Neural Language of Thought Models

Updated 9 December 2025

Neural Language of Thought Models are computational frameworks that embed compositional, language-like representations in neural architectures following the Language of Thought hypothesis.
They leverage techniques like vector binding, discrete codebooks, and autoregressive priors to mimic symbolic reasoning and hierarchical structure.
Empirical evaluations on synthetic datasets highlight superior compositionality and generalization, though challenges remain for real-world application and systematicity.

Neural Language of Thought Models (NLoTM) are a class of computational models that operationalize the Language of Thought Hypothesis (LoTH) within neural network architectures. NLoTMs are designed to represent and manipulate compositional, language-like structures in a manner compatible with cognitive science, computational neuroscience, and machine learning, unifying insights from symbolic reasoning, probabilistic programming, and neural representation learning.

1. Theoretical Foundations and Historical Context

The foundational premise of NLoTMs is the LoTH, originally articulated by Fodor (1975), which posits that human thought is structured as a mental “language” composed of discrete symbols combined by syntactic rules. In its classical form, the LoTH assumes digital, symbolic representations manipulated by rule-based computations akin to digital computers. However, empirical evidence has increasingly favored a Nonclassical LoTH: neural representations that are continuous, high-dimensional, and grounded in biological substrates, yet mirror some hierarchical and compositional traits observed in natural language and cognition (Piccinini, 11 Oct 2025).

NLoTMs seek to instantiate these language-like thought structures in neural architectures, thereby bridging the gap between abstract symbolic/cognitive theories and realizable neural network computation. This research direction has been further motivated by the limitations of classical symbolic models and the emerging successes—and theoretical limitations—of deep learning architectures in capturing human-like compositionality (Klein, 26 Aug 2025).

2. Architectural Principles and Computational Mechanisms

2.1 Nonclassical LOT Realization

Nonclassical NLoTMs encode compositional, language-like representations not as digital symbol sequences but as patterns of continuous neural activity, trajectories, or attractor states in a high-dimensional state space. Combination and manipulation of representations rely on non-discrete operations such as vector concatenation, matrix binding, and recurrent dynamics, illustrated as:

$v_A = f_R\bigl(W_R [v_B; v_C] + b_R\bigr)$

where $v_B, v_C \in \mathbb{R}^d$ represent constituent vectors, $W_R \in \mathbb{R}^{d \times 2d}$ parameterizes the binding operation for rule type $R$ , $b_R$ is a bias, and $f_R$ a nonlinear activation (Piccinini, 11 Oct 2025). Such mechanisms allow for hierarchical and recursive composition without explicit symbolic pointers or digital rule application.

2.2 Semantic Vector Quantization and Discrete Factor Coding

To align neural representations with high-level symbolic factors (objects, properties), NLoTMs introduce discrete, learnable codebooks within a neural encoder-decoder framework. For instance, the Semantic Vector-Quantized Variational Autoencoder (SVQ-VAE) (Wu et al., 2 Feb 2024) partitions object-centric slot embeddings into blocks, each quantized against an independent codebook:

Each slot $s_n \in \mathbb{R}^{d_s}$ is split into $M$ blocks $z_e^{n,m} \in \mathbb{R}^{d_c}$ .
Codebooks $C_m \in \mathbb{R}^{K \times d_c}$ specialize to different semantic factors.
Quantization assigns $z_q^{n,m} = C_{m,k^*}$ with $k^* = \arg\min_j \|z_e^{n,m} - C_{m,j}\|_2$ .

These discrete codes serve as the neural substrate for symbol-like abstractions, enabling compositional scene representation and systematic generation.

2.3 Autoregressive Compositional Priors

Trained discrete latent codes are modeled by an autoregressive transformer, the Autoregressive LoT Prior (ALP), which learns a generative sequence model over code sequences. The prior is optimized to maximize:

$L_\mathrm{prior} = -\sum_{t=1}^{N M} \log p(z_q^{(t)} | z_q^{(<t)})$

This enables synthesis of novel, coherent scenes or concepts by sequentially sampling factors, mirroring the productivity of compositional language (Wu et al., 2 Feb 2024).

3. Empirical Evidence, Model Evaluation, and Limitations

3.1 Empirical Support for NLoTMs

Empirical studies have shown that NLoTMs excel in structured perception, downstream reasoning, and out-of-distribution generalization:

On 2D Sprites and CLEVR datasets, NLoTMs achieve superior FID (Fréchet Inception Distance) and generation accuracy over VQ-VAE, dVAE, and continuous baselines, reflecting better semantic compositionality.
In odd-one-out and property comparison tasks, block-level discrete factorization dramatically improves zero-shot and OOD generalization (ID/OOD accuracy of 75.9/71.2% for CLEVR-Hard), while preserving object segmentation quality (Wu et al., 2 Feb 2024).

3.2 Limitations and Open Challenges

Current NLoTM implementations primarily address synthetic or semi-synthetic visual domains; performance on rich, real-world data is a subject for future investigation.
All factors are discretized; truly continuous attributes (pose, lighting) must await hybrid approaches.
Systematicity—generalizing to all possible compositions—is not guaranteed and interpretability of distributed codes remains limited (Piccinini, 11 Oct 2025).

4. Contrasts with Transformer-Based and Classical Symbolic Models

Recent analysis demonstrates that transformers, while successful at language modeling, are limited to “linear formats” that cannot represent the supralinear, hierarchical structures necessary for genuine language-of-thought computation (Klein, 26 Aug 2025). Specifically:

Permutation invariance: The transformer residual stream is invariant under permutation of input sequence positions, formalized as $T(P_\pi X) = P_\pi T(X)$ for permutation matrices $P_\pi$ .
Substring invariance: Masked transformers satisfy $T(X[n]) \approx T(X)[n]$ , precluding retroactive structure binding in sequence contexts.

Transformers can efficiently emulate the state-string function of finite-state automata via shortcut computation, but cannot operate on tree-like or graph-like representations essential for conceptual, language-like thought. Thus, such architectures are ill-suited as NLoTM substrates in the strict cognitive sense (Klein, 26 Aug 2025).

5. Hybrid Neural-Symbolic and Probabilistic Language of Thought Models

NLoTMs have been extended into hybrid frameworks combining neural meaning functions and symbolic probabilistic inference. In “From Word Models to World Models,” natural language utterances are translated by an LLM into code programs in a Probabilistic Language of Thought (PLoT), e.g., Church (Wong et al., 2023).

Neural networks provide wide-coverage, amortized mappings from utterances $x$ to programs $z$ :

$p(z|x) \approx \text{Codex.prompt}(x;\text{PLoT template})$

Executable PLoT programs enable principled Bayesian inference over structured, generative world models.
Integration of modular cognitive engines (graphics, physics, planning) within PLoT enables NLoTM reasoning across domains, from vision and physical dynamics to social intent inference.

A typical workflow involves translation of language to code, assembly of context-specific generative models, and inference for human-like reasoning over hypothetical scenarios (Wong et al., 2023).

6. Directions for Model Design and Future Research

Key architectural principles and open problems for NLoTMs include:

Continuous codes and compositional binding: Use of graded, noise-robust representations with learned, structure-sensitive combination operations (Piccinini, 11 Oct 2025).
Object-centric disentanglement: Decompose representations into object/factor-specific codes, each aligned with semantic properties (Wu et al., 2 Feb 2024).
Integration with probabilistic and symbolic reasoning: Employ neural encoders for semantic abstraction and symbolic backends for systematic inference and compositional planning (Wong et al., 2023).
Biological plausibility: Avoid discrete instruction pointers and rigid digital memory; instead, coordinate processing and memory in recurrent, locally plastic neural substrates, as observed in brain circuits (Piccinini, 11 Oct 2025).
Hybrid discrete-continuous models and scaling: Expand from synthetic settings to naturalistic data; combine symbolic programs with continuous factor codes for broader, more expressive scene understanding (Wu et al., 2 Feb 2024).

A plausible implication is that future NLoTMs will incorporate increasingly sophisticated neural-symbolic interfaces, richer structural priors, and biologically informed learning rules to close the gap between artificial systems and natural cognitive intelligence.

Table: Key Model Components in Leading NLoTMs

Component	Role	Representative Paper
SVQ-VAE (block quantization)	Factorizes object-centric slots into semantic codes	(Wu et al., 2 Feb 2024)
Autoregressive LoT Prior (ALP)	Compositional generative model over codes	(Wu et al., 2 Feb 2024)
Neural-to-PLoT Translator	Maps language to symbolic programs for inference	(Wong et al., 2023)
Continuous compositional binding	Realizes hierarchical structures non-digitally	(Piccinini, 11 Oct 2025)

This summary reflects an overview of recent advances and evidential assessments of NLoTM architectures and their place within the broader landscape of computational models of cognition.