character-level negative log-likelihoods, i.e., into the same cross-entropy term already built into the energy. That is a restatement of the training objective, not a derived consequence of the architecture.

full rationale

The paper is a conceptual architecture proposal with no fitted parameters or trained implementation, so the main circularity risk is not statistical forcing but definitional reduction. The clearest instance is the advertised recovery of surprisal theory in §5.4: the character-level predictive-coding energy introduced in §5.1 is cross-entropy by construction, and the paper's chain-rule decomposition simply rewrites -log p(word|context) as a sum of character-level -log y_true terms. The model therefore 'predicts' surprisal because surprisal was put into the loss. The N400/P600 and garden-path discussions are mappings or 'natural candidate' assignments rather than derivations; they are unsupported or analogical, but not circular. The paper's own §9 caveat that trained dynamics may be 'read into the architecture by construction' shows awareness of the broader risk, but does not undo the specific definitional surprisal step. Self-citations to the earlier LEPP paper supply the diffusion/energy machinery as prior architectural premises; they are load-bearing but not in the same way as the loss-identity step, since the psycholinguistic 'recoveries' are not derived from the earlier paper's equations. Overall, one centrally advertised 'recovery' reduces by construction to the model's loss function, while the rest of the paper retains independent (if speculative) architectural content, giving a partial circularity score of 6.

Assumptions & free parameters 4 free parameters · 7 assumptions · 3 invented entities

The central claim rests on several unproven architectural postulates: the hub-and-spoke framework, the portability of the author's diffusion prediction module, and the identity-preserving conditioning of spoke predictions by the hub. The only mathematical results are standard predictive coding updates and a qualitative Kramers rate. Free parameters such as precision matrices and thresholds are unspecified. Invented entities are the latent hub states and an artificial amygdala, none with independent falsifiable handles.

free parameters (4)

Precision matrices Π_s and Π_z in predictive coding energy = None specified
Used in E_pc = 1/2 ε_s^T Π_s ε_s + 1/2 ε_z^T Π_z ε_z (sections 4 and 5.5); weights sensory vs prior error and would need to be set for any implementation; attention is modeled by reducing Π_s in the billboard case.
Word-boundary detection threshold = None specified
In section 5, the difference between predicted and actual character-level latent states exceeding a threshold signals the end of a word; the threshold value is unspecified and would be tuned.
Diffusion noise schedule and annealing parameters for E_p = None specified
The LEPP diffusion module is assumed from the author's previous paper and applied to both visual and word latents; concrete parameter values for the Langevin sampling process are not given.
Energy barrier height ΔE for Necker cube switching = None specified
The Kramers escape rate in section 4.1 is invoked qualitatively; no quantitative barrier height, noise amplitude, or temperature is provided.

assumptions (7)

standard math Bayesian predictive coding update with gradient descent on cross-entropy plus prior energy (section 5.1)
Standard predictive coding and variational inference derivation, cited to Whittington and Bogacz, Rao and Ballard.
standard math Kramers escape rate formula for bistable switching, rate ∝ exp(-ΔE/kT) (section 4.1)
Used as the mechanism for Necker cube switches under a fixed energy landscape.
standard math Divisive normalization can approximate softmax (Heeger, 2017) (section 5.3)
Bridges discrete categorical readout to neural circuitry; the power-law form differs from the exponential softmax but is treated as qualitatively equivalent.
domain assumption Hub-and-spoke semantic cognition framework of Lambon Ralph et al. 2017, with an amodal ATL hub integrating modality-specific spokes (section 2)
The entire architecture is grounded in this neuropsychological framework; the paper notes the single-hub versus distributed-hub debate but still uses converging hubs.
domain assumption Object file theory and per-object visual pipelines, with one predictive coding pipeline per attended object (section 4)
Used to justify level 3 object pipelines; no computational derivation is provided.
ad hoc to paper The diffusion-based temporal prediction module from the author's prior LEPP paper can be transplanted to word-level and hub-conditioned prediction (sections 3 and 5)
The energy function E_p(x; z_t, u_{t+1}=zzz_t) is assumed from Varma (2026) without re-derivation or empirical validation in this paper.
ad hoc to paper A central hub state can condition every spoke's prediction while preserving spoke identity (sections 3, Figures 3, 6, 7)
Core architectural postulate, described verbally; no formal analysis or simulation shows identity preservation or asynchronous multirate stability.

invented entities (3)

Central amodal hub state zzz_t
purpose: Shared multimodal latent representation modeled on the ATL, fed back to condition each modality's prediction energy.
It is the paper's own construct; no falsifiable handle outside the model is provided beyond the proposed experimental program.
Level 2 vision hub and language hub states zz_t
purpose: Integration of object and scene pipelines for vision, and word pipelines for language.
Architectural constructs; the paper maps them to PPA, LOC, pMTG and STG regions qualitatively but provides no neural data tying states to recordings.
Artificial amygdala
purpose: Imagined extension to create an emotion state by tracking prediction errors and feeding valence back into the hub (section 5).
Explicitly proposed as something 'one can imagine', with no mechanism or implementation specified.

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Cite this review

Pith. "Pith review of A Hierarchical Energy-Based Model for Multimodal Cognition." pith.science (2026). https://pith.science/paper/EFEMMCFL

@misc{pith2026260812398,
  author       = {Pith},
  title        = {Pith review of: A Hierarchical Energy-Based Model for Multimodal Cognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EFEMMCFL}},
  note         = {Machine review of arXiv:2608.12398}
}

read the original abstract

We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that extends a previously proposed single-modality model (LEPP) to integrate vision and language. Following the view that generative neural networks are effective theories of cognitive dynamics, analogous to how statistical mechanics relates to thermodynamics, IM-LEPP models cognition as latent states flowing through learned energy landscapes rather than as an account of neural circuitry. The architecture is a hub-and-spoke hierarchy, grounded in the controlled semantic cognition framework of Lambon Ralph et al., in which predictive-coding pipelines for visual objects, scenes, and linguistic units converge on a shared amodal hub modeled on the anterior temporal lobe. Each pipeline's own prediction is conditioned by, rather than overwritten by, the current hub state, preserving pipeline-specific identity while letting every prediction reflect the full multimodal context. We show this architecture gives a mechanistic account of attentional phenomena such as inattentional blindness and Necker-cube bistability, and that its structure recovers or motivates independently established findings in psycholinguistics, including surprisal theory, the N400/P600 ERP components, and garden-path reanalysis, alongside a falsifiable contrast with transformer language models on trajectory-sensitivity in next-word prediction. We also discuss data-efficient language acquisition relative to LLMs, outline a semantic/episodic memory subsystem, situate the model against predictive coding, the free-energy principle, JEPA, and Hierarchical Temporal Memory, and propose concrete experimental predictions to test its central claims Key Words: predictive processing; predictive coding; energy-based models; diffusion models; effective theory; computational neuroscience.

Figures

Figures reproduced from arXiv: 2608.12398 by the authors.

**Figure 1.** The hub and spoke model for cognition. The hub and spoke model for cognition was proposed by Ralph et al. in 2017, and it was based on key findings from a decade of research into neurocognitive and neurocomputational underpinnings of the brain’s semantic cognition abilities. Based on this data, they proposed the hub and spoke model of semantic representation that is shown in the above figure, which was based on the … view at source ↗

**Figure 2.** This figure illustrates graded pattern of semantic representation at the central [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** The IM-LEPP model We propose a model for integration of visual and language modalities based on the hub and spoke model, which we call integrated multimodal latent energy-based predictive processing or IM-LEPP (see above figure). This model extends the LEPP model that was described in the previous paper in several ways: It proposes a hierarchical spatial integration structure for the vision model, it introduces a hi… view at source ↗

Figures from the paper (11 more)

**Figure 4.** Figure 4: Hub and Spoke Model for Vision: Integration of Predictive Processing Pipelines [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Predictive Processing Pipeline for an Individual Object (or Scene) at Level 3 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: Integration of Multiple Object and Scene Level Predictive Processing Pipelines [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 7.** Figure 7: Integration of Vision and Language Hubs into the Cognition Level Hub at Level 1 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Energy Landscape for the Necker Cube A useful test case for the level 3 energy landscape used for predictions, is a genuinely ambiguous stimulus such as the Necker cube, whose line drawing is equally consistent with two distinct three-dimensional interpretations. Let 𝑥…

**Figure 9.** Figure 9: Classification of a character image into one of K categories [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: Predictive Processing Pipeline for Next Character Prediction [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]

**Figure 11.** Figure 11: Predictive Processing Pipeline for Predicting the Latent Representation for the [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]

**Figure 12.** Figure 12: Integration of vision and language modules at the central ATL hub [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]

**Figure 13.** Figure 13: Predictive Coding Pipeline for Characters [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]

**Figure 14.** Figure 14: Incorporation of Episodic Memory into the IM [PITH_FULL_IMAGE:figures/full_fig_p036_14.png]

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work page arXiv 1992