Human-like Conceptual Representations

Updated 4 November 2025

Human-like conceptual representations are multidimensional internal structures that encode, organize, and deploy concepts for generalization, categorization, and inference.
They integrate sensory, linguistic, and abstract features through methods like sparse embeddings and spiking neural networks for robust, interpretable performance.
Validation via behavioral tasks, neuroimaging analyses, and abstraction graphs confirms their alignment with human cognitive and perceptual processes.

Human-like conceptual representations are the internal, relational, and often multidimensional structures by which intelligent systems—including humans and advanced AI—encode, organize, and flexibly deploy concepts to support generalization, abstraction, categorization, and inference. These structures are empirically characterized by their alignment with human psychological phenomena, their predictive power for human behavioral and neural data, and their capacity to ground domain-general reasoning across perceptual, linguistic, and social contexts.

1. Foundations and Theoretical Principles

Human-like conceptual representations are defined by both their content and structure. In cognitive science and computational neuroscience, concepts are not merely static definitions, but multidimensional entities embedded in a relational space. Such structures are characterized by:

Organizational Core: Human conceptual spaces exhibit robustness, coherence across methods and cultures, and low-dimensional, sparse, interpretable axes corresponding to psychological features such as taxonomic, functional, or perceptual attributes (Zheng et al., 2019, Suresh et al., 2023).
Relational Geometry: Concepts are encoded as points in a high-dimensional space, with similarity and distance reflecting graded, context-sensitive relations, supporting generalization, analogy, and categorization (Nenadović et al., 2019, Du et al., 1 Jul 2024).
Multimodal Integration: Human-like representations integrate sensory-derived (embodied) information with linguistic, symbolic, and abstract features, typically coordinated via semantic control systems inspired by neurocognitive findings (Wang et al., 12 Jan 2024, Chang, 2022).
Plasticity and Experience-Dependence: Conceptual representations adapt to individual experiences, sensorimotor impairments, and socio-cultural context, exhibiting both universal cores and domain- or agent-specific reorganization (Bao et al., 10 Mar 2024, Monaco et al., 2018).

These principles are operationalized by various computational models, including sparse non-negative embeddings, spiking neural networks simulating semantic control, dual embodied-symbolic models, and explicitly structured graphs for abstraction and inference.

2. Experimental Methodologies and Modeling Frameworks

A wide array of methodologies are employed to probe, quantify, and model human-like conceptual representations:

A. Behavioral and Psychometric Paradigms

Odd-one-out triplet tasks: Capture the underlying similarity structure for large concept sets, with embeddings inferred via SPoSE and ordinal embedding (Zheng et al., 2019, Du et al., 1 Jul 2024, Studdiford et al., 1 Oct 2025).
Feature-listing, pairwise similarity, triadic comparisons: Used to assess structural coherence, domain generality, and inter-method stability (Suresh et al., 2023).
Reverse dictionary and in-context derivation: Exploits LLMs' internal representations elicited from language-based tasks to produce human-like concept spaces (Xu et al., 21 Jan 2025).

B. Neuroimaging and Neural Alignment

fMRI Representational Similarity Analysis (RSA): Quantifies correspondence between embedding-derived similarity matrices and neural response similarity in category-selective cortical areas (e.g., FFA, PPA, EBA, RSC), providing neural plausibility criteria (Du et al., 1 Jul 2024, Xu et al., 21 Jan 2025).
Decoding and encoding models: Link activation patterns to concept representations; analyses of clustering/structuring factors in cortical representations illuminate the fluidity of conceptual grouping (Hendrikx et al., 2020).

C. Probing and Abstraction Approaches

Abstraction graphs: Formal encoding of conceptual hierarchy as DAGs; abstraction alignment metrics assess the extent to which a model’s uncertainty or errors are explained by human abstraction structures (Boggust et al., 17 Jul 2024).
Layer-wise probing in neural models: Concept vector and linear classifiers identify at which network levels human concepts are preserved or degraded—critical for understanding interpretability–performance tradeoffs (Lomaso et al., 29 Oct 2025).

3. Empirical Signatures and Mechanisms

A. Sparsity, Dimensionality, and Interpretability

Human conceptual spaces are low-dimensional and sparse, typically comprising interpretable axes (taxonomic, functional, perceptual) (Zheng et al., 2019, Du et al., 1 Jul 2024).
In models, sparsity is achieved via L1 regularization and non-negativity constraints, supporting direct interpretability and graded feature activation (Zheng et al., 2019).

B. Embodiment vs. Symbolic and Multimodal Fusion

Human-like representations emerge from integration of embodied (sensorimotor) and symbolic (linguistic, relational) modalities (Wang et al., 12 Jan 2024, Chang, 2022). Models based on spiking neural networks fuse multimodal input via spike-based coding with semantic control (Wang et al., 12 Jan 2024).
Multimodal LLMs (trained on both images and text) develop representations with closer alignment to human judgments than unimodal models, capturing visual, perceptual, and abstract semantic information (Du et al., 1 Jul 2024, Xu et al., 2023).
Dual coding and hybrid architectures support rapid generalization, improved robustness, and human-like sample efficiency.

C. Abstraction, Hierarchy, and Generalization

Human concepts are organized hierarchically: abstraction graphs (e.g., WordNet, ICD-9) enable measurement of whether model behavior is locally or globally aligned to human conceptual relationships (Boggust et al., 17 Jul 2024).
Geometric unification of multiple levels—objects and relations—via unsupervised alignment supports analogical, relational, and cross-domain generalization (Nenadović et al., 2019).

D. Plasticity, Individual Differences, and Social Construction

Conceptual representations adapt to new information and social interaction: preschool children’s mental models shift from anthropocentric to more machine-like after exposure and dialogue (Monaco et al., 2018).
Variations among individuals (e.g., blind vs. sighted) reveal domain- and experience-specific reorganizations in both the reliance on, and structure of, conceptual features (Bao et al., 10 Mar 2024).

4. Model–Human Alignment and Benchmarking

A. Alignment Metrics

Representational Similarity Analysis (RSA): Quantifies the correspondence between model and human similarity/dissimilarity matrices (Iaia et al., 21 May 2025, Du et al., 1 Jul 2024).
Procrustes $R^2$ : Assesses the variance in human spaces explained by (rotated/scaled) model embeddings; high $R^2$ indicates stronger alignment (Studdiford et al., 1 Oct 2025).
Partial correlation and ablation analyses: Identify which variables (e.g., concreteness, frequency) uniquely drive alignment (Iaia et al., 21 May 2025).

B. Key Factors Impacting Alignment

Objective and Data Diversity: Training on diverse datasets and with objectives aligned to semantic reasoning (e.g., contrastive, image-text pairing) increases alignment more than scaling alone (Muttenthaler et al., 2022).
Instruction Fine-tuning: More important than parameter count or multimodal pretraining for developing human-like concept structures in LLMs (Studdiford et al., 1 Oct 2025).
Embodiment and Multimodality: Visual and sensorimotor experience improve model–human alignment in sensory and motor domains, which cannot be fully captured by text alone (Xu et al., 2023).
Conceptual Axis Dominance: Alignment is often driven by a single dominant dimension, such as concreteness, while other features contribute weakly or not at all (Iaia et al., 21 May 2025).

5. Applications and Implications

A. AI Value Learning and Safe Exploration

Representational alignment facilitates value learning and safe adaptation: models sharing human similarity structure learn human value functions faster, generalize more robustly, and reduce unsafe exploration (Wynn et al., 2023).

B. Social, Perceptual, and Embodied Inference

Human-like conceptual representations are foundational for machine perception of social interactions, as shown by verb-centric representations capturing human organization of action scenes (Yun et al., 25 Sep 2025).
Neurosymbolic methods equip AI agents with interpretable, image-schematic reasoning capabilities, enabling natural communication and intuitive alignment with human users (Olivier et al., 31 Mar 2025).

C. Limits and Controversies

Even strong task performers (LLMs, vision transformers, chess engines) may lack internal conceptual coherence or diverge from human concepts at deeper model layers, risking brittle generalization and opaque reasoning (Suresh et al., 2023, Lomaso et al., 29 Oct 2025).
Strict category dichotomies (concrete/abstract) rarely structure human conceptual or neural representation; fluid, context-dependent, high-dimensional organization is more realistic (Hendrikx et al., 2020).

D. Methodological Impact

Behavioral embedding methods (e.g., odd-one-out, triplet similarity) and abstraction-alignment metrics are critical new tools for evaluating and comparing the human-likeness of conceptual representations across populations, models, and training regimes (Boggust et al., 17 Jul 2024, Bao et al., 10 Mar 2024, Zheng et al., 2019).

6. Future Directions

Integrated Hybrid Models: Focus on unified architectures capable of flexibly combining symbolic, embodied, and multimodal conceptual features, embedded in efficient, interpretable latent spaces (Chang, 2022, Wang et al., 12 Jan 2024).
Personalized and Contextualized Concepts: Expand methods for quantifying and modeling individual, cultural, and context-driven differences in conceptual structure, with applications to fairness, value learning, and personalized AI.
Robustness and Safety: Prioritize architectures and training regimes that preserve conceptual interpretability and alignment throughout hierarchical processing, especially in creative and safety-critical domains (Lomaso et al., 29 Oct 2025).
Neuroscientific Grounding: Train and evaluate future systems in light of direct neural and behavioral alignment, using joint brain-model analysis to assess biological plausibility and guide inductive biases (Du et al., 1 Jul 2024, Xu et al., 21 Jan 2025).
Comprehensive Benchmarking: Develop new, behaviorally and cognitively informed evaluation frameworks capable of measuring not just performance, but the human-likeness and generalizability of conceptual representations (Studdiford et al., 1 Oct 2025, Muttenthaler et al., 2022).

Table: Critical Factors for Human-like Conceptual Representation (Examples from Data)

Factor	Empirical Signature/Affect	Exemplary Reference
Modality (Embodiment)	Sensory/motor alignment improves with direct experience; critical for non-verbal domains	(Xu et al., 2023, Wang et al., 12 Jan 2024)
Training Objective	Contrastive, image-text, and instruction tuning yield higher human-alignment than pure classification	(Muttenthaler et al., 2022, Studdiford et al., 1 Oct 2025)
Structural Coherence	Humans: high; LLMs: task/prompt-dependent, low	(Suresh et al., 2023, Lomaso et al., 29 Oct 2025)
Abstractness/Concreteness Axis	Concreteness is key shared dimension underlying alignment	(Iaia et al., 21 May 2025, Hendrikx et al., 2020)
Hierarchical/Abstraction Structure	Alignment with human abstraction graphs predicts generalization, error containment	(Boggust et al., 17 Jul 2024)
Individual/Cultural Experience	Conceptual spaces reorganize with sensory deprivation, culture, or social interaction	(Monaco et al., 2018, Bao et al., 10 Mar 2024)

Human-like conceptual representations, as currently understood, are not reducible to any single static formalism or dataset. Rather, they are multidimensional, flexibly structured across context, modality, and experience, and are ultimately defined by their ability to support the generalization, communication, and reasoning phenomena observable in human cognition and behavior. Advances in behavioral modeling, neuroimaging, and computational alignment continue to refine the operational standards by which these representations are scientifically measured and engineered.