Cognitive Elements in AI
- Cognitive Elements are explicit representations of mental states and knowledge units that underpin reasoning, perception, and decision-making in AI systems.
- They span multiple scales—from multi-agent belief systems and ontological models to granular reasoning operations like logical coherence and forward chaining.
- CE frameworks integrate physical states, communication, and cognitive behaviors, thereby supporting immersive systems, conversational agents, and LLM performance enhancement.
“Cognitive Elements” (CEs) is an overloaded term in contemporary arXiv literature rather than a single settled construct. In cognitive and immersive systems, CEs are defined as the formal representation of the mental attitudes and states of agents, including beliefs, knowledge, goals, desires, intentions, and emotions (Peveler et al., 2017). In content-centric computational cognitive modeling, the term is used for explicitly represented units of knowledge and reasoning such as ontological knowledge, lexical resources, memories, metaparameters, and heuristic decision functions (Nirenburg et al., 22 Mar 2025). In recent reasoning research, CEs denote a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, reasoning representations, and reasoning operations (Kargupta et al., 20 Nov 2025). A more ontological formulation defines a CE as any entity or symbol whose existence and definability is asserted by the minimal CES axiom and then located within the Hierarchical State Grid (Itoh, 15 Oct 2025). This suggests that the term functions as a family of related notions centered on explicitly modeled constituents of cognition, but at different levels of abstraction.
1. Terminological scope and definitional variants
The literature uses “Cognitive Elements” at several scales: as mental attitudes in multi-agent reasoning, as content-bearing structures in neuro-symbolic agents, as cognitively inspired components in conversational systems, and as elementary behaviors in reasoning analysis. The following summary captures the main usages represented in recent work.
| Setting | What CEs denote | Representative paper |
|---|---|---|
| Cognitive and immersive systems | Beliefs, knowledge, goals, desires, intentions, emotions | (Peveler et al., 2017) |
| Content-centric cognitive modeling | Ontologies, lexicons, memories, metaparameters, heuristics | (Nirenburg et al., 22 Mar 2025) |
| Reasoning analysis | 28 elements across invariants, controls, representations, operations | (Kargupta et al., 20 Nov 2025) |
In the CAIS line of work, CEs are “the core ontological entities” enabling theory-of-mind-level reasoning, because the system tracks what each agent knows, believes, wants, intends, or feels at any point in time (Peveler et al., 2017). In the C4 and LEIA tradition, the paper explicitly notes that it does not use “cognitive elements” as a formal object, but it does describe content-centric knowledge resources and mental models that play the role of manipulable cognitive units (Nirenburg et al., 22 Mar 2025). In socially oriented conversational-agent research, a closely related operationalization appears through semantic memory, episodic memory, emotion, working memory, and learning (Clay et al., 2023). In the LLM-reasoning literature, the phrase acquires a highly granular meaning: the paper synthesizes cognitive science into a taxonomy of 28 cognitive elements and studies their behavioral manifestation in humans and models (Kargupta et al., 20 Nov 2025).
A formal ontological generalization appears in the CES-IMU-HSG framework, where a CE is any entity or symbol whose existence is grounded by reflexive self-reference,
and whose defined status is realized as a uniquely specified state in the Hierarchical State Grid (Itoh, 15 Oct 2025). This is not the dominant usage in AI systems engineering, but it provides an explicit mathematical-logical definition of what counts as a cognitive element.
2. Formal representation in cognitive and immersive systems
The most explicit engineering definition of CEs is given in the work on cognitive and immersive systems. There, the representational backbone is the deontic cognitive event calculus, described as a quantified multi-operator modal logic extending event calculus and subsuming first-order logic with temporal, cognitive, and sometimes deontic modalities (Peveler et al., 2017). The primitive operators include belief, knowledge, desire, common knowledge, saying, perception, and intention:
$\believes(a, t, \phi),\quad \knows(a, t, \phi),\quad \desires(a, t, \phi),\quad \common(t, \phi),\quad \says(a, b, t, \phi),\quad \perceives(a, t, \phi),\quad \intends(a,t,\phi).$
The sorts include Agent, Time, Event, ActionType, Action, and Fluent (Peveler et al., 2017).
This framework treats mental attitudes as first-class logical objects. Nested attitudes are directly expressible, for example:
$\believes(a, t, \believes(b, t, \lnot \phi)).$
That expressivity supports theory-of-mind reasoning, false-belief detection, and intervention policies. The paper gives formal requirements for false belief correction and missing belief correction, both driven by the room’s model of agents’ mental states. It also proves a property called “expectation of usefulness”: if one agent perceives that another agent was absent for an event, the first agent believes that the CAIS will inform the absent agent (Peveler et al., 2017).
The Cognitive Polysolid Framework operationalizes these ideas in a cognitively enriched blocks-world setting. The room tracks changes in goals and beliefs as agents leave and re-enter, and intervenes when an agent returns with outdated beliefs about the current goals (Peveler et al., 2017). The significance of this formulation lies in its unification of physical state, communicative acts, and recursively embedded cognitive states within a single reasoning framework. In this usage, CEs are not merely attributes attached to agents; they are the formal substrate on which perception, explanation, planning, and collaboration depend.
3. Content-centric, neuro-symbolic, and conversational interpretations
In content-centric computational cognitive modeling, especially in the RPI LEIA Lab line of work, the relevant structures are explicit, semantically interpretable, declarative knowledge resources rather than opaque latent vectors (Nirenburg et al., 22 Mar 2025). The paper lists ontological knowledge, lexical resources, memories of past perceptions, interpretations, reasonings, and actions, metaparameters such as confidence, vagueness, and incompleteness, and heuristics and decision functions. It states that all this content supplies essential metacognitive heuristics for the agent’s decisions (Nirenburg et al., 22 Mar 2025).
This architecture gives CEs a metacognitive role. Because the agent has explicit access to its own beliefs, memories, reasoning processes, and information-quality parameters, it can inspect, adjust, explain, and learn from them (Nirenburg et al., 22 Mar 2025). Decision functions may omit a feature when the computational cost of determining a parameter value is too high, albeit with lower confidence in the resulting decision; confidence is itself computed using metaparameters including vagueness and incompleteness of sensory input (Nirenburg et al., 22 Mar 2025). The neural component is deliberately constrained: LLMs act as language generators and filters for surface realization, paraphrasing, content selection, and filtering of candidate new lexical material, while the symbolic CE infrastructure remains primary (Nirenburg et al., 22 Mar 2025). The paper presents this as the reverse of common LLM-centric neurosymbolic strategies.
A related but more application-oriented decomposition appears in research on social conversational agents. That literature does not formalize CEs in modal logic, but it identifies a set of cognitively inspired components that function as operational cognitive units: semantic memory, episodic memory, emotion, working memory, and learning (Clay et al., 2023). Semantic memory is modeled through knowledge graphs, knowledge graph embeddings such as
Bayesian networks, and ontologies. Episodic memory is modeled through structured event storage with contextual tags and partial-cue recall. Working memory acts as a session-context buffer between user input, episodic and semantic memory, and the core generative or retrieval model. Emotion is represented through valence-arousal-dominance coordinates, emotion embeddings, and reinforcement-learning formulations such as Sentiment Look-Ahead. Learning is implemented through continual or lifelong learning mechanisms, as in BlenderBot3 and NELL (Clay et al., 2023).
Across these architectures, the common thread is explicit manipulability. Whether the system reasons over modal attitudes, ontological concepts, episodic records, or metacognitive parameters, CEs are the structures that can be stored, inspected, updated, and used in downstream reasoning and action.
4. Cognitive elements as a taxonomy of reasoning behavior
Recent work on LLM reasoning shifts the meaning of CEs from architectural components to a fine-grained behavioral taxonomy. The paper introduces 28 cognitive elements drawn from cognitive science and grouped into four dimensions: reasoning invariants, meta-cognitive controls, reasoning representations, and reasoning operations (Kargupta et al., 20 Nov 2025). The invariants include logical coherence, compositionality, productivity, and conceptual processing. The meta-cognitive controls include self-awareness, context awareness, strategy selection, goal management, and evaluation. The representational dimension includes sequential, hierarchical, network, ordinal, causal, temporal, and spatial organization. The operational dimension includes context alignment, knowledge alignment, verification, selective attention, adaptive detail management, decomposition and integration, representational restructuring, pattern recognition, abstraction, forward chaining, backward chaining, and backtracking (Kargupta et al., 20 Nov 2025).
The empirical study is unusually large: 170K traces from 17 models across text, vision, and audio modalities, alongside 54 human think-aloud traces (Kargupta et al., 20 Nov 2025). Its central result is structural rather than merely accuracy-based. Humans employ hierarchical nesting and meta-cognitive monitoring, while models rely on shallow forward chaining, with divergence most pronounced on ill-structured problems (Kargupta et al., 20 Nov 2025). On well-structured problems, models show high behavioral diversity with average presence rate approximately 0.37; on ill-structured problems, average presence rate approximately 0.28, with concentration on sequential organization, logical coherence, and forward chaining (Kargupta et al., 20 Nov 2025).
The paper also reports specific human–LLM contrasts. Self-awareness appears in 49% of human traces versus 19% of LLM traces, and abstraction in 54% versus 36% (Kargupta et al., 20 Nov 2025). At the research-community level, the meta-analysis of 1,598 LLM reasoning papers finds that sequential organization is covered in 55% of papers and decomposition in 60%, whereas self-awareness appears in 16% and evaluation in 8% (Kargupta et al., 20 Nov 2025). The authors argue that the neglected elements are precisely those that correlate with success on complex, ill-structured tasks.
A practical consequence follows from this taxonomy. By extracting successful CE structures and linearizing them into test-time prompts, the paper reports performance improvements of up to 60% on complex problems (Kargupta et al., 20 Nov 2025). The claim is not that models lack the relevant behaviors entirely, but that they fail to deploy them spontaneously. In this usage, CEs are neither mental states nor symbolic memory objects; they are observable reasoning primitives and organizational patterns.
5. Measurement, augmentation, and empirical relevance
The measurement problem cuts across all CE interpretations. Work on measuring cognition and cognitive augmentation defines a cognitive process as any transformation that converts data, information, knowledge, or wisdom from one state to another, typically increasing its value (Fulbright, 2022). The paper reviews entropy-based, algorithmic, quantum, emergent, structural, and augmentation-oriented metrics, then concludes that no single metric universally quantifies all aspects of cognition and that a family of theoretically- and empirically-validated metrics should be constructed (Fulbright, 2022).
This framework is directly relevant to CEs because it asks how much thinking an artificial entity does, how much is done by a human, and how to measure the combined or emergent effect of humans working with intelligent artificial entities (Fulbright, 2022). It includes explicit augmentation measures for human and artificial contributions,
and augmentation factors
while also emphasizing that existing approaches do not adequately capture meaning, relevance, value, level of cognition, or empirical grounding in human-artificial synergies (Fulbright, 2022). The paper explicitly recommends future empirical work involving both human and artificial Cognitive Elements (CEs) (Fulbright, 2022).
A separate educational study provides an adjacent empirical signal about what is lost when AI systems take over parts of a task. In an experimental design with 40 adult students randomly assigned to ChatGPT or control conditions, the AI-assisted group reported significantly lower cognitive engagement on a four-item CES-AI measuring deep processing, mental effort, attention, and strategic thinking (Georgiou, 30 Jun 2025). The reported means were for the ChatGPT group and for the control group, with (Georgiou, 30 Jun 2025). The paper interprets this as potential cognitive offloading. Although the study concerns engagement rather than CEs as a formal ontology, it is relevant because many CE frameworks treat deep processing, strategic regulation, and self-monitoring as central to cognition.
6. Broader theoretical context, ambiguities, and open questions
A broader dynamical-systems perspective defines cognition as the process by which a system absorbs information into its state (Hall, 2018). In that view, a complex network of agents cognizes knowledge about its environment, internal dynamics and initial state by forming emergent, macro-level patterns; fast propagation dynamics aggregate information, and slower pattern dynamics coordinate via positive feedback to form coherent patterns defined using closed walks in the graph of agents (Hall, 2018). The paper emphasizes that a cognitive network’s knowledge is encoded or embodied by the selection of patterns which emerge. This suggests a systems-level background for CE thinking: local units matter, but cognition may be encoded at the pattern level rather than in any single element.
Another theoretical background comes from the distinction between apprehension and judgment. One paper describes apprehension as a fast, Bayesian, approximately 1-second process producing coherent perception through the recruitment of neuronal groups, whereas judgment is a roughly 3-second, language-mediated, self-conscious comparison of two apprehensions retrieved from memory and linked by inverse Bayes inference (Arecchi, 2017). The paper does not use CE terminology, but it sharply separates algorithmic perception from reflective, creative, model-forming cognition. A plausible implication is that later CE taxonomies, especially those separating representational structure from meta-cognitive control, are formalizing distinctions already present in this earlier account.
Two recurrent issues remain unresolved across the literature. First, there is no universal granularity for CEs. In one setting a CE is a belief formula, in another an ontology entry or episodic memory, in another a behavior such as backtracking or abstraction, and in another a definable state in a categorical grid (Peveler et al., 2017, Nirenburg et al., 22 Mar 2025, Kargupta et al., 20 Nov 2025, Itoh, 15 Oct 2025). Second, explicit CE modeling is often presented as an alternative to purely LLM-driven methods, yet the strongest recent behavioral study shows that models already possess some of the repertoires associated with success but do not deploy them spontaneously (Kargupta et al., 20 Nov 2025). This creates a live methodological question: whether future progress depends more on richer explicit CE architectures, better elicitation and scaffolding of latent behaviors, or hybrid systems that combine both strategies.
A final terminological caution is necessary. The acronym “CE” is not unique to cognition on arXiv. In structural dynamics, “CEs” refers to connecting elements in state-space substructuring, a wholly unrelated usage (Dias et al., 2023). Within cognitive research itself, the phrase remains polysemous. What unifies the major usages is not a single formal definition, but a shared commitment to decomposing cognition into explicit constituents that can be represented, manipulated, measured, or scaffolded.