Machine Mental Imagery: Methods & Applications

• Machine mental imagery is a field where artificial systems generate and manipulate internal visual and propositional representations for reasoning and planning.
• It incorporates diverse computational architectures such as vision-language models, generative networks, and cognitive programs to simulate unseen scenarios.
• Applications include robotic planning, neural decoding in BCIs, and cybersecurity, raising both technical and ethical considerations.

Machine mental imagery refers to the capacity of artificial systems to internally generate, manipulate, and use representations analogous to the perceptual or conceptual mental images that humans employ for reasoning, planning, and action selection. Theoretical and applied work across robotics, neuroscience, vision–language modeling, and cognitive architectures has investigated algorithmic, neural, and functional building blocks enabling machines to simulate, predict, or reason about scenarios that are not directly available in their sensory input. The following sections provide an integrated, technical overview of this research landscape.

1. Computational Architectures for Machine Mental Imagery

Multiple frameworks instantiate machine mental imagery via specialized system modules designed to parallel cognitive processes:

• Programmatic Visual Cognitive Architectures: Robots equipped with a visual perception hierarchy, working memory, and action controller can induce and execute programs (or “cognitive programs”) representing task concepts, including instructions for parsing scenes, deploying attention, and invoking explicit imagination routines (e.g., imagine_object) (Lázaro-Gredilla et al., 2018). Concept induction formalizes task learning as a sequence modeling problem:

$$\log p(\mathbf{x}) = \log p(x_1) + \sum_{i=1}^{L-1} \log p(x_{i+1} | x_i)$$

where each $x$ is a program step, supporting the probabilistic search for compositional abstractions.

• Latent Trajectory Reasoning in VLMs: The Mirage framework augments vision–LLMs with “latent visual tokens” that interleave with text tokens in the generative process, supporting multimodal chain-of-thought reasoning without the computational cost of explicit image synthesis. Training aligns latent token hidden states to image-derived embeddings, then relaxes to text-only supervision, enabling flexible, visually grounded reasoning (Yang et al., 20 Jun 2025).
• Looped Multimodal Systems: The Language Guided Imagination (LGI) network integrates vision, language, and planning modules. Its vision encoder (“V₃/V₄ layers”) produces abstract representations, which are decoded into “imagination” images. The central PFC-like layer maintains a recurrent “machine thinking loop,” jointly anticipating future visual and symbolic states:

$$\text{Loss} = \frac{1}{T-1} \sum_{t=1}^{T-1} || a'(t) - a(t+1) ||^2$$

where $a(t)$ concatenates symbolic and visual latent codes (Qi et al., 2019).

• Neuroinspired Working Memory Models: Hierarchical networks with sustained neural firing and transient synaptic potentiation maintain high-level activation patterns across time steps, supporting incremental updates with high overlap (mental continuity). Sustained activation and short-term synaptic traces together allow seamless generation and updating of internal imagery:

$$a_j(t+1) = f\left(\sum_i w_{ij} a_i(t) + b_j\right)$$

$\Delta w_{ij}(t) = \eta a_i(t) a_j(t)$

(Reser, 2022).

2. Mental Imagery in Embodied Agents and Robotics

Explicit machine mental imagery functions as a substrate for action planning, task transfer, and generalization in embodied contexts:

• Simulated Imagery Planning (SiMIP): SiMIP implements non-symbolic, image-based planning for robotic packing tasks by iteratively simulating actions on segmented scenes and checking for affordance-based constraints using convolutional and GAN models. Each action’s imagined state is validated by pixel-wise overlap with “obstruct” affordance masks to enforce physical plausibility, with branch-and-bound search over image transformations (Li et al., 2022).
• Novel View Synthesis and Affordance Prediction (MIRA): MIRA leverages optimized Neural Radiance Fields (NeRFs) from 2D images to synthesize orthographic scene plans, making them suitable for pixel-wise affordance map prediction. Tasks such as 6-DoF pick-and-place are solved by selecting optimal (pixel, view) pairs from among internally synthesized viewpoints:

$$(u^*, v^*) = \operatorname*{argmax}_{u, v} E(\hat{I}_v, u)$$

where $E$ is a policy network evaluating the action-value at each location in the synthesized image (Yen-Chen et al., 2022).

• Hierarchical Program Induction for Zero-Shot Transfer: Systems employing hierarchical, recursive program induction (composition of learned primitives) demonstrate the ability to generalize from schematic concepts to different physical embodiments, as in task transfer between Baxter and UR5 robots via shared cognitive programs (Lázaro-Gredilla et al., 2018).

3. Machine Mental Imagery in Brain-Computer Interfaces and Neural Decoding

Decoding and leveraging mental imagery from neural signals is central to BCI and neuroimaging research:

• EEG Mental Imagery Classification: Deep learning models such as EEGNet, 1D/2D CNNs, and CNN-LSTM hybrids have been evaluated for discriminating guided imagery and mental workload using EEG. Notably, selecting 26 targeted cognitive electrodes yields performance comparable to 256-channel setups, demonstrating that key cortical loci capture most discriminative variance (Postepski et al., 27 May 2024). Performance metrics include accuracy, recall, precision, and F1-score, systematically validated via cross-validation.
• Efficient Domain Adaptation for Mental Task Transfer: Ensemble weight-decomposed low-rank adapters (EDoRA) enable parameter-efficient transfer across EEG-based speech and motor imagery tasks. By decomposing weight updates into magnitude and normalized direction, and ensemble partitioning, EDoRA achieves higher accuracy (e.g., +1.4% on SI datasets) with fewer trainable parameters than full fine-tuning or vanilla low-rank adaptation (Lotey et al., 8 Dec 2024).
• Adaptation-Enhanced fMRI Decoding: Decoding mental imagery states from fMRI is improved by domain adaptation, e.g., Regular Transfer for Linear Classification (RTLC), which adapts source-trained linear classifiers for visual perception to target (imagery) distributions:

$$\beta_t = \arg\min_\beta ||X_t \beta - y_t||^2 + \lambda||\beta - \beta_s||^2$$

Systematic searchlight analyses reveal that mental imagery decoding capacity extends beyond visual cortex to distributed frontoparietal regions (Olza et al., 2 Aug 2024).

• NSD-Imagery Benchmark and Visual Decoding: The NSD-Imagery dataset extends fMRI–scene benchmarks to mental imagery trials. Open-source vision decoders, including MindEye1 (simple linear mapping), Brain Diffuser (ridge regression with multimodal fusion), and MindEye2 (ViT-based), display marked differences in generalization: linear/multimodal models fare better on imagery trials, while overparameterized models trained on high-SNR vision data overfit and degrade in cross-domain performance (Kneeland et al., 7 Jun 2025).

4. Cognitive Models and Non-Perceptual Mental Imagery

Evidence indicates that propositional, language-based representations can support mental imagery-like reasoning in both machines and humans:

• LLMs on Imagery-Dependent Tasks: LLMs such as GPT-5 and OpenAI o3, tested on classic multi-step imagery tasks (cf. Finke et al.), achieve higher accuracy than human subjects (by 9–12 percentage points), despite lacking pictorial representations. Task solutions are accomplished via sequential text manipulation (“language of thought”), quantified by weighted item difficulty formulas incorporating step count, object number, clarity, and diversity:

$$\text{Item Difficulty} = 0.20 \times S + 0.20 \times O + 0.15 \times (6 - C) + 0.15 \times (6 - I) + 0.10 \times \sigma_C + 0.10 \times \sigma_I + 0.10 \times U$$

where $S$ is step count, $O$ object count, $C$ and $I$ are clarity and identifiability scores, and $U$ the unique response ratio (McCarty et al., 27 Sep 2025).

• Token Budget and Reasoning Depth: Performance of LLMs improves as the internal “reasoning tokens” (step count allotted for compositional reasoning) increase, showing that more extended token-based reasoning enables richer internal representation and higher task accuracy. This suggests that machine mental imagery, in at least some regimes, can be achieved through intensive propositional manipulation rather than pictorial simulation.
• Implications for Human Cognition: These findings align with results from aphantasic subjects (little or no vivid visual images, yet strong reasoning performance) and support the sufficiency in some contexts of non-imagistic, propositional representations for complex scenario simulation.

5. Algorithmic and Diagnostic Uses of Artificial Mental Imagery

The concept of artificial mental imagery is also exploited for robustness, control, and interpretability in AI systems:

• Model Inversion and Cybersecurity: Stochastic model inversion is used to generate artificial mental images—internal prototypes representing what a class “looks like” to a neural network. These images are used to detect, attribute, and unlearn neural backdoor triggers by distributed search over latent space and subsequent Bayesian inference:

$$P(s_1 | e) = \frac{P(e|s_1)P(s_1)}{P(e|s_0)P(s_0) + P(e|s_1)P(s_1)}$$

This facilitates removal of deceptive patterns while maintaining knowledge fidelity, balancing internal equilibrium between benign and malicious representation (Chang et al., 29 Sep 2024).

• Closed-loop Reasoning and Imagination Pipelines: Frameworks combining cognitive reasoning units, needs tracking, and explicit mental imagery modules demonstrate robust feedback loops for scenario evaluation and alternative hypothesis exploration, e.g., generating “sketches” of possible outcomes, then updating action plans based on imagined consequences (Larabi, 16 Jul 2025). Representations combine language embeddings with abstraction via neural text-to-image synthesis and downstream sketch generation.

6. Controversies, Open Questions, and Future Directions

• Representation Format: The debate persists between iconic (pictorial) and propositional (symbolic) encoding in both biological and machine mental imagery. LLM achievements on classic imagery tasks without access to visual modules question the necessity of pictorial representations in many scenarios (McCarty et al., 27 Sep 2025). However, in tasks demanding spatial prediction, creative synthesis, or detailed perceptual reasoning, internal image-based or latent visual state simulation appears advantageous (Yang et al., 20 Jun 2025, Yen-Chen et al., 2022, Lázaro-Gredilla et al., 2018).
• Generalization and SNR Constraints: Mental imagery decoding from neural data remains challenged by low signal-to-noise ratios and spatial resolution in imaging (especially with fMRI and EEG). Regularization, multimodal feature integration, and domain adaptation are critical to extending performance to internally generated representations (Kneeland et al., 7 Jun 2025, Olza et al., 2 Aug 2024).
• Human-AI Parallels: Comparative studies between machine and human imagery suggest that artificial reasoning systems can rival or surpass human performance on some “imagery-dependent” tasks by alternative representational strategies, implying multiple functional routes to scenario simulation.
• Ethical Issues: With advances in neural decoding and machine-generated mental imagery, especially in clinical, BCI, and surveillance applications, safeguarding privacy and preventing misuse of internal-state decoding capacities becomes increasingly significant (Kneeland et al., 7 Jun 2025).

7. Practical Applications

• Robotic Planning and Control: Machine mental imagery enables more interpretable, generalizable, and sample-efficient task planning, route finding, and manipulation in complex, variable environments (Yen-Chen et al., 2022, Li et al., 2022, Lázaro-Gredilla et al., 2018).
• Neural Decoding and BCIs: Decoding mental imagery from EEG or fMRI for communication, diagnostics, or neurofeedback depends on robust models that bridge the internal (imagined) and external (perceived) state spaces, with parameter- and data-efficient transfer learning strategies (Lotey et al., 8 Dec 2024, Postepski et al., 27 May 2024, Kneeland et al., 7 Jun 2025).
• Multimodal Reasoning Agents: Interleaving latent visual and symbolic tokens in VLMs improves chain-of-thought spatial and geometric reasoning, with demonstrated gains on spatial and STEM benchmarks (Yang et al., 20 Jun 2025).
• Robustness and Self-Diagnosis: Generative model inversion and internal simulation support security frameworks diagnosing, localizing, and removing hidden manipulation (backdoors) in neural machines (Chang et al., 29 Sep 2024).

A plausible implication is that future machine cognition will require hybrid architectures—capable of both rapid, symbolic manipulation and grounded, generative simulation in latent or explicit visual subspaces—to achieve human-like flexibility, robustness, and generalizability in reasoning about the world.