# Enabling Human-CPS Cognitive Interoperability: Cognitive Architectures as Technologies for Human-Like Cognitive Digital Twins

Jana Al Haj Ali, Ben Gaffinet, Mario Lezoche, Hervé Panetto, Yannick Naudet

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Jana Al Haj Ali, Ben Gaffinet, Mario Lezoche, Hervé Panetto, Yannick Naudet. Enabling Human-CPS Cognitive Interoperability: Cognitive Architectures as Technologies for Human-Like Cognitive Digital Twins. Journal of Industrial Information Integration, 2025, 48, pp.100969. 10.1016/j.jii.2025.100969. hal-05312158

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# Enabling Human-CPS Cognitive Interoperability: Cognitive Architectures as Technologies for Human-Like Cognitive Digital Twins

Jana Al Haj Ali<sup>a</sup>, Ben Gaffinet<sup>a,b</sup>, Mario Lezoche<sup>a</sup>, Hervé Panetto<sup>a</sup>, Yannick Naudet<sup>b</sup>

<sup>a</sup>University of Lorraine, CNRS, CRAN, Nancy, France

<sup>b</sup>Luxembourg Institute of Science and Technology, Belval, Luxembourg

## Abstract

Cognition, the set of mental processes that enable humans to perceive, reason, learn and decide, plays an essential role in effective collaboration between humans and Cyber-Physical Systems (CPSs). To achieve seamless cognitive interoperability between humans and CPSs, it is necessary to integrate a Cognitive Digital Twin (CDT) and a Human Digital Twin (HDT) to provide digital representations of both physical assets and human cognitive states. In this article, we first analyse the three essential functions of CDT and HDT: emulation, cognition and simulation, and review the state-of-the-art technologies for each of them, from supervised learning and knowledge graphs to deep reinforcement learning. Focusing on the cognitive layer, we review the state of the art in cognitive architectures, describing their symbolic, sub-symbolic and hybrid types and reporting on their real-world implementations in different domains. We then assess the relevance of these architectures for the integration of human-like reasoning in CDTs. Finally, we identify the main technological challenges and gaps that need to be addressed in order to implement fully operational CDTs.

**Keywords:** Human-CPS Collaboration, Cognitive Digital Twin, Human Digital Twin, Human Cognition, Cognitive Architecture, Industry 5.0

## 1. Introduction

Industry 5.0 promises a paradigm shift towards human-centric smart manufacturing, in which human workers and AI-equipped machines collaborate as partners [8]. This evolution is part of a broader transformation in which the industry of the future is moving towards becoming a Cyber-Physical Enterprise (CPE), comprising autonomous and cooperative technical elements, as well as humans and sub-organisations, which are interconnected across processes, machines, companies, and supply chain networks [50]. Operating such an enterprise increases organisational complexity and imposes strict interoperability requirements, i.e. the fundamental ability of different digital products or systems to connect and exchange information [21]. At the same time, the growing complexity of CPEs necessitates capabilities that extend beyond basic mechanical control or data exchange. Simply replicating a physical process in a digital system is no longer enough; Cyber Physical Systems (CPSs) must integrate perception, reasoning and learning capabilities in order to manage dynamic and uncertain environments. In other words, as companies integrate IoT networks, robots and human operators into closely linked ecosystems, there is a growing demand for systems capable of connecting, understanding and aligning with human thought processes so that they can adapt to new situations in real time.

In this context, traditional interoperability, which focuses on technical, syntactic and semantic compatibility, is insufficient. Cognitive interoperability is needed instead [3]. This is defined as the ability of heterogeneous agents (humans and CPS) to share their understanding and align their decision-making processes for effective collaboration [47]. Harmonious collaboration between humans and CPS requires not only the exchange of data, but also alignment with human behaviour, communication models and thought processes. Cognitive interoperability therefore emphasises shared situational awareness, unity of intent, and mutual understanding between human operators and intelligent machines. In practical terms, this means aligning mental models so that an artificial agent can interpret information and make decisions that its human teammate can understand [47]. For instance, an AI system with high cognitive
interoperability would grasp the objectives and context of a human operator, potentially anticipating their require-
ments or inclinations. Our recent studies have identified cognitive interoperability as a fundamental factor in the
effectiveness of collaboration between humans and CPS in next-generation enterprises [2].

The concept of the Cognitive Digital Twin (CDT) is a promising approach to implementing cognitive interoper-
ability in practice [2]. Unlike a traditional Digital Twin (DT), which is a real-time digital replica of a physical asset or
process [61], a CDT integrates cognitive functions such as perception, attention, memory, reasoning, problem solving
and learning [72], enabling it to interpret state information, make decisions and learn over time. In other words, a CDT
not only represents the physical state of a system, but also certain aspects of its cognitive state, such as its objectives
and decisions, or even the cognitive context of a human operator [18]. Thus, the CDT framework can be applied
to machines and human operators alike. Researchers envisage environments in which every CPS and every human
is associated with an individualised DT, enabling cognition to be distributed across a human-machine collaboration
[46]. In such a configuration, for example, an operator's Human Digital Twin (HDT) could model their cognitive
state, including their intentions, workload, and situational awareness.

We present a CDT architecture organised around three core functions: emulation, cognition, and simulation [46]. Firstly, the emulation function ensures that the CDT has an accurate and up-to-date representation of the real system. This is essential for higher-level reasoning and decision-making. Secondly, the cognition function emulates the agent’s reasoning, learning and decision-making processes in order to perform semantic reasoning, goal-oriented planning, anomaly detection and adaptation through learning. The third function, simulation, enables the CDT to learn from interactions by simulating possible future interactions and assimilating data from past real-world interactions. It can then update its knowledge or strategies accordingly. These three functions together create a feedback loop: emulation provides a reference context for the cognitive module, which generates decisions or learning updates. Simulation then allows these decisions to be refined and learnt from in a virtual environment before being applied in the real world [46].

Implementing these functions requires various technologies. For example, emulation can be supported by deter-
ministic models and simulation by machine learning tools, while cognitive architectures or other AI approaches can
support cognition. Cognitive architectures, such as SOAR [32], ACT-R [54] and LIDA [16], form part of a central field
of natural and artificial intelligence dedicated to simulating the complex processes of perception, decision-making and
learning in the human brain [4]. These are frameworks that define how an artificial agent perceives, reasons and learns.
These can generally be classified as symbolic, sub-symbolic or hybrid architectures. The various sections of the ar-
ticle present the strengths and the limitations of each category that need to be evaluated for integration into the CDT
framework.

In the remainder of this article, we develop a framework for cognitive collaboration between humans and CPS,
based on the ideas presented above. The main contributions of this work are as follows:

* Define cognitive interoperability in human-CPS collaboration and present the CDT and the HDT as a framework for representing both physical assets and human cognitive states.

* Identify the three functions of the CDT: emulation, cognition and simulation, accompanied by a state-of-the-art review of the technologies supporting each of these functions.

* Review symbolic, sub-symbolic and hybrid cognitive architectures, with an overview of their theoretical foundations, types and concrete implementations in human-machine interaction, robotics and manufacturing.

* Evaluate the integration of these architectures into CDTs and HDTs to provide them with human-like reasoning capabilities, highlighting the advantages and disadvantages of each approach.

* Discuss criteria for selecting cognitive architectures over alternative technologies for each CDT function.

* Systematically identify the key technological challenges and research gaps that need to be filled to achieve the deployment of fully operational CDTs and HDTs.

The rest of this article is organised as follows: Section 2 provides an overview of human cognition and describes its key functions. Section 3 introduces the concept of cognitive interoperability, demonstrating its central role in collaboration between humans and CPS. Section 4 focuses on cognition in digital twins, defining the emulation, cognition and simulation functions of a CDT, and reviewing the key technologies that support these functions in Section
5. Section 6 reviews cognitive architectures, from their origins in biological systems to symbolic, sub-symbolic, and hybrid AI frameworks. It also examines existing implementations in Human-Machine Interaction (HMI), robotics, and manufacturing. Section 7 then presents our research roadmap. This addresses the functional challenges of CDTs, compares cognitive architectures with other approaches and details their advantages and limitations in the fields of HMI, robotics, manufacturing and human behaviour emulation. Section 8 concludes with a summary of our findings and directions for future work.

## 2. Cognition: Global View

There is a significant difference between Artificial Intelligence (AI) and biological intelligence, due in part to the complexity and adaptability of human cognitive processes. Cognition plays a central role in bridging this gap, serving as the link between artificial systems and human-like intelligence. Recently, there has been growing interest in understanding and defining artificial cognition. This interest clearly differs from previous approaches based mainly on experimental psychology to evaluate or interpret Machine Learning (ML) algorithms by comparing them with human performance [67]. The aim of artificial cognition is not to apply methods from psychology to analyse or explain the behaviour of AI systems. Rather, it is to identify and study the cognitive characteristics of AI systems that are close to human capabilities. More specifically, the study of cognition aims to integrate it directly into these systems in order to obtain cognitive systems comparable to humans.

Cognition represents the sensory processes, general operations and complex integrated activities involved in interacting with information [62]. Sensory processes include vision, perception and attention. General operations include language, memory, recognition, recall, information retrieval and management behaviours. Complex integrated activities include reasoning, judgement, decision making, problem solving, meaning making and creativity. These cognitive processes, ranging from basic sensory functions to complex decision-making and reasoning, are not abstract; they are based on distinct anatomical regions of the human brain. The cognitive function mapping scheme was established based on neuroscience research [69, 7] and is illustrated in Figure 1, where different lobes are responsible for specific cognitive functions. The frontal lobe governs executive functions such as planning, reasoning and problem solving, while the parietal lobe deals with spatial awareness and sensory integration. The occipital lobe processes visual data and the temporal lobe is at the heart of memory and language comprehension. The cerebellum, although traditionally associated with motor control, also contributes to cognitive activities such as motor learning and coordination.

<figure><img src="image_2.png" /><figcaption>Figure 1: Cognitive Functions in Human Brain</figcaption></figure>
By highlighting the distribution of cognitive tasks between these brain regions, the figure reinforces the idea that cognition is not limited to data collection. It involves the transformation, storage and effective use of knowledge to understand, judge, make decisions and adapt dynamically [2]. It covers a wide range of processes, from basic perception and memory retrieval to complex problem solving and creative reasoning, enabling intelligent behaviour and flexible responses to a variety of circumstances.

### 3. Cognitive Interoperability

In CPEs, humans and CPSs interact continuously to perform common tasks. While syntactic and semantic interoperability allow these systems to exchange data and interpret predefined meanings, they are insufficient for true collaboration, especially in dynamic or uncertain environments. These forms of interoperability do not guarantee that humans and CPSs perceive situations, interpret information or reason about actions in the same way. Humans rely on context, experience and intention, while CPSs operate on structured data and fixed logic. This mismatch can lead to misunderstandings, misaligned goals or coordination failures, especially in situations that require mutual adaptation, real-time responses or joint decision making. To address these limitations, we consider cognitive interoperability as a key element of human-CPS collaboration. Drawing on prior work and a recent state-of-the-art analysis, cognitive interoperability refers to the ability of different agents (human or artificial) to align their thoughts and perception of information, allowing for mutual understanding and shared intentions. It involves creating shared mental models, aligning knowledge, and ensuring a common way to use, interpret and reason knowledge. As proposed by Naudet et al. [48], cognitive interoperability is a prerequisite for collaboration between CPSs and humans by implementing mutual understanding, shared cognitive processes and the semantics of the information exchanged. Cognitive interoperability means aligning the minds of the entities interacting with each other. For CPSs and humans, this means that:

* CPSs are social-cognitive agents with human-like modes of thought and behaviour that enable them to act as peers of humans.

* There is a cognitive coupling between CPSs and humans, with a mutual and shared understanding and perception of each other's situations and intentions. This is achieved through social cognition.

Cognitive interoperability is based on a set of interacting functions that enable agents, whether human or artificial, to align their internal processes of understanding and reasoning. As shown in Figure 2, this alignment begins with the individual perceptions of both the human and the CPS, which are harmonised to reach a state of congruence in perception. This congruence allows each agent to interpret stimuli and reason in a compatible way, forming the foundation for a coherent understanding of the shared environment. On this basis, each agent develops its own internal understanding of the situation, which is then aligned with the other's through iterative exchanges and feedback mechanisms. This process leads to a state of *mutual understanding*, in which agents not only share information but also interpret it in a similar way, recognising each other's perspectives and intentions. Such reciprocity is essential to build trust and predictability in human-CPS interactions. Aligning perception and understanding creates *shared mental models*, representing the cognitive structures maintained by both entities. These models guide joint decision-making, allowing humans and CPSs to coordinate their actions and make informed choices based on a harmonised understanding of the situation. To support the emergence of these components in real-world interactions, it is essential to activate the cognitive functions shown in Figure 1, which together enable agents to interpret, reason and adapt in a mutually comprehensible and coordinated way.

To achieve cognitive interoperability between humans and CPS, it is essential to ensure that both agents can interact, reason and make decisions based on a shared and continuously updated understanding of their environment. This requires systems capable of representing physical and numerical states, interpreting heterogeneous data from multiple sources, including human data, and adapting their behaviour in real time as objectives and contexts change. These requirements imply the need for a DT as a fundamental component. A DT provides a synchronised, real-time model of the CPS, enabling monitoring, analysis and interaction. However, to meet the cognitive requirements described above (human-like capabilities), this DT needs to evolve into a CDT. The limitations of conventional DTs and the definition, functionality and application potential of CDTs are discussed in the next section (section 4).
<figure><img src="image_3.png" /><figcaption>Figure 2: Main Components of Cognitive Interoperability [3].</figcaption></figure>

# 4. Cognition in Digital Twins

Recent developments in DT technology have increasingly focused on integrating cognitive capabilities beyond static data replication and real-time monitoring. Originally developed as digital replicas of physical systems, DTs focused primarily on monitoring operational parameters and simulating behaviour. However, as systems become more autonomous and context-aware, there is a growing push to integrate cognitive elements into DT frameworks. This enables DTs not only to reflect the current state of physical entities, but also to analyse, predict and dynamically adapt to complex environments. By integrating cognitive layers, DTs become more than reactive systems; they evolve into intelligent entities capable of making real-time decisions, detecting anomalies and performing optimisation tasks. In the following sections, we extend this discussion by exploring how cognition is being exploited in Human Digital Twins (HDTs) to replicate human cognition, thinking and behaviour, and in CDTs in industrial contexts to enhance system intelligence, autonomy and adaptability.

## 4.1. Cognition in CDTs

Although DTs have provided the foundation for real-time system monitoring and simulation, their limitations become apparent in environments that require adaptive and autonomous behaviour and semantic understanding. To address these challenges, the CDT has emerged as a next-generation paradigm that integrates AI-driven cognition into the DT framework. Defined as a digital representation that embodies intelligent capabilities covering all phases of the life cycle of its physical counterpart [1], a CDT goes beyond the replication of the physical system to integrate cognitive capabilities inspired by human cognition (Figure 1). These capabilities enable the CDT to dynamically interpret sensor data, anticipate changes and autonomously optimise system performance. In industrial environments, CDTs are increasingly recognised as essential tools for resilient, agile and human-centred systems, particularly in domains such as supply chain management where synchronisation, responsiveness and multi-actor coordination are essential [24]. By modelling not only operational parameters, but also intentions and behavioural patterns, CDTs improve human-machine collaboration and system interpretability. Therefore, a “CDT is a DT and a cognitive system. As a DT it emulates a physical system that can be or not itself cognitive. As a cognitive system it possesses cognitive functions, bringing it in particular the ability to semantically model, process and interpret information autonomously and actively learning from its interactions” [2].

This dual characterisation of the CDT, as both a digital replica and a cognitive system, raises key research questions regarding the localisation and structuring of cognition within the DT framework. These questions are particularly important in the context of CPEs, where heterogeneous entities, comprising both technical systems and human actors, interact continuously and are all potentially augmented by a DT. In such environments, CPSs such as machines, robots, actuators and sensors, as well as the humans operating in the workshop, constitute the set of active entities whose coordination and adaptability are fundamental to the company's performance. When these entities are individually
coupled to DTs, cognition can emerge at different levels, either naturally, as in the case of humans, or artificially via computer models in the CPSs and their DTs. This has led to the recent conceptualisation of cognitive CPSs (CCPSs) and CDTs. Notably, these developments have often followed distinct research trajectories, and the question of how, where and for what purpose cognition should be integrated into DTs remains largely undefined. This challenge has been directly addressed in our recent work [46], in which we propose the Entity CDT architecture to formally define the structure and functional allocation of cognition within the DT of an entity. The architecture, illustrated in Figure 3, specifies the processing layers of the entity (which can be a CPS or a CCPS) and its corresponding CDT, with explicit attention to the role and positioning of cognitive capabilities.

<figure><img src="image_4.png" /><figcaption>Figure 3: Entity-CDT Architecture. Entity here can be a CPS or a CCPS [46].</figcaption></figure>

An entity is conceptualised as an agent composed of sensors, a control system and actuators, which enable it to perceive its environment (*obs*), transform observations into control decisions (*cmd*) and execute physical actions (*act*). These actions are responses to external stimuli (*sti*) and can in turn influence other entities or environmental conditions. The CDT receives structured data (*data_e*) from its physical counterpart, including sensor outputs, command notifications and internal state data, and can also access larger environmental data (*data*), such as that generated by other entities, DTs or information systems. Three essential functions are defined within the CDT:

### 4.1.1. Emulation Function

The CDT emulation function computes an estimate of the response (action) of the entity *act* to a stimulus *sti*. This input may be supplemented by data provided by the entity *data_e*, which may include, in particular, its own observations *obs* and notification of commands sent *cmd*, as explained above. In the case of a CPS, it can be assumed that the observations of its sensors can be automatically transmitted to the DT. In the case of a human, the transmission would be indirect via a Human-Machine Interface (HMI), subject to specific interpretation by the human: it cannot be raw data from the human senses, except via a dedicated external sensor (e.g. ECG, EEG, oculometer, etc.). The DT then retrieves all useful data from its or the entity's environment: from sensors and other entities in the entity's environment, from other DTs, and from any other useful source of information. From this data, the DT's emulation function estimates the state of the entity and the resulting action *act* in response to the stimulus *sti*, using entity models built from the acquired, a priori known or learned data. This function enables synchronisation between the physical and digital layers.

### 4.1.2. Cognition Function

The CDT cognition function takes as input *act*, the estimated response of the entity by the emulation function, and data from the environment (*data*) and the entity (*data_e*) to perform any cognitive processing deemed useful to the system. This function is essential to enable autonomous behaviour, context-sensitive reasoning and adaptation to unexpected situations. Any form of cognition can be implemented here, including learning, anomaly detection, symbolic reasoning, or goal-based decision making. A common objective is to detect deviations between the observed reality and the estimated state or action of the entity, and to reason about these gaps in order to derive possible explanations or corrective actions.

Cognition in CDTs can be implemented using two main strategies, as illustrated in Figure 4. The first approach (1) aims to provide cognitive capabilities to a non-cognitive physical entity (e.g. a standard CPS). In this configuration, a **strong coupling** is established via a bidirectional link between the cognition function of the DT and the controller of the entity, effectively transforming the CDT into a **cognitive controller**. This allows the CPS to benefit directly from
<figure><img src="image_5.png" /><figcaption>Figure 4: Two implementation strategies for cognitive functions within CDTs. (1): Cognition is externalized to support a non-cognitive CPS via strong coupling. (2): Cognition is internal to the CDT and supports only its own functions, with weak coupling to the physical system.</figcaption></figure>

the reasoning and decision-making capabilities housed in the CDT. The second strategy (2), more commonly found in the literature, involves implementing cognition as an *internal service* that supports the CDT's own operations, in particular, model simulation, analysis and optimisation. Here, the coupling between the CDT and the physical system is **weak**, as the cognitive functions remain within the CDT and do not directly control or enhance the CPS. Nevertheless, this configuration enhances the ability of the CDT to generate refined predictions, contextual recommendations or adaptive feedback, thus contributing to the overall intelligence of the system without changing the behaviour of the physical entity itself.

### 4.1.3. *Simulation Function*

The simulation function allows the system to explore possible future situations and test how the entity might behave in those situations. This function is particularly useful when the system is confronted with situations that have not been planned or programmed in advance. It can be activated during operation, for example when something unusual happens, or in parallel with operation to prepare for situations that may occur in the future. While the emulation function shows how the system is currently behaving, the simulation function helps the CDT to anticipate and prepare for what might happen. To do this, the CDT needs a model not only of the entity to which it is connected, but also of the environment around it, i.e. the other systems, variables and constraints that are likely to affect its behaviour. In a CPE, this might mean simulating a complete production cell, with all the machines and conditions that interact. Because each entity has its own CDT, it is easier to simulate how all parts of the system work together. Each CDT uses its own models to respond to a common situation and achieve its own goals.

The simulation starts with a defined situation, such as a problem or scenario to be solved. The simulation engine sends a version of this situation to all the CDTs involved. Each CDT first uses its emulation function to estimate how the real system would react. Then the simulation function adjusts or improves this action using the CDT's internal logic, such as optimisation rules or learning-based methods. In our work, we use *Deep Reinforcement Learning (DRL)* to help the CDT explore better ways of acting. This method allows the CDT to try different strategies and learn the one that works best over time, using the same behavioural models already used in the emulation function (this approach has been selected on the basis of a comparison detailed in section 5.3). This approach offers more flexibility than other simpler models such as BDI (Belief-Desire-Intention). At each stage of the simulation, the simulation function generates an improved action based on the system's knowledge and objectives. The simulation engine collects the actions of all the CDTs, checks whether the global objectives and constraints are respected and helps the system to evolve towards a common solution.

It is within this functional framework that the CDT demonstrates its full potential: as an intelligent agent capable of operating in complex situations, supporting both the autonomous behaviour of the system and the coordinated responses of multiple entities. In section 5, the specific technologies used to implement emulation, simulation and cognition functions in a digital twin will be explored in detail. In the next section 4.2, we focus on a specific class of CDTs: the Human Digital Twin (HDT).
## 4.2. Cognition in HDTs

When the paired entity is a human, a DT becomes an HDT, a specialised form of DT designed to model and interact with human agents. A precise definition is given in [18]: "*Human Digital Twin is a class of Digital Twin whose twinned entity is a human individual. It is a digital representation of the twinned human, emulating their state and dynamics. It automatically receives data, with optional manual entries, and provides automatic feedback directly to the twinned human.*". The human nature of the twinned entity creates a set of specific challenges. Unlike CPS, which can transmit sensor data directly and continuously to their CDTs, humans require indirect communication channels, usually through HMIs or external sensing devices. These devices may include physiological or behavioural sensors such as ECG, EEG or eye trackers that provide partial observations of the human's internal state. Consequently, the emulation and cognition functions of an HDT need to process noisy, incomplete and heterogeneous data in order to infer relevant information about the human, such as his physical state, intention, workload or emotional state. This requires multimodal data fusion, semantic interpretation and reasoning about high-level unobservable variables. The role of the emulation and cognition functions are also different from CDTs due to the limited control and influence an HDT can exert on their twinned human.

Figure 5 illustrates key differences between a CDT of a CPS (see Figure 3) and an HDT of a human. The human is conceptualized as an agent that responds to stimuli sti from the environment with a motor action act. Through their sensory organs the stimulus sti is processed into observations obs. Based on the observations and internal state the human's cognitive system making a decision dec on what action to execute, which leads to an observable action act in physical space. The HDT receives data about the environment, stimulus, other entities and DTs in the same way a CDT does. However, a first major difference to CDTs is the data the available data from the human. Data about the human state data_h can be sensed by deploying sensors. Given the limitations of sensing capabilities, many parameters of a human's internal state remain hardly accessible. A second source of information about the human are manual data entries. The human may choose to provide information through an action act_m that needs to be converted into exploitable data data_m by an HMI. For instance, a human can communicate that they feel tired through a tablet which sends the information to the HDT. Feedback fbk_data from the HDT cannot be directly received in the form of data, instead it is necessarily transformed by the HMI into a physical stimulus fbk_sti that can be perceived and understood by the human. Next, we discuss the HDT functions and how their role is different from a CDT.

<figure><img src="image_6.png" /><figcaption>Figure 5: Human-HDT Architecture.</figcaption></figure>

### 4.2.1. Emulation Function

Emulation is deeply dependent on the measurable data about the human data_h. As previously mentioned, gathering data about humans is more challenging as many internal processes are undetectable and need to be inferred from available data. Additionally, the stimulus is not directly accessible either. For CPS the output of sensors can be shared directly with the CDT, but for humans only the input of the sensory organs, meaning the stimulus sti is detectable. Detecting the cognitive state has serious limitations as existing detection methods, like fMRI or EEG,
restrict the behaviour of the human. Alternatively, the behaviour can be modelled based on the received stimulus sti
and validated with the observed discrepancies between the predicted action âct and actual action act. In this case,
the entire cognitive cycle covering all the cognitive functions that are naturally present in humans. Consequently, an
HDT that emulates human behaviour by modelling the entire cognitive cycle will exhibit all the cognitive functions
to be considered a cognitive system and hence a CDT. However, HDTs that emulate non-cognitive aspects should not
be considered CDTs.

4.2.2. Cognition and HDT functions

Based on the emulated human action a whole variety of useful functions may be implemented. A first is to use
the output and compare it with the real behaviour. Any discrepancy can be interpreted in two ways; (1) the emulation
model inaccurate and needs to be calibrated or trained to more closely follow the real human behaviour; (2) the human
is exhibiting abnormal behaviour for which an intervention may be needed. In the first case the new observations may
be used in subsequent model training (if data-driven models are deployed). For parametrised mechanistic models it is
an indication that a calibration step is required. In the second case the emulated behaviour can be considered the one
of a undisturbed and healthy human being. Under that assumption discrepancies can be an indication of abnormal and
dangerous behaviour. For example, the human may have a medical emergency which exceeds the modelling scope of
the emulation model. A less dramatic example is a worker that is distracted by external factors that are not captured
by the HDT (e.g. loud noise when the HDT is unable to receive auditory data). If an intervention is needed the HDT
can do so by sending a feedback fbk_data to the HMI which is transformed for the human into a feedback stimulus
fbk_sti, for instance a message on a screen or an alarm sound. Alternatively, the HDT can send messages to external
system to adapt to the unexpected human behaviour.

4.2.3. Simulation Function

The HDT’s simulation function can be used to explore new situations including the possible reactions of human
workers. As for simulations with CDTs, simulations with HDTs require a simulator of the environment as well.
Exploring new situations is particularly useful if multiple DTs (CDTs of CPSs and HDTs of humans) are available to
co-simulate rare and dangerous situations where a physical test would put a human worker at risk [19].

An additional use of the simulation function is evaluating the reaction to provided HDT feedback within a given context. It would help avoid interruptions during critical moments that may have a negative effect on production outcomes or safety. In general, it may help provide relevant information at right time and in the right format to guide the worker without causing unnecessary cognitive load.

**5. Technologies for Cognitive Digital Twin functions**

The cognitive nature of an entity, whether fully cognitive (e.g. a human) or non-cognitive (e.g. a standard CPS), directly influences the choice of implementation strategies for each of the core functions of CDT: emulation, cognition and simulation. In the following sections, we present the main technologies used to implement each of these three functions.

**5.1. Technologies for Emulation function**

The emulation function of a DT is to mimic the behaviour of the real system in real time. Emulation creates a
live mirror of the state of the physical asset, often using sensor feeds and data from the control system, so that the
CDT reflects what is happening now or in the very recent past in the factory, as described in section 4.1.1. In other
words, when the physical machine changes state, the emulated CDT is immediately updated to reflect that change.
This real-time synchronisation is essential for cognition, as it provides the CDT with an up-to-date perception of the
physical world. Table 5.1 shows the key technologies and approaches that enable the emulation function.

Based on Table 5.1, industrial IoT connectivity provides the basic “nervous system” for emulation by continu-
ously streaming sensor and controller data, via MQTT, OPC UA or 5G, from the shop floor to the CDT, ensuring a
low-latency pipeline that keeps the digital model in phase with its physical counterpart [42]. Secondly, real-time 3D
models and virtual environments, often implemented in tools such as Siemens Tecnomatix Plant Simulation running
<table><caption>Table 1: Emulation Function Technologies in CDTs</caption><thead><tr><td>Technologies</td><td>Key Tools &amp; Platforms</td><td>Description</td></tr></thead><tbody><tr><td>Industrial IoT Connectivity</td><td>MQTT, OPC UA, 5G gateways, IoT sensors</td><td>Provides real-time “perception” of machine state [42].</td></tr><tr><td>Real-Time 3D Models &amp; Virtual Environments</td><td>Siemens Tecnomatix Plant Simulation, game engines</td><td>Visualizes live shop-floor conditions for anomaly detection [53].</td></tr><tr><td>Virtual Commissioning &amp; Controller Emulation</td><td>PLC simulators (hardware-in-the-loop)</td><td>Enables risk-free testing of control logic in sync with real hardware [53].</td></tr><tr><td>Augmented Reality for Live Monitoring</td><td>AR headsets, tablet apps</td><td>Overlays current metrics on physical assets to enhance situational awareness [68].</td></tr><tr><td>Supervised Machine Learning (ML)</td><td>Random Forest, Decision Trees</td><td>Real-time sensor-to-state mapping and anomaly detection [23, 20].</td></tr><tr><td>Knowledge-Based Systems (Ontologies) and Semantic Graphs</td><td>Protégé, RDF stores, Neo4j + ML pipelines</td><td>Integration of expert rules and contextual knowledge and semantic labelling of sensor/data streams for contextual perception [38, 45, 71].</td></tr></tbody></table>

in “soft real-time” connected mode, recreate a live graphical replica of machines, conveyors and parts, allowing human operators and AI agents to visualise current conditions and detect anomalies as they occur [53]. Following this, virtual commissioning goes a step further by embedding real PLC logic in hardware or software simulators in the loop, allowing developers to safely test control programs against a true-to-life emulation of the machine’s behaviour [53]. In addition, augmented reality interfaces (such as tablets or headsets) then project this live emulation data, temperatures, quality measurements, tool positions, directly onto physical assets, improving situational awareness on the shop floor [68]. To add cognitive depth, supervised machine learning models (such as random forests or decision trees) process sensor streams in real time to map raw inputs to operational states or detect faults on the fly [23, 20], while ontology-driven approaches use Knowledge Graphs (KG), built with Protégé, RDF shops or Neo4j, to semantically label and contextualise data streams, improving the CDT’s ability to classify events and reason about their implications [38, 45]. In maintenance settings, ontology-based DTs have been shown to support structured decision-making and data integration across the asset lifecycle [71].

Among these technologies, supervised ML is particularly effective for emulation due to its flexibility and scalability. When combined with KGs, it can be further enhanced by integrating semantic labelling and reasoning capabilities. KGs help to structure and annotate sensor data with domain-specific concepts, enabling models to generalise more effectively and improve explanatory power. For example, industrial platforms have used RDF-based KGs to associate raw data such as vibration or temperature signals with meaningful categories such as “bearing wear” or “heat stress”, which are then used to build predictive models [5]. This hybrid approach significantly reduces manual data pre-processing, improves interpretability and enables more accurate and context-sensitive emulation.

## 5.2. Technologies for Cognition function

In the context of CDTs, two implementation strategies, strong coupling and weak coupling, require different sets of enabling technologies (as explained in Section 4.1.2). Table 2 presents the key technologies suitable for each strategy, drawing on both cognitive architectures and complementary AI and knowledge-based methods.

According to Table 2, each technology category brings distinct cognitive capabilities to CDTs. Cognitive architectures are theoretical frameworks that formalise perception, working and long-term memory, decision making and
<table><caption>Table 2: Cognitive Function Technologies in CDTs</caption><thead><tr><td>Technologies</td><td>Key Tools &amp; Platforms</td><td>Description</td></tr></thead><tbody><tr><td>Cognitive Architectures</td><td>SOAR engine, ACT-R framework</td><td>Symbolic reasoning and human-like planning via rules, chunking, modular memory and RL [6].</td></tr><tr><td>ML Models (DL, RL)</td><td>TensorFlow, PyTorch, scikit-learn, RL toolkits (e.g. OpenAI Gym)</td><td>Data-driven perception, anomaly detection and policy optimization via RL [13].</td></tr><tr><td>KGs and Ontologies</td><td>Neo4j Aura, Amazon Neptune, RDF4J, OWL APIs</td><td>Semantic modeling of domain knowledge (long-term memory) and context-aware inference [33, 25].</td></tr><tr><td>Rule-Based Reasoning Engines</td><td>Expert systems, logic solvers, Prolog engines</td><td>Deductive if-then reasoning for diagnosis, planning, and anomaly explanation, high transparency of decision logic [13, 26].</td></tr><tr><td>Hybrid Approaches (cognitive multi-agent, codelet-based)</td><td>JADE (agents), custom codelet frameworks, MPI for parallelism</td><td>Neuro-symbolic AI, and distributed agents (e.g. many small “codelets” for perception, memory, action) aligned with cognitive theories (global workspace, attention) [26].</td></tr></tbody></table>

learning in an integrated processing cycle; SOAR and ACT-R use production rules and modular memory constructs to support goal-directed planning, episodic retrieval and adaptive rule adaptation [30, 6]. Next, machine learning frameworks (TensorFlow, PyTorch, scikit-learn, OpenAI Gym) provide data-driven perception and reinforcement control policies for real-time anomaly detection and process optimisation, but their black-box nature and large data requirements usually require coupling with symbolic methods to ensure interpretability and robustness [13]. Then, knowledge graphs and ontologies (Neo4j Aura, Amazon Neptune, RDF4J/OWL) provide structured semantic stores that enable contextual inference and provenance tracking across heterogeneous data sources, although building and synchronising large domain models with high-speed sensor feeds is labour intensive [33, 25]. In addition, rule-based engines (expert systems, Prolog) provide transparent deductive reasoning for fault diagnosis and planning with clear audit trails, essential for safety-critical scenarios, but require continuous updating of the rule base and specialised middleware to integrate noisy real-time sensor streams [13, 26]; and hybrid, codelet-based or multi-agent frameworks (JADE, MPI) aim to unify symbolic reasoning and sub-symbolic learning within the framework of cognitive theories such as the global workspace to produce adaptive and emergent behaviours, although they introduce considerable complexity in terms of integration and validation [26].

Among these approaches, cognitive architectures stand out as the most promising foundation for CDTs, as they provide a unified framework capable of simulating **human-like** reasoning, learning, and synchronisation, while seamlessly integrating symbolic rule-based processing with sub-symbolic learning mechanisms. Their ability to model goal-directed planning, episodic memory, and adaptive rule optimization makes them particularly suited to reproducing operator behaviour and decision dynamics in complex production environments [4], and will be explored in detail in Section 6.

## 5.3. Technologies for Simulation function

The simulation function of a CDT focuses on predictive modelling and scenario exploration. In this context, simulation means that the CDT can perform virtual experiments of the manufacturing process or product, predicting future states or testing hypothetical conditions without affecting the actual system (as described in section 4.1.3). Table 5.3 shows the key technologies and approaches that enable the emulation function.
<table><caption>Table 3: Simulation Function Technologies in CDTs</caption><thead><tr><td>Technologies</td><td>Key Tools &amp; Platforms</td><td>Description</td></tr></thead><tbody><tr><td>Discrete-Event &amp; Process Simulation</td><td>Plant Simulation, custom DES engines</td><td>Enables “what-if” analyses and AI-driven process optimization [73]</td></tr><tr><td>Physics-Based Simulation Engines</td><td>MATLAB/Simulink, multibody &amp; CFD solvers</td><td>Generates high-fidelity time-series data for AI training [25]</td></tr><tr><td>Co-Simulation &amp; Multi-Model Integration</td><td>Functional Mock-up Interface (FMI), HLA middleware</td><td>Captures system-of-systems behavior for holistic scenario planning [35]</td></tr><tr><td>AI and Reinforcement Learning (RL) in Simulation</td><td>A2C, PPO, SAC agents in discrete-event simulators</td><td>Provides a virtual “playground” for learning and refining control policies via sim-to-real transfer [37, 42]</td></tr></tbody></table>

Various technologies are presented in Table 5.3. First, discrete-event process simulators replicate production work-flows and, when coupled with optimisation algorithms such as genetic algorithms and recurrent neural networks such as bi-GRU with attention, generate synthetic data on flow and failure rates to identify optimal operating parameters [73]. In parallel, high-fidelity physics engines are often implemented in the form of Simulink models derived from SysML system designs that reproduce equipment dynamics, including robot trajectories, fluid and thermal flows, and sensor outputs, which are then fed into machine learning pipelines to build predictive models [25]. To capture the interactions between systems, co-simulation frameworks based on standards such as the Functional Mock-Up Interface or High-Level Architecture coordinate multiple domain-specific simulators (mechanical, electrical, control) in real time, enabling holistic simulation analysis of entire production cells [35]. Finally, AI and RL enable large-scale virtual experimentation and safe transfer: a Deep Reinforcement Learning (DRL) agent interacts continuously within a high-fidelity simulated robotic cell with precise kinematics, sensor models and control logic, exploring millions of action sequences to optimise goals such as capture success or cycle time without risk to the physical hardware [37]. After initial training, the CDT uses domain randomisation (varying lighting, friction, payload, etc.) and live feedback to bridge the reality gap, iteratively refining the policy so that the behaviours learned in silico remain robust at the time of deployment. Beyond RL, generative models such as GANs synthesise rare or faulty sensor data, such as bearing wear signatures or thermal excursions, to enrich the sparse real-world examples and enable the twin to train more accurate fault detection and prognostic models entirely from synthetic datasets [42].

At this final stage, DRL looks particularly promising because it enables safe, large-scale exploration of complex control strategies, promotes continuous policy improvement based on real-time feedback, and integrates seamlessly with synthetic data generation to improve fault tolerance and, above all, learn from its environment.

## 6. Cognitive Architectures

Cognitive architecture is a theory of the fixed mechanism and structures that underlie human cognition [34]. In essence, a cognitive architecture is a map of how cognitive processes (perception, memory, reasoning, etc.) are organised and integrated to produce behaviour. Researchers in cognitive science and AI use this concept to develop unified theories of cognition and to design intelligent agents. Historically, cognitive architectures have been explored both in biological contexts to explain how the human brain gives rise to mind and in artificial contexts to build AI systems with general intelligence. This exploration covers several paradigms, from classical symbolic models of the mind to connectionist models (neural networks), as well as hybrid approaches that aim to combine the two. In recent years, researchers have worked to extract the common core of many architectures into a single consensus framework represented in Figure 6 and known as the Common Model of Cognition [57] (previously the Standard Model of Mind [31]).
<figure><img src="image_7.png" /><figcaption>Figure 6: The Common Model of Cognition [57].</figcaption></figure>

The diagram shows how the common model of cognition works as a simple, interconnected system. At the centre is working memory, which holds everything you are thinking about at any given time. On one side is declarative memory, which provides facts and experiences when you need them, and on the other is procedural memory, which uses “if-then” rules to decide what to do next. Below that, the perceptual module turns raw sensory information into useful information, and the motor module turns decisions into actions. Each part communicates with working memory through its own channel, allowing the system to perceive the world, think, remember and act in a coordinated way. In the following sections, we first examine cognitive architectures from the perspective of neuroscience and psychology, then review the main architectures of artificial intelligence, before discussing applications in human-machine interaction, robotics and manufacturing.

## 6.1. Cognitive Architectures in Biological Systems

Early theories of cognitive architecture in psychology adopted an information processing perspective, largely independent of biology. The mind was seen as “software” running on the “hardware” of the brain, with researchers focusing on abstract cognitive structures (e.g. memories, rule-based processors) without reference to neuronal anatomy [51]. In this symbolic or *cognitivist* paradigm, cognition is seen as the manipulation of symbols according to formal rules, as in Allen Newell and Herbert Simon's physical symbol system hypothesis [10]. Classical symbolic architectures assume that complex thought emerges from sequences of discrete symbolic operations, like a digital computer. However, in the early 1980s, *connectionism* proposed a different approach: rather than symbols and explicit rules, cognition would emerge from networks of simple units (similar to neurons). These distributed parallel processing models automatically learn activation patterns between units, which more closely matches the biological functioning of the brain. Throughout the late XXe century, the debate opposed:

1. Symbolists, for whom thought is based on the manipulation of formal symbols.

2. Connectionists, for whom thought arises from the collective interaction of artificial neurons.

Some critics, notably Fodor and Pylyshyn, have questioned the ability of connectionist models to account for the complex structures and rules that seem to drive human thought [10].

To balance these points of view, hybrid models have emerged that incorporate both symbolic and subsymbolic representations. Cognitive architects have recognised that human cognition can involve several levels of representation: high-level symbolic reasoning and low-level associative learning. An important example is the Adaptive Control of Thought-Rational (ACT-R) architecture developed by John R. Anderson, which, although often considered a symbolic architecture, has explicit links to neuroscience [54]. ACT-R includes modules for different cognitive functions, e.g. a declarative memory module, a goal module, a visual perception module, each of which is mapped to specific brain regions (a design based on neuroimaging data). For example, in ACT-R, the MRI montage in Figure 7 shows the visual module (orange) on axial slices at z = -10 mm and -20 mm, corresponding to the occipital cortex, while the
goal module (green) is visible at z = +40 mm in the dorsolateral prefrontal cortex. The architecture uses rule-based production systems for cognition (“if-then” rules to model stages of thought), each memory chunk in ACT-R has an activation level that determines how easily it can be retrieved, and these activation levels spread between chunks much like neural signals in the brain. In fact, much of the research that went into developing ACT-R involved analysing the neurological evidence for the existence of different modules and their interconnections.

<figure><img src="image_8.png" /><figcaption>Figure 7: The cognitive architecture ACT-R and its mapping on brain regions [9].</figcaption></figure>

Another influential biological model is Global Workspace Theory (GWT), which provides a cognitive architecture for higher-order consciousness and cognition [63]. Originally proposed by Bernard Baars, GWT compares the brain to a theatre: many unconscious processes (backstage actors) compete for attention, and the “stage” of the global workspace is a limited-capacity memory where the winning information is broadcast to the whole system as conscious content. In this model, attention is the projector that brings certain information into the global workspace, making it globally available to otherwise separate brain modules. Cognitive processes such as decision making, working memory and problem solving are supported by this global diffusion, while once an element enters the workspace it can influence system-wide processes [63]. As a cognitive architecture, GWT provides a functional explanation of consciousness, which arises from the interaction of specialised and unconscious components in the brain. Its influence is evident in modern neurally plausible cognitive models. For example, Franklin’s LIDA architecture [16] implements GWT in software agents and remains a reference point for consciousness research in both neuroscience and AI.

## 6.2. Cognitive Architectures in Artificial Intelligence

In the field of AI, cognitive architectures serve as blueprints for the construction of AI agents that exhibit human-like intelligence or cognition. AI researchers have developed numerous architectures, generally classified into three categories: symbolic, subsymbolic (connectionist) and hybrid. These AI architectures are inspired by psychological models, but are implemented as computational systems that can be run on a computer. Below, we present each category and some key examples, detailing the theoretical and technical aspects of each.

### 6.2.1. Symbolic Architectures

Symbolic cognitive architectures, as explained in the previous section, come from the classical tradition of AI in symbol manipulation [28]. They are based on the principle that intelligence results from the execution of logical operations on symbolic representations (like a programme operating on data structures). Symbolic systems represent concepts using symbols that can be manipulated using a set of predefined instructions [28]. A canonical example is the Soar architecture, originally developed by Allen Newell, John Laird and Paul Rosenbloom as a unified theory of cognition and problem solving [32, 29]. Figure 8 illustrates the Soar cognitive architecture, which is organised around a single, global buffer memory (the central box) in which perceptual inputs are stored and from which actions are sent back to the environment [11]. In Soar, long-term knowledge is divided into three separate memories: procedural
memory (productions), semantic memory (general facts) and episodic memory (event records) alongside the unified working memory, rather than being encoded only as productions:

* The procedural memory contains all the *if-then* rules (productions) that correspond to the working memory in order to propose actions or new problem states.

* The semantic memory provides declarative facts when requested by the productions.

* The episodic memory provides traces of past experiences that serve as reference points for problem solving.

Whenever a production matches the contents of the working memory, it triggers to suggest an action or modify the state of the problem. If no production applies, i.e., in the event of a deadlock, Soar automatically creates a subgoal, represented by a new state in the working memory, and invokes its learning mechanism (illustrated on the left) to resolve it. Once successfully resolved, the fragmentation process compiles the reasoning trace into a new production, which is stored in the procedural memory for future use.

<figure><img src="image_9.png" /><figcaption>Figure 8: The Soar cognitive architecture [11].</figcaption></figure>

Soar has demonstrated its ability to simulate a wide range of cognitive tasks [49], from simple puzzle solving to complex decision making and can even approach human response times for certain tasks. Newell [49] positioned Soar as the basis for a unified theory of cognition, demonstrating that with the appropriate knowledge, the same architecture could model behaviour in various domains: language use, reasoning, spatial navigation, etc. Technically, Soar's rule-based engine and its use of universal subgoals make it a powerful problem-solving tool that searches through possible actions until the goals are achieved. Soar continues to be extended to include reinforcement learning, semantic memory, mental imagery modules, etc., but it remains essentially a symbolic problem space architecture. Another well-known symbolic architecture is ACT-R, mentioned above in the biological context (Section 6.1), but also used in AI. In AI applications, ACT-R provides a programming framework for modelling human reasoning and memory [11]. For example, AI systems have been built using ACT-R to simulate human multitasking, driving a car, or solving algebra problems, allowing the system to behave similarly to human error and timing. Symbolic architectures such as Soar and ACT-R are valued for their interpretability, the ability to inspect symbolic rules and structures to understand the agent's reasoning, and the facility with which they integrate prior knowledge via rules. However, they are often criticised for their fragility and difficulty in adapting to the noise and complexity of the real world, which is where sub-symbolic learning can help.

### 6.2.2. Subsymbolic Architectures (Neural Networks and Emergent Models)

Unlike symbolic approaches, sub-symbolic architectures don't use explicit rules and symbols, instead, they use neural-like units and weighted connections as the basis for cognition [28]. These architectures are built on Artificial Neural Networks (ANN), which learn and compute by activating and transforming patterns. The terms sub-symbolic, neural, and emergent architectures are often used interchangeably, as they all refer to systems where cognitive abilities arise from the interaction of numerous simple processing units rather than from explicit symbolic manipulation.
They are called emergent because cognition is not pre-programmed but emerges from the distributed dynamics and adaptive learning processes of neural networks. Traditional AI neural networks (such as multilayer perceptrons or recurrent neural networks) typically focused on narrow tasks, but some researchers have designed large-scale neural architectures aimed at broader cognitive functions. A notable example is Semantic pointer architecture unified network (Spaun), a cognitive architecture that is essentially a functional simulation of a brain [12]. Spaun, developed by Chris Eliasmith and colleagues [64], consists of approximately 2.5 million spiking neurons organised into modules reflecting brain regions (visual cortex, prefrontal cortex, basal ganglia, etc.). As shown in Figure 9, these modules interact via information projections (solid black lines) and flow control projections (dashed blue lines), coordinated by a central action selection network. Spaun is able to perform several cognitive tasks: handwritten digit recognition, sequence memorisation, simple reasoning and even RL, by receiving only visual inputs (images of handwritten digits) and producing outputs via a physically modelled arm.

Internally, Spaun's design incorporates principles from neuroscience: for example, it uses a spiking neural model where neurons communicate via discrete pulses and employs the Neural Engineering Framework to encode and decode information into neural discharge patterns. The key to Spaun lies in the concept of semantic pointers, a neural representation that allows complex information to be compressed. Technically, Spaun demonstrates how a single neural architecture can exhibit a variety of cognitive behaviours by changing context: without any modification to the code, the same network can handle perception, working memory, decision-making and motor control in different tasks.

<figure><img src="image_10.png" /><figcaption>Figure 3.20: High-level architectural diagram of the Spaun model.</figcaption></figure>

<caption>Figure 9: The Spaun cognitive architecture [64].</caption>

This is a major achievement for subsymbolic AI, demonstrating the viability of brain-inspired architecture in large-scale simulation. More generally, modern DL architectures (such as deep convolutional and recurrent networks) can be considered subsymbolic cognitive architectures when designed to handle multiple cognitive functions. For example, a DRL agent that processes vision and produces actions, such as DeepMind's Atari game network [44], follows a cognitive architecture model: a pipeline of neural networks transforming sensory inputs into motor outputs, modulated by learned value signals. However, most DL systems lack the cognitive scope and modularity of classical cognitive architectures.

Despite their successes, purely subsymbolic architectures have limitations in abstract reasoning, structured problem solving, and integration of prior knowledge. This has led to the emergence of hybrid cognitive architectures in the field of AI, which integrate symbolic and subsymbolic techniques.

### 6.2.3. Hybrid Architectures (Combining Symbolic and Neural)

Hybrid architectures attempt to achieve the best of both worlds by combining explicit symbolic reasoning with the learning and robustness of neural networks [28]. A notable hybrid architecture is CLARION by Ron Sun [65], which is explicitly designed around the distinction between explicit and implicit cognition. In CLARION, cognitive
processes are divided into two levels: a higher level that uses symbolic representations via symbolic rules and chunks, and a lower level that uses subsymbolic representations via distributed neural networks. For example, the Action-Centred Subsystem (ACS) of CLARION illustrated in Figure 10 may include a lower-level neural network that learns skills through trial and error, and a higher-level rule system that contains explicit strategies; the two levels interact and can learn from each other's experience. This dual-process structure is inspired by psychological theories that humans possess both intuitive, unconscious skills and conscious, declarative knowledge. CLARION also includes a Non-Action-Centric Subsystem (NACS) for semantic knowledge that includes a motivational subsystem for drives and rewards and a metacognitive subsystem for higher-level control, making it very comprehensive.

<figure><img src="image_11.png" /><figcaption>Figure 10: The CLARION cognitive architecture [11].</figcaption></figure>

The technical aspect of CLARION lies in its ability to reason, for example by following rules, while at the same time improving these rules or acquiring new ones through lower-level neural learning. This makes it possible to model how a person or an agent can progressively automate a skill: initially their performance may follow explicit instructions (higher level), but with practice the knowledge becomes implicit (encoded in lower level networks). CLARION has been used to simulate cognitive tasks and even social situations, demonstrating the flexibility of hybrid architectures.

Sigma, developed by Paul Rosenbloom and colleagues, is another hybrid architecture [56]. Sigma is designed to unify several AI paradigms using a graphical model-based [27] approach. In Sigma, all cognitive operations, from symbolic reasoning and probabilistic inference to continuous signal processing, are formulated as message passing in a factorial graph. In essence, Sigma's engine generalises logic and probability into a single mathematical framework, allowing the architecture to deal natively with uncertainty, learning and discrete structures. Sigma's design goals include *grand unification*, covering cognition, perception, and motor control in a single architecture, and *functional elegance*, meaning that it strives to derive many capabilities from a small set of fundamental principles. Specifically, Sigma represents knowledge using predicates and conditionals [55], like a symbolic AI system, but implements them in the background as factorial graphs that can also encode probabilities and utilities. A single inference algorithm, a synthesis of logical and Bayesian updating, drives the whole system. This allows Sigma, for example, to perform probabilistic reasoning about rules or to integrate perceptual signals into symbolic memory. Although still under development, Sigma has already demonstrated examples such as visual scene understanding combined with cognitive queries, and aims to address aspects such as emotions and personality by integrating them as additional factors in the model.
Beyond CLARION and Sigma, there are many other hybrid architectures. For example, Soar itself has been enhanced with neural learning modules; ACT-R can be considered hybrid due to its subsymbolic activation equa-tions. Hybrid approaches are now common in cognitive systems research, reflecting the view that human intelligence probably needs both robust learning from data (a strength of neural networks) and abstract reasoning with structure (a strength of symbolic AI). Technically, designing a hybrid architecture involves specifying how the symbolic and sub-symbolic parts interact. For example, does the neural component provide suggestions from which the symbolic component chooses, or does the symbolic component provide goals for the neural network to learn? Different ar-chitectures give different answers. But all share the idea that a single paradigm is insufficient to cover the breadth of cognition. As Sun (2025) argues [66], the integration of neural and symbolic processes can also shed light on psychological theories of dual processes, providing a computational embodiment of System 1: fast, intuitive thinking, and System 2: slow, deliberative thinking, within the same cognitive system.

These architectures are essentially cognitive operating systems to which specific skills or knowledge can be added, making them attractive for practical applications, as we will see below.

### 6.3. Real Implementation of Cognitive Architectures in AI Agents

Cognitive architectures are not just theoretical constructs: they have been applied in various fields to stimulate intelligent behaviour in machines and model human performance. Below, we highlight applications in the fields of Human-Machine Interaction (HMI), robotics and industrial manufacturing.

#### 6.3.1. Human-Machine Interaction

Cognitive architectures are increasingly used in the field of HMI to model user cognition and improve interface design in order to understand how humans interact with automated systems. Researchers have built an ACT-R cog-nitive model to simulate a driver taking control of an autonomous vehicle [59]. This model reproduces the sequence of mental steps required to regain situational awareness during a control transfer and has helped explain the wide variability in driver response times. Such cognitive simulations are valuable for identifying factors that influence HMI performance, such as interface alerts and user distractions, and for designing safer embedded interfaces.

Another application is the development of HDTs for industrial operators. In 2023, an HDT of a chemical plant control room operator was developed using the ACT-R architecture [6]. This cognitive twin can mimic an operator's monitoring and decision-making behaviour in abnormal situations such as process disturbances. In experiments with 426 simulated scenarios, the ACT-R-based twin showed attention patterns similar to those of humans and validated against eye-tracking data from real operators. The result shows that ACT-R can be used successfully to simulate human-like operator behaviour. However, more work is needed to enable emulating the behaviour and actions of a specific individual.

These models demonstrate the potential of cognitive architectures to predict human responses to interface alerts and to aid training and evaluation. For example, the HDT can generate a large database of human responses to rare emergency situations, helping designers identify interface flaws and train novice operators in multiple alarm management.

#### 6.3.2. Robotics

We propose that the next generation of autonomous robots should be driven by brain-inspired cognitive architec-tures based on the fundamental principles of developmental robotics. Developmental robotics is a two-decade-old interdisciplinary field that integrates robotics, developmental psychology, and neuroscience [41, 60, 43]. Unlike in-dustrial manipulators, whose behaviour is fixed and limited to controlled environments, developmental robots function as embodied models of human ontogeny. They progressively acquire sensorimotor and cognitive skills through explo-ration and social interaction, use lifelong learning to adapt to new situations, and autonomously generate goal-directed actions in open environments. The aim is not to mimic the optimised performance of the mature adult brain, but to reproduce the progressive process of knowledge acquisition and self-organisation that leads to autonomous decision making [58].

For example, the Gentlebots project has developed a cognitive architecture for autonomous social robots to per-form domestic tasks [40]. Initially based on behaviour, the architecture has been extended to include symbolic mem-ory and planning capabilities to handle complex human-robot interaction scenarios. The latest version uses behaviour
trees for low-level control and a distributed memory graph for high-level knowledge. It has been successfully tested in real-world service robotics competitions such as RoboCup@Home and the European Robotics League [40]. These competitions have demonstrated the effectiveness of the architecture in tasks such as domestic service, where the robot needs to perceive its environment, plan actions (e.g. picking up objects) and interact naturally with people.

Another important effort is in Human-Robot Collaboration (HRC) for industry. A recent example is the DAC-HRC architecture [17], which aims to optimise teamwork between human workers and collaborative robots in a factory. Based on cognitive theory (distributed adaptive control), DAC-HRC combines several cognitive modules for perception, goal management, learning, etc. to enable features such as joint intention and interdependence. It has been used in an intelligent recycling plant to coordinate humans and robots involved in dismantling electronic waste. Pilot studies have shown that the robot adapts to each human partner through turn-taking, personalised error handling, adaptive safety measures and even gesture-based communication. This case study demonstrates how cognitive architectures can provide robots with social awareness and real-time adaptability, significantly improving HRI in dynamic and unstructured environments.

Furthermore, the LIDA architecture has been extended with a hierarchical spatial memory to improve robot navigation in realistic environments [39]. Researchers have linked LIDA to ROS middleware [52] and demonstrated that a robot agent can localise and move in a simulated 3D world (Gazebo) with physical phenomena and sensor noise, an important step towards real-world deployment. Similarly, the Soar architecture has been used for robot control and planning; for example, a Soar-based algorithm for autonomous exploration of mobile robots has been proposed to manage mapping and decision making in unknown environments [36].

### 6.3.3. Manufacturing

In manufacturing, the application of cognitive architectures is part of the broader trend of cognitive manufacturing or Industry 4.0/5.0, where production systems are becoming more intelligent and adaptive [13]. Modern factories generate huge amounts of data from machine sensors, production logs and more, requiring complex real-time decision making. A study conducted in 2024 defines cognitive manufacturing as the use of AI systems capable of reasoning from multimodal data and domain knowledge to manage the complexity of modern production. To achieve this, manufacturing research has turned to cognitive architectures to equip machines and software with advanced planning, diagnostic and learning capabilities similar to human cognition.

One area of application is logistics and autonomous operations in factories. The TROCA project (2019) [22] developed an autonomous transport robot for warehouses, controlled by a cognitive architecture called MECA. The robot's "artificial mind" was responsible for several tasks: navigating the facility, transporting packages, managing its own battery level, interacting with human operators and coordinating its work with other robots. The cognitive architecture enabled the robot to perform these multiple tasks. For example, it could explore the inventory, then move on to a delivery task, all the while monitoring its power and responding to human instructions. By integrating all these capabilities into a single cognitive framework, the TROCA robot illustrates how cognitive architectures can create flexible AGVs (Automated Guided Vehicles) or mobile robots that adapt to the changing needs of a production environment.

Another example of an application is the cognitive orchestration of manufacturing processes. Fischbach et al. [14] presented CAAI, a cognitive architecture for cyber-physical production systems. CAAI acts as an intelligent decision layer in a factory's software stack: it takes high-level goals from human engineers (e.g., the goal of optimising the throughput of a production line), selects the appropriate AI models/algorithms, and configures a data processing pipeline to achieve the goal. The architecture continuously monitors performance and can automatically adjust or change algorithms, for example changing a predictive maintenance model if it is underperforming. Due to its modular design using containers and streaming platforms, CAAI has been deployed on a big data platform and evaluated in a real use case in production, demonstrating its ability to reduce the effort of integrating AI into manufacturing.

Cutting-edge research is also exploring neuro-symbolic AI for manufacturing decisions, combining cognitive architectures with advanced machine learning. A 2024 study presented LLM-ACTR [70], a system that integrates the ACT-R cognitive architecture with a Large Language Model (LLM) to support manufacturing decision making. The LLM-ACTR approach was tested on a design-for-manufacturing problem (optimising an assembly process) and outperformed a standard LLM by reducing the "noisy" or unreliable reasoning that pure LLMs often produce. Essentially, the researchers used an ACT-R model to simulate human-like problem solving in manufacturing tasks, and then
integrated these structured decision models into an LLM. This hybrid cognitive system enabled more explainable de-
cisions closer to those made by humans, demonstrating the potential of combining data-driven AI with the structured
knowledge of cognitive architectures in complex manufacturing scenarios.

7. Research Directions and Roadmap

This section presents research directions related to the integration of cognition into a CDT. First, we explore the main functional challenges of a CDT: real-time state emulation, cognitive reasoning and learning capabilities, predictive simulation tasks, and the technical obstacles that each of these poses (Section 7.1). Next, we compare cognitive architectures (symbolic, sub-symbolic and hybrid) with other AI approaches, highlighting their respective strengths and weaknesses in the context of a CDT (Section 7.2). Finally, we then examine the benefits and limitations of using cognitive architectures, particularly in the three key application areas related to our case study: HMI, robotics, and manufacturing, to illustrate where they add value and where they have gaps (Section 7.3).

7.1. *Challenges related to functions in the CDT*

The emulation function of a DT is fundamental, as it ensures synchronisation between the physical entity and its
digital representation, forming the basis for high-level reasoning and predictive capabilities. The accuracy of emula-
tion has a direct impact on the reliability of CDT in real-time decision-making scenarios, particularly when the entity
is engaged in critical or dynamic tasks. Furthermore, in the case of human entities, the emulation process may require
semantic interpretation of high-level states, such as intentions or workload, derived from indirect measurements or
multimodal data fusion. This adds complexity to the emulation pipeline and often requires contextual modelling ap-
proaches that combine physical state estimation with behavioural inference. The technology chosen for this function
combines supervised machine learning with a knowledge graph database: sensor streams are classified and mapped
to a structured ontology, which instantly feeds the representation of the twin's state. The most challenging part is
semantic alignment, which involves ensuring that raw data is correctly mapped to high-level concepts in the graph, so
that the twin model remains consistent and meaningful as new data arrives. The main technological gaps relate to the
management of heterogeneous data formats and legacy systems (interoperability) and the updating of the ontological
structure as assets evolve.

The cognitive function enables a CDT to understand, reason and learn from the data reflected. The quality of
this cognitive layer has a direct impact on the twin's ability to support decisions and deal with new situations. Its
implementation requires managing a constantly evolving knowledge base and choosing reasoning methods that com-
bine transparency and flexibility. Using cognitive architectures, the CDT transforms real-time data into structured
knowledge, detects anomalies, suggests corrective actions and refines its own behaviour. Perhaps the most interesting
challenge is to keep this internal knowledge base both complete and up-to-date: as new models emerge or system com-
ponents change, the architecture must automatically learn new rules or concepts without human intervention, while
continuing to reason fast enough to support real-time decisions. The benefits and limitations of cognitive architecture
are explored in depth in a dedicated subsection later on.

Beyond reproducing the current state, CDTs use simulation to predict future states or test scenarios. The simulation function allows a CDT to execute virtual scenarios and predict future states based on its cognitive knowledge. Detailed models can predict failures or test control strategies, but they can be slow and resource-intensive. Simpler models are faster, but may miss important details. It is important to keep simulations in sync with real-time data and feed their results back to the cognitive layer to avoid misleading predictions. Designing this function requires choosing the right balance between fidelity and speed, and using scalable computing resources (cloud, edge) to provide reliable and timely scenario analysis. Thanks to DRL, the technology chosen for this function, the CDT can run hypothetical scenarios to predict future states and optimise strategies. Its main obstacle is the transfer from simulation to reality: policies designed in a virtual environment often fail when confronted with the chaotic and noisy reality of physical systems.
## 7.2. Cognitive Architectures vs. Alternative Approaches

A cognitive architecture is a software framework that integrates essential cognitive functions into an autonomous agent model. It provides a means to implement the cognitive functions of a CDT. By integrating such an architecture into a DT, it can interpret sensor data, update its knowledge, make decisions and learn over time in a human or intelligent way, rather than simply reflecting static data.

The integration of a cognitive architecture for CDTs becomes important for systems that display cognitive behaviour themselves (e.g. human operators). Indeed, if a real-world entity involves complex decision making (a human operator, an intelligent machine, etc.), its CDT must possess cognitive characteristics to faithfully mimic its behaviour. Several categories of cognitive architectures can fulfil this role, each offering different advantages:

* Symbolic cognitive architectures are preferred when a CDT needs to follow human-like problem-solving steps or explain its reasoning, as this type of architecture models cognition using symbolic representations and rule-based processing, closely mirroring human logical reasoning and declarative knowledge structures.

* Subsymbolic architectures, often based on neural networks or deep learning, abandon explicit symbolic reasoning and instead learn cognitive behaviours from data. These emerging models perform well at pattern recognition and adaptation from large amounts of data, which is well suited for CDTs that need to process high-volume sensor flows or learn complex patterns.

* Hybrid architectures that integrate both symbolic and sub-symbolic components, with the aim of capturing low-level learning and high-level reasoning in a single framework, are particularly interesting for CDTs, as they allow abstract reasoning while continuing to learn from raw data streams, thus balancing the interpretability of symbolic models with the adaptability of neural networks.

Table 4 summarises the main differences between cognitive architectures and traditional AI approaches, highlighting when each is more suitable depending on the cognitive scope and the type of reasoning required in the CDT.

Regarding table 4, the choice between a full cognitive architecture and another AI approach in a CDT depends on the cognitive scope requirements of the scenario. Based on the characteristics of cognitive architectures, we can conclude that they perform well in scenarios where a wide range of cognitive functions need to work together. They provide an integrated agent with perception, memory, decision making and learning subsystems, which is ideal when the CDT needs to mimic complex problem solving processes or human-like responses, as is the case with an HDT. If we tried to recreate this cognitive scope without an architecture, we would end up assembling many disparate models: one for anomaly detection, one for decision logic, one for planning learning updates, etc., which can be fragile and difficult to manage.

On the other hand, other cognitive models and techniques may be more appropriate when the required “cognition” of the twin is limited or when data-driven accuracy is the top priority. For example, if the primary function of a CDT is predictive analytics to predict the remaining lifetime of a machine, a DL model or a statistical ML model could perform this specific function with greater accuracy and less development effort than implementing a full cognitive architecture. In such cases, the twin can still be considered “cognitive” in the broad sense, as it has an acquired capability, but it does not require a comprehensive architecture coordinating multiple cognitive capabilities. The CDT can also be based on commonly understood decision rules. In a scenario where domain knowledge can be explicitly encoded, a rule-based engine may be sufficient to guide the actions of the CDT without the overload of a cognitive architecture.

## 7.3. Benefits and Limits of cognitive architectures applications

Cognitive architectures offer rich and explainable modelling of human mental processes that present cognitive functions, but they suffer from computational constraints, latencies, and development and maintenance complexity that can slow their adoption. Their extensibility and interoperability remain limited, and they struggle to reproduce certain aspects of human cognition, such as creativity, emotion, and large-scale continuous learning. The benefits and limitations of these architectures are examined in detail for each application case (HMI, robotics and manufacturing) in the following subsections.
<table><caption>Table 4: Comparison between Cognitive Architectures and AI methods for CDTs.</caption><thead><tr><td>Criterion</td><td>Cognitive Architectures (CA)</td><td>AI Methods</td></tr></thead><tbody><tr><td>Main purpose</td><td>Integrates multiple cognitive functions (perception, memory, reasoning, learning, decision-making) into one coherent agent.</td><td>Performs a specific cognitive task (e.g., prediction, classification, control) with high accuracy.</td></tr><tr><td>Nature of cognition</td><td>Human-like, interpretable, and context-aware cognition.</td><td>Task-oriented, data-driven cognition focused on optimization.</td></tr><tr><td>Learning mechanism</td><td>Combines symbolic reasoning and adaptive learning (hybrid) or emergent learning (neural).</td><td>Primarily statistical or deep learning from large datasets.</td></tr><tr><td>Knowledge representation</td><td>Uses explicit knowledge structures (rules, symbols) and/or implicit representations (neural activations).</td><td>Relies mainly on implicit data representations learned from examples.</td></tr><tr><td>Scope of application</td><td>Broad: needed when several cognitive capabilities must work together or when human-like cognition is required, such as in HRC or HDT-CDT interaction.</td><td>Narrow: suited for isolated functions (e.g., prediction, control, or classification) where human-like reasoning is not required.</td></tr><tr><td>Explainability</td><td>High (especially in symbolic or hybrid architectures).</td><td>Often limited (black-box models).</td></tr><tr><td>Adaptability</td><td>Learns and adapts continuously across multiple tasks.</td><td>Learns per task; retraining often needed for new conditions.</td></tr><tr><td>Complexity and development effort</td><td>Higher: requires modelling multiple cognitive processes and integration.</td><td>Lower: faster to deploy for a well-defined task.</td></tr><tr><td>Example use case</td><td>HRC, decision support, autonomous reasoning, HDT-CDT interaction.</td><td>Fault prediction, anomaly detection, optimization.</td></tr></tbody></table>

### 7.3.1. For Human-Machine Interaction

In the field of HMIs, the use of cognitive architectures as predictive models of users is recommended. These models provide insight into users' cognitive load, attention and decision-making processes, enabling the design of interfaces that are adapted to human capabilities. Benefits include improved safety by anticipating human errors, optimised interface design based on expected cognitive load, and personalised assistance or training by virtual cognitive agents. Cognitive architectures thus bridge the gap between human factors and AI, promoting the design of interfaces that take human cognition into account from the outset.

One of the main challenges is the accuracy of cognitive models. Developing an accurate ACT-R or Soar model of a user requires a deep understanding of human behaviour and extensive validation. Models may not generalise to all users and require calibration or fine-tuning to reflect the behaviour of an individual. Cognitive processes are influenced by the physiological state but cognitive architectures are rarely modelling this interdependence. In addition, cognitive architectures can be very demanding on computational resources, posing challenges for real-time adaptation in interfaces. Despite these challenges, HMI case studies show that even simplified cognitive models can improve system design by making the human-in-the-loop more predictable and understandable.
### 7.3.2. For Robotics

Recent research in robotics is moving towards flexible, human-centred autonomy. Cognitive architectures are facilitating this evolution by providing robots with memory, context understanding and learning capabilities that are lacking in traditional robotic programmes. A clear trend is emerging with the emergence of hybrid architectures that combine reactive behaviours for real-time response with deliberative reasoning for planning and learning [40]. The result is robots that can perform both low-level sensorimotor tasks and high-level cognitive tasks such as goal reasoning within the same system. The benefits include improved autonomy in unstructured environments, where robots can reason in novel situations; easier human-robot collaboration, where robots follow interaction protocols similar to those of humans; and reusability, where cognitive modules can often be reused for different robots and different tasks.

However, the application of cognitive architectures to physical robots poses challenges. One is real-time performance, where cognitive reasoning can be slow or resource-intensive, which is problematic for robots that need to react in fractions of a second for safety reasons. Robustness is another issue, as complex cognitive systems can fail unpredictably when faced with sensor noise or unexpected situations. Integration is also not trivial, as researchers need to connect abstract cognitive modules to real robot hardware and sensors (as shown in the LIDA+ROS example [39] in Section 6.3.2).

### 7.3.3. For Manufacturing

Cognitive architectures in manufacturing are contributing to intelligent and adaptive production systems by providing high-level reasoning and learning capabilities that complement physical machines and IoT infrastructure. We are seeing the emergence of cognitive manufacturing systems that can diagnose their own faults, optimise workflows and collaborate with human workers in decision-making processes. The benefits include greater flexibility, as production lines can be intelligently reconfigured according to objectives without the need for manual reprogramming. Systems acquire a form of memory and learning; for example, a cognitive planner can remember past disturbances and improve the robustness of planning so manufacturing systems can better anticipate human actions or needs, resulting in more effective control of human intervention.

However, manufacturing environments are complex and safety-critical, creating challenges for the deployment of cognitive architectures. One challenge is integration with existing machines and data when cognitive system need to interface with different devices and protocols on the shop floor. It is also important to ensure reliability and real-time performance; a cognitive decision module must not make dangerous decisions on a fast-moving production line. There is also the issue of trust and transparency, where operators need to understand the decisions of a CDT, thus the interest in explainable hybrids such as LLM-ACT-R (see Section 6.3.3).

After exploring the benefits and constraints specific to each application area, we will discuss the general limitations of integrating cognitive architecture into a CDT in the following subsection.

### 7.3.4. For Emulating Human Behaviour

While cognitive architectures are inspired by the functioning of the human mind, they are not necessarily created to reproduce the behaviour of a human individual. Indeed, many seek to enhance engineered systems with human-like cognition. Cognitive architectures that focus on human behaviour are a powerful tool to simulate the behaviour of humans, making them a useful to, for instance, assess the cognitive load induced by a user interface. However, they rarely seek to account for behavioural differences between individuals which can be rooted in their personality, preferences and past experiences.

A non-personalised cognitive architecture may still be valuable for simplified use cases where the environment is controlled and the participants thoroughly briefed about their task. To realise the long term vision of an HDT companion, personalised models that learn the individual's behaviour are required. Research on learning mechanisms to adapt cognitive architectures to an individual is limited. For instance, ACT-R can be personalised by adapting model parameters like memory decay and retrieval speed or adapting production rules, but there is no generally established and accepted calibration procedure. Robust calibration procedures represent a step towards model personalisation, but ideally models would be trained directly on the observed behaviour and actions of the twinned individual. This would enable models to continuously learn the behaviour of their twin.

It is important to recognize the complexity of humans as a biological and cognitive system. Physiological and cognitive processes influence each other continuously, but cognitive architectures overwhelmingly focus on cognitive
processes. In contrast, embodied cognition considers a close interaction between motor and sensory functions of the body and cognitive processes of the mind. Foglia and Wilson express this concisely: "*Consequently, the body intrinsically constrains, regulates, and shapes the nature of mental activity.*" [15]. For realistic emulation, cognitive architectures may be extended to include the influences of physiological processes to move towards a holistic human model. This ambition should be considered as very-long term as both human brain and body are only partially understood and are subject to centuries of continuous research efforts with no end in sight.

As well as the technical challenges associated with personalisation and physiological integration, these developments raise important ethical questions. The collection and modelling of human cognitive and physiological data inevitably raises issues of privacy, consent, and ownership of the data. Sensitive information such as neural patterns, stress responses or behavioural tendencies could be misused or introduce unintended biases into model training. Furthermore, if models are poorly generalised to the entire population, individual differences embedded in this data may amplify inequalities. Therefore, it is essential to ensure transparency, fairness, and security in the processing of personal data, so that future human-oriented cognitive architectures respect ethical and societal values.

## 8. Conclusion

Cognitive interoperability is the foundation for cooperative and intelligible action between people and CPS, a foundation that is becoming increasingly important as we move towards human-centric AI integration in industry. It is achieved through the interaction of these cognitive twins (CDTs & HDTs), which creates a shared cognitive space in which humans and AI can understand each other's state and coordinate their actions. In the context of Industry 5.0, this means that technology enhances the capabilities of human workers rather than replacing them, resulting in safer, more efficient, and more resilient operations. This article uses the CDT and the HDT as a unifying framework to represent and connect physical systems and human cognitive processes within a CPE.

CDTs extend traditional DT models by incorporating cognitive capabilities using various techniques. Essentially, a CDT does not simply simulate a physical asset or process, but can emulate the state of the real system in real time, reason cognitively about that state (and learn from it), and simulate future scenarios or behaviours to aid decision-making. Emulation and simulation are complementary functions in a CDT, and state-of-the-art frameworks often incorporate both. A useful distinction is that emulations model the current or past behaviour of the physical twin, while simulations model the future or hypothetical behaviour. Emulation is about real-time accuracy and synchronisation, while simulation is about exploration and prediction. Emulation generally requires close coupling to hardware (sensors/PLC), real-time data processing, and sometimes real-time operating constraints (soft real-time). Simulation, on the other hand, can run faster or slower than real-time and is not required to reproduce exactly what the physical system is doing at the moment, but is used to evaluate different scenarios. On the other hand, HDTs model each individual uniquely, inferring their internal states (such as intentions and workload) from indirect, noisy and heterogeneous data (e.g. from physiological sensors, behaviour trackers and manual HMI inputs). HDTs must therefore align stimuli and actions semantically with limited direct measurements, reason about high-level latent variables and safely co-simulate human-machine scenarios without direct human control. All human feedback loops operate via HMI-mediated stimuli, which adds a level of complexity not found in conventional CDTs.

The design of the cognition function of a CDT and HDT involves choosing an architecture for its cognitive functionalities. Cognitive architectures are a field of research and technological development in which the processes of human cognition (cognitive functions) are formally modelled and computationally instantiated. By building and validating comprehensive frameworks that approximate the functional organisation and information processing mechanisms of the human brain, researchers aim to generate artificial agents capable of adopting adaptive, goal-directed behaviour in diverse work environments. This article reviews the theoretical foundations and design principles of different types of cognitive architectures (symbolic, emergent, and hybrid), evaluates their contribution to AI and the empirical study of natural intelligence, and examines their role in a CDT.

AI research has produced a rich landscape of cognitive architectures. Symbolic architectures, such as Soar and ACT-R, focus on structured and explicable reasoning; subsymbolic architectures, such as Spaun, exploit neural computation for brain-like learning and parallelism; and hybrid architectures, such as CLARION and Sigma, aim to cover the full spectrum of intelligence by unifying symbolic and neural paradigms. Each architecture comes with theoretical commitments (e.g., is cognition rule-based or emergent?) and technical implementations (production system, neural network, factorial graph, etc.), but they all aim at a common goal: an integrated model of an intelligent and cognitive
agent capable of performing a wide range of cognitive tasks using general principles. In all cases, cognitive architecture serves as a model for cognition, a unifying structure in which perception, memory, decision making and learning modules work together in cycles similar to those of a human or intelligent agent. This allows the cognitive function of a CDT to be systematically implemented when it needs to mimic human-like capabilities.

Cognitive architectures give CDTs autonomy and adaptability. A CDT that can understand contextual data and apply acquired knowledge can go beyond passive reflection to actively predict and respond to change. As a recent study shows, CDTs that incorporate cognitive functions become more self-adaptive and autonomous, opening up new applications that were not possible with traditional twins. By integrating cognitive reasoning, a CDT can predict future states or problems and take proactive action. In areas such as Industry 4.0/5.0, this means that CPSs managed by CDTs can self-optimise and handle new situations without the need for constant human intervention.

In summary, cognitive architectures should be used when a CDT requires full human-like cognition, i.e. multiple cognitive processes working together, reasoning in unfamiliar situations and being able to explain its decisions. Scenarios include virtual assistants for operators, autonomous robots in unpredictable environments, or any case where the role of the twin goes beyond prediction to active problem solving. Conversely, if the need is specific and well defined, such as classifying machine states, optimising a known process or applying simple rules, more limited AI techniques may be more appropriate. It should be noted that these choices are not mutually exclusive: it is possible to integrate specialised ML models within a cognitive architecture (for perception or pattern recognition), thus combining the strengths of both. The design of the CDT can therefore be hybrid, not only in terms of the type of architecture (symbolic or neural), but also in terms of the combination of a cognitive framework and AI algorithms. The guiding principle is to adapt the cognitive mechanism of the twin to the complexity of the task: use the richest architectures for open-ended, knowledge-intensive tasks, and simpler models for limited, data-intensive tasks.

Overall, cognitive architectures are helping to transform automated manufacturing into autonomous and cognitively informed manufacturing, in line with the Industry 4.0/5.0 vision of self-optimising and context-aware factories.

Despite these advances, the creation of an integrated CDT remains a challenge. A study conducted in 2024 [13] showed that no current system fully integrates all cognitive capabilities (perception, learning, reasoning, etc.) in a single manufacturing twin. The integration challenge covers both technical and organisational aspects: ensuring interoperability between heterogeneous components (ML models, simulation software, reasoning engines), meeting real-time constraints for control applications, and gaining the trust of human users (who must accept the recommendations of the twin). Research efforts are currently focused on developing standard architectures and middleware to simplify integration. There is also a push towards hybrid AI techniques (neuro-symbolic methods) that combine statistical learning and symbolic reasoning, which is ideal for CDTs. In short, while early implementations of CDTs including cognitive architectures offer clear benefits, from efficiency gains to improved decision support, further integration and validation work is required for full-scale industrial adoption.

## Acknowledgements

This work has been partially supported by the ANR French National Research agency and the FNR Luxemburgish National Research funds project AI4C2PS (INTER/ANR/22/17164924/AI4C2PS), 2023-2026.

## References

[1] Adl, A. E. (2016). The cognitive digital twins: Vision, architecture framework and categories.

[2] Al Haj Ali, J., Gaffinet, B., Panetto, H., and Naudet, Y. (2024a). Cognitive systems and interoperability in the enterprise: A systematic literature review. *Annual Reviews in Control*, 57:100954.

[3] Al Haj Ali, J., Gaffinet, B., Panetto, H., and Naudet, Y. (2025). From semantic interoperability to cognitive interoperability: Enabling human-cps collaboration in industry 5.0. In *International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2025)*. Springer.

[4] Al Haj Ali, J., Lezoche, M., Panetto, H., Naudet, Y., and Gaffinet, B. (2024b). Cognitive architecture for cognitive cyber-physical systems. *IFAC-PapersOnLine*, 58(19):1180–1185.

[5] Ayani, M., Ganebäck, M., and Ng, A. H. (2018). Digital twin: Applying emulation for machine reconditioning. *Procedia CIRP*, 72:243–248.

[6] Balaji, B., Shahab, M. A., Srinivasan, B., and Srinivasan, R. (2023). Act-r based human digital twin to enhance operators’ performance in process industries. *Frontiers in human neuroscience*, 17:1038060.
[7] Balasubramani, P. P., Ojeda, A., Grennan, G., Maric, V., Le, H., Alim, F., Zafar-Khan, M., Diaz-Delgado, J., Silveira, S., Ramanathan, D., et al. (2021). Mapping cognitive brain functions at scale. *NeuroImage*, 231:117641.

[8] Bocklisch, F. and Huchler, N. (2023). Humans and cyber-physical systems as teammates? characteristics and applicability of the human-machine-teaming concept in intelligent manufacturing. *Frontiers in artificial intelligence*, 6:1247755.

[9] Borst, J. P., Nijboer, M., Taatgen, N. A., van Rijn, H., and Anderson, J. R. (2015). Using data-driven model-brain mappings to constrain formal models of cognition. *PLoS One*, 10(3):e0119673.

[10] Card, S. K. and NEWELL, A. (1990). Cognitive architectures. In *Human Performance Models for Computer-Aided Engineering*, pages 173–179. Elsevier.

[11] Chong, H.-Q., Tan, A.-H., and Ng, G.-W. (2007). Integrated cognitive architectures: a survey. *Artificial Intelligence Review*, 28:103–130.

[12] Choo, F.-X. (2018). *Spaun 2.0: Extending the world's largest functional brain model*. University of Waterloo.

[13] El Kalach, F., Yousif, I., Wuest, T., Sheth, A., and Harik, R. (2024). Cognitive manufacturing: Definition and current trends. *Journal of Intelligent Manufacturing*, pages 1–21.

[14] Fischbach, A., Strohschein, J., Bunte, A., Stork, J., Faeskorn-Woyke, H., Moriz, N., and Bartz-Beielstein, T. (2020). Caai—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems. *The International Journal of Advanced Manufacturing Technology*, 111:609–626.

[15] Foglia, L. and Wilson, R. A. (2013). Embodied cognition. *Wiley Interdisciplinary Reviews: Cognitive Science*, 4(3):319–325.

[16] Franklin, S., Madl, T., D'mello, S., and Snaider, J. (2013). Lida: A systems-level architecture for cognition, emotion, and learning. *IEEE Transactions on Autonomous Mental Development*, 6(1):19–41.

[17] Freire, I. T., Guerrero-Rosado, O., Amil, A. F., and Verschure, P. F. (2024). Socially adaptive cognitive architecture for human-robot collaboration in industrial settings. *Frontiers in Robotics and AI*, 11:1248646.

[18] Gaffinet, B., Al Haj Ali, J., Naudet, Y., and Panetto, H. (2025). Human digital twins: A systematic literature review and concept disambiguation for industry 5.0. *Computers in Industry*, 166:104230.

[19] Gaffinet, B., Al Haj Ali, J., Panetto, H., and Naudet, Y. (2023). Human-centric digital twins: Advancing safety and ergonomics in human-robot collaboration. In *International conference on innovative intelligent industrial production and logistics*, pages 380–397. Springer.

[20] Garg, S., Gogoi, A., Chakraborty, S., and Hazra, B. (2021). Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system. *Probabilistic Engineering Mechanics*, 66:103173.

[21] Geraci, A. (1991). *IEEE standard computer dictionary: Compilation of IEEE standard computer glossaries*. IEEE Press.

[22] Gudwin, R., Rohmer, E., Paraense, A., Froes, E., Gibaut, W., Oliveira, I., Rocha, S., Raizer, K., and Feljan, A. V. (2020). The troca project: An autonomous transportation robot controlled by a cognitive architecture. *Cognitive Systems Research*, 59:179–197.

[23] Hamel, C., Ahsan, M. M., and Raman, S. (2024). Pmi-dt: Leveraging digital twins and machine learning for predictive modeling and inspection in manufacturing. *arXiv preprint arXiv:2411.01299*.

[24] Ivanov, D. (2023). Intelligent digital twin (idt) for supply chain stress-testing, resilience, and viability. *International Journal of Production Economics*, 263:108938.

[25] Jinzhi, L., Zhaorui, Y., Xiaochen, Z., Jian, W., and Dimitris, K. (2022). Exploring the concept of cognitive digital twin from model-based systems engineering perspective. *The International Journal of Advanced Manufacturing Technology*, 121(9):5835–5854.

[26] Johansen, S. T., Unal, P., Albayrak, Ö., Ikonen, E., Linnestad, K. J., Jawahery, S., Srivastava, A. K., and Løvfall, B. T. (2023). Hybrid and cognitive digital twins for the process industry. *Open Engineering*, 13(1):20220418.

[27] Koller, D. and Friedman, N. (2009). *Probabilistic graphical models: principles and techniques*. MIT press.

[28] Kotseruba, I. and Tsotsos, J. K. (2020). 40 years of cognitive architectures: core cognitive abilities and practical applications. *Artificial Intelligence Review*, 53(1):17–94.

[29] Laird, J. E. (2019). *The Soar cognitive architecture*. MIT press.

[30] Laird, J. E. (2022). Introduction to soar. *arXiv preprint arXiv:2205.03854*.

[31] Laird, J. E., Lebiere, C., and Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. *Ai Magazine*, 38(4):13–26.

[32] Laird, J. E., Newell, A., and Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. *Artificial intelligence*, 33(1):1–64.

[33] Lam, A. N., Svaland, G. B., Barcelona, M. Á., Keaveney, S., Mallouli, W., Nguyen, L., Belbachir, A., Ma, X., Srivastava, A. K., and Belbachir, A. N. (2024). Sindit: A framework for knowledge graph-based digital twins in smart manufacturing. In *IFIP International Internet of Things Conference*, pages 33–52. Springer.

[34] Lehman, J. F., Laird, J., Rosenbloom, P., et al. (2006). A gentle introduction to soar, an architecture for human cognition: 2006 update. *University of Michigan*, pages 1–37.

[35] Li, Y., Chen, J., Hu, Z., Zhang, H., Lu, J., and Kiritsis, D. (2022). Co-simulation of complex engineered systems enabled by a cognitive twin architecture. *International journal of production research*, 60(24):7588–7609.

[36] Lindes, P. (2022). *Constructing meaning, piece by piece: A computational cognitive model of human sentence comprehension*. PhD thesis, University of Michigan.

[37] Liu, Y., Xu, H., Liu, D., and Wang, L. (2022). A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping. *Robotics and Computer-Integrated Manufacturing*, 78:102365.

[38] Madani, H. M., Djezzar, M., Mounir, H., Zianou, A. S., and Maimour, M. (2022). Semantic models and machine learning approach in cps: A survey. In *SIoT-2022: International Workshop on Semantic IoT, Co-located with the KGSWC-2022*, pages 21–23.

[39] Madl, T., Franklin, S., Chen, K., Montaldi, D., and Trappl, R. (2016). Towards real-world capable spatial memory in the lida cognitive architecture. *Biologically Inspired Cognitive Architectures*, 16:87–104.

[40] Martín, F., Lera, F. J. R., Ginés, J., and Matellán, V. (2020). Evolution of a cognitive architecture for social robots: Integrating behaviors and symbolic knowledge. *Applied Sciences*, 10(17):6067.

[41] Metta, G., Sandini, G., and Konczak, J. (1998). A developmental approach to sensori-motor coordination in artificial systems. In *SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218)*, volume 4, pages 3388–3393. IEEE.
[42] Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., and Paczkowski, T. (2025). Generative ai in ai-based digital twins for fault diagnosis for predictive maintenance in industry 4.0/5.0. *Applied Sciences*, 15(6):3166.

[43] Min, H., Yi, C., Luo, R., Zhu, J., and Bi, S. (2016). Affordance research in developmental robotics: A survey. *IEEE Transactions on Cognitive and developmental systems*, 8(4):237-255.

[44] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. *arXiv preprint arXiv:1312.5602*.

[45] Mohammed, W. M., Haber, R. E., and Martinez Lastra, J. L. (2022). Ontology-driven guidelines for architecting digital twins in factory automation applications. *Machines*, 10(10):861.

[46] Naudet, Y., Al Haj Ali, J., Gaffinet, B., and Panetto, H. (2024). Cognition in digital twins for cyber-physical systems and humans: Where and why? In *International Conference on Innovative Intelligent Industrial Production and Logistics*, pages 399-412. Springer.

[47] Naudet, Y., Panetto, H., and Yilma, B. A. (2023a). Towards cognitive interoperability in cyber-physical enterprises. *IFAC-PapersOnLine*, 56(2):695-706.

[48] Naudet, Y., Panetto, H., and Yilma, B. A. (2023b). Towards cognitive interoperability in cyber-physical enterprises. *IFAC-PapersOnLine*, 56(2):695-706. 22nd IFAC World Congress.

[49] Newell, A. (1994). *Unified theories of cognition*. Harvard University Press.

[50] Panetto, H., Iung, B., Ivanov, D., Weichhart, G., and Wang, X. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. *Annual reviews in control*, 47:200-213.

[51] Petersen, S. E. and Sporns, O. (2015). Brain networks and cognitive architectures. *Neuron*, 88(1):207-219.

[52] Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A. Y., et al. (2009). Ros: an open-source robot operating system. In *ICRA workshop on open source software*, volume 3, page 5. Kobe.

[53] Redelinghuys, A. J. H. (2020). *An architecture for the digital twin of a manufacturing cell*. PhD thesis, Stellenbosch: Stellenbosch University.

[54] Ritter, F. E., Tehranchi, F., and Oury, J. D. (2019). Act-r: A cognitive architecture for modeling cognition. *Wiley Interdisciplinary Reviews: Cognitive Science*, 10(3):e1488.

[55] Rosenbloom, P. S. (2010). Combining procedural and declarative knowledge in a graphical architecture. In *Proceedings of the 10th International Conference on Cognitive Modeling*, pages 205-210. Citeseer.

[56] Rosenbloom, P. S. (2013). The sigma cognitive architecture and system. *AISB Quarterly*, 136:4-13.

[57] Rosenbloom, P. S., Laird, J. E., Lebiere, C., Stocco, A., Granger, R. H., and Huyck, C. (2024). A proposal for extending the common model of cognition to emotion. *arXiv preprint arXiv:2412.16231*.

[58] Sandini, G., Sciutti, A., and Morasso, P. (2024). Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents. *Frontiers in Computational Neuroscience*, 18:1349408.

[59] Scharfe, M. and Russwinkel, N. (2019). Towards a cognitive model of the takeover in highly automated driving for the improvement of human machine interaction. In *TC Stewart (Chair), Proceedings of the 17th International Conference on cognitive modelling. Waterloo, Canada: University of Waterloo*.

[60] Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. *Connection Science*, 18(2):173-187.

[61] Semeraro, C., Lezoche, M., Panetto, H., and Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. *Computers in Industry*, 130:103469.

[62] Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., and de Laat, M. (2022). Human and artificial cognition. *Computers and Education: Artificial Intelligence*, 3:100107.

[63] Signa, A., Chella, A., and Gentile, M. (2021). Cognitive robots and the conscious mind: a review of the global workspace theory. *Current Robotics Reports*, 2:125-131.

[64] Stewart, T., Choo, F.-X., and Eliasmith, C. (2012). Spaun: A perception-cognition-action model using spiking neurons. In *Proceedings of the Annual Meeting of the Cognitive Science Society*, volume 34.

[65] Sun, R. (2006). The clarion cognitive architecture: Extending cognitive modeling to social simulation. *Cognition and multi-agent interaction*, pages 79-99.

[66] Sun, R. (2025). Dual-process theories, cognitive architectures, and hybrid neural-symbolic models. *Neurosymbolic Artificial Intelligence*, 1:NAI-240720.

[67] Taylor, J. E. T. and Taylor, G. W. (2021). Artificial cognition: How experimental psychology can help generate explainable artificial intelligence. *Psychonomic Bulletin & Review*, 28(2):454-475.

[68] Ullah, A., Younas, M., and Saharudin, M. S. (2025). Transforming manufacturing quality management with cognitive twins: a data-driven, predictive approach to real-time optimization of quality. *Journal of manufacturing and materials processing*, 9(3).

[69] Wang, Y. (2012). On abstract intelligence and brain informatics: mapping cognitive functions of the brain onto its neural structures. *International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)*, 6(4):54-80.

[70] Wu, S., Oltramari, A., Francis, J., Giles, C. L., and Ritter, F. E. (2024). Cognitive llms: Towards integrating cognitive architectures and large language models for manufacturing decision-making. *arXiv preprint arXiv:2408.09176*.

[71] Zappa, S., Franciosi, C., Polenghi, A., and Voisin, A. (2024). Ontology-based digital twin for maintenance decisions in manufacturing systems: an application at laboratory scale. *IFAC-PapersOnLine*, 58(8):13-18.

[72] Zheng, X., Lu, J., and Kiritsis, D. (2022). The emergence of cognitive digital twin: vision, challenges and opportunities. *International Journal of Production Research*, 60(24):7610-7632.

[73] Zhu, X. and Ji, Y. (2022). A digital twin-driven method for online quality control in process industry. *The International Journal of Advanced Manufacturing Technology*, pages 1-20.