Model Of Cognitive Architecture Research Articles

Temporal heterogeneity in cognitive architectures

In 2020, Mc Fadden published an article in which he discusses how algorithms can be encoded in time and space. By analyzing the topology of the cytoarchitecture of the brain, cognitive architectures can understand the underlying mechanisms that have led to the development of human intelligence in space. In this study, our focus lies in investigating temporal heterogeneity as a mechanism that the brain could have developed not solely as a biological constraint, but also as an evolutionary advantage. To accomplish this, we employed virtual agents within a virtual environment and constructed a prototype cognitive architecture. Subsequently, we compared the benefits and drawbacks of having this cognitive architecture operate under a model of temporal heterogeneity versus one characterized by temporal homogeneity. At the conclusion of the article, we present the results obtained from two perspectives. From a quantitative standpoint, we contrast the agents’ adaptation to the environment based on the cognitive architecture model employed by each agent. On this front, we found evidence that temporal heterogeneity might be useful in finding parameter optimizations faster, amongst other benefits. From a qualitative perspective, we examine the potential of this model to explore the cognitive processes of the virtual agents, concluding that a different representation of percepts is needed, which we further discuss.

Cognitive Architecture of Cognitive Activity: Modeling and Psychophysiological Assessment

Abstract—The main approaches to modeling human cognitive activity and the underlying neural mechanisms are described. The systematization of cognitive architectures is given, and such popular models as ACT-R, SOAR, CLARION and CHREST is overviewed with examples of their practical application in psychology and neurophysiology. The use of the developed models of cognitive functions makes it possible to predict the effectiveness of perception and selection of information, which knowledge and procedures are required for the optimal solution of the problem, the expected error rate while task performing, and what functional brain system is used to organize behavior. Improvement and addition of existing models of cognitive architecture is considered as a prospect for the development of cognitive neuroscience, understanding the patterns of intelligence formation and the development of artificial intelligence.

The process of implementing rules in the proposed model of SOAR cognitive architecture in artificial intelligence PSO architecture

The process of implementing rules in the proposed model of SOAR cognitive architecture in artificial intelligence PSO architecture

Modeling Pilot Flight Performance in a Cognitive Architecture: Model Demonstration

Cognitive architecture models can support the simulation and prediction of human performance in complex human-machine systems. In the current work, we demonstrate a pilot model that can perform and simulate taxiing and takeoff tasks. The model was built in Queueing Network-Adaptive Control of Thought Rational (QN-ACTR) cognitive architecture and can be connected to flight simulators such as X-Plane to generate various data, including performance, mental workload, and situation awareness. The model results are determined in combination by the declarative knowledge chunks, production rules, and a set of parameters. Currently, the model can generate flight operation behavior similar to human pilots. We will collect human pilot data to examine further and validate model assumptions and parameter values. Once validated, such models can support interface evaluation and competency-based pilot training, providing a theory-based predictive approach complementary to human-in-the-loop experiments for aviation research and development.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing.

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed. A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability. This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model. Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays. The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed. This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

The meaningful-based cognitive architecture model of schizophrenia

In subsymbolic operation of the Meaningful-Based Cognitive Architecture (MBCA) the input sensory vector is propagated through a hierarchy of Hopfield-like Network (HLN) functional groups, is recognized and may associatively trigger in the instinctual core goals module as well as in groups of HLNs arranged as pre-causal and pattern memory, vectors propagated to the output motor group of HLNs which produce an output signal. In full causal symbolic operation, the processed sensory input vector is also propagated to the logic/working memory groups of HLNs, where it can be compared to other vectors in the logic/working memory, and produce various outputs in response. The processed sensory input vector can trigger in the instinctual core goals module intuitive logic, intuitive physics, intuitive psychology and intuitive planning procedural vectors, as well as trigger in the causal group of HLNs learned logic, physics, psychology and planning procedural vectors which are also sent to the logic/working memory groups of HLNs. These circuits can allow the MBCA to act causally on information it has never seen before. An example is given of a Python simulation where the MBCA which is controlling a legged robot causally determines that a shallow whitewater river will cause water damage to itself, while if the MBCA is acting associatively only and never having seen whitewater before and normally crossing shallow rivers, will cross the whitewater river and become damaged. While the MBCA does not attempt to replicate biological systems at the neuronal spiking level, its HLNs and the organization of its HLNs are indeed inspired by biological mammalian minicolumns and mammalian brains. The MBCA model leads to the hypothesis that in the course of hominin evolution, HLNs became co-opted into groups of HLNs providing more extensive working memories with causal abilities, unlike non-hominins. While such co-option of the minicolumns can allow advantageous causal symbolic processing integrated with subsymbolic processing, the order of magnitude of increased complexity required for such organization and operation, created a vulnerability in the human brain to psychosis, which does not occur with significant prevalence in non-humans.

Modeling Driver Take-Over Reaction Time and Emergency Response Time using an Integrated Cognitive Architecture

Drivers’ take-over reaction time in partially automated vehicles is a fundamental component of automated vehicle design requirements, and take-over reaction time is affected by many factors such as distraction and drivers’ secondary tasks. This study built cognitive architecture models to simulate drivers’ take-over reaction time in different secondary task conditions. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers’ task-specific skills and knowledge were programmed as production rules. A driving simulator program was connected to the models to produce prediction of reaction time. Model results were compared with human results in both single-task and multi-task conditions. The models were built without adjusting any parameter to fit the human data. The models could produce simulation results of take-over reaction time similar to the human results in take-over conditions with visual or auditory concurrent tasks, as well as emergency response time in a manual driving condition. Overall, R square was 0.96, root mean square error (RMSE) was 0.5 s, and mean absolute percentage error (MAPE) was 9%. The models could produce simulation results of reaction time similar to the human results from different task conditions. The production rules are plausible representations of drivers’ strategies and skills. The models provide a useful tool for the evaluation of take-over alert design and the prediction of driver performance.

Functional explanation in mathematics

Mathematical explanations are poorly understood. Although mathematicians seem to regularly suggest that some proofs are explanatory whereas others are not, none of the philosophical accounts of what such claims mean has become widely accepted. In this paper we explore Wilkenfeld’s (Synthese 191:3367–3391, 2014) suggestion that explanations are those sorts of things that (in the right circumstances, and in the right manner) generate understanding. By considering a basic model of human cognitive architecture, we suggest that existing accounts of mathematical explanation are all derivable consequences of Wilkenfeld’s ‘functional explanation’ proposal. We therefore argue that the explanatory criteria offered by earlier accounts can all be thought of as features that make it more likely that a mathematical proof will generate understanding. On the functional account, features such as characterising properties, unification, and salience correlate with explanatoriness, but they do not define explanatoriness.

Visual Search Without Selective Attention: A Cognitive Architecture Account.

A key phenomenon in visual search experiments is the linear relation of reaction time (RT) to the number of objects to be searched (set size). The dominant theory of visual search claims that this is a result of covert selective attention operating sequentially to "bind" visual features into objects, and this mechanism operates differently depending on the nature of the search task and the visual features involved, causing the slope of the RT as a function of set size to range from zero to large values. However, a cognitive architectural model presented here shows these effects on RT in three different search task conditions can be easily obtained from basic visual mechanisms, eye movements, and simple task strategies. No selective attention mechanism is needed. In addition, there are little-explored effects of visual crowding, which is typically confounded with set size in visual search experiments. Including a simple mechanism for crowding in the model also allows it to account for significant effects on error rate (ER). The resulting model shows the interaction between visual mechanisms and task strategy, and thus it represents a more comprehensive and fruitful approach to visual search than the dominant theory.

Research on new concept cognitive satellite navigation system

According to the spectrum division given by the International Telegraph Union (ITU), the current satellite navigation frequency space is particularly crowded. In addition to GLONASS, the four satellite navigation systems partially or completely overlap the frequency bands of GPS, Galileo and COMPASS systems, resulting in satellite navigation signal interference exists objectively and inevitable. Although the new navigation signal design system provides a method of alleviating the mutual interference of satellite navigation signals, however, it can not fundamentally solve the problem of satellite navigation signal compatibility in the long run. Aiming at the demand of compatibility of global satellite navigation system and taking the work pattern and mechanism of human brain as a reference, this paper proposes the basic concept and connotation based on cognitive satellite navigation system and constructs the cognitive satellite navigation system architecture model and information closed-loop life cycle. The electromagnetic environment characteristics and signal compatibility characteristics into the overall structure of the system in order to make the system performance changes with the electromagnetic environment and spectral characteristics of different adaptive changes, in theory, put forward a high-performance environmental model parameter perception Satellite navigation system architecture model, which is the future direction of satellite navigation system development.

Why the Common Model of the mind needs holographic a-priori categories

The enterprise of developing a common model of the mind aims to create a foundational architecture for rational behavior in humans. Philosopher Immanuel Kant attempted something similar in 1781. The principles laid out by Kant for pursuing this goal can shed important light on the common model project. Unfortunately, Kant’s program has become hopelessly mired in philosophical hair-splitting. In this paper, we first use Kant’s approach to isolate the founding conditions of rationality in humans. His philosophy lends support to Newell’s knowledge level hypothesis, and together with it directs the common model enterprise to take knowledge, and not just memory, seriously as a component of the common model of the mind. We then map Kant’s cognitive mechanics to the operations which are used in the current models of cognitive architecture. Finally, we argue that this mapping can pave the way to develop the ontology of the knowledge level for general intelligence. We further show how they can be actualized in a memory system using high dimensional vectors to achieve specific cognitive abilities.

A rational cognitive architectural model of language generation

In this paper, a prototype model demonstrating the rationality in humans is developed. For queries to be answered, the internal knowledge representation mechanism is understood to be in the form of inter-related clusters permanently stored in the long term memory (LTM). In response to a query involving a single predicate (simplest case), only one cluster along with its various attributes and values may be retrieved. In contrast, a complex query involving multiple predicates requires the retrieval of multiple related chunks, forming a network of knowledge, often referred to as a concept map, from which the response is generated. An internal process that happens is the matching of predicate of the question with the facts in the knowledge-base and retrieval of the chunk(s) with the corresponding Attribute-Value pairs, through a process akin to unification, a substitution procedure. However, an unanswered query due to the absence of a specific knowledge generates a negative response.

Cognitive Representation of Human Action: Theory, Applications, and Perspectives.

In this perspective article, we propose a cognitive architecture model of human action that stresses the importance of cognitive representations stored in long-term memory as reference structures underlying and guiding voluntary motor performance. We introduce an experimental approach to ascertain cognitive representation structures and provide evidence from a variety of different studies, ranging from basic research in manual action to application-oriented research, such as athlete performance and rehabilitation. As results from these studies strongly support the presence of functional links between cognitive and motor processes, we regard this approach as a suitable and valuable tool for a variety of different disciplines related to cognition and movement. We conclude this article by highlighting current advances in ongoing research projects aimed at improving interaction capabilities in technical systems, particularly for rehabilitation and everyday support of the elderly, and outline future research directions.

Origins and evolution of enactive cognitive science: Toward an enactive cognitive architecture

Abstract This paper presents a historical perspective on the origin of the enactive approach to cognitive science, starting chronologically from cybernetics, with the aim of clarifying its main concepts, such as enaction, autopoiesis, structural coupling and natural drift; thus showing their influences in computational approaches and models of cognitive architecture. Works of renowned authors, as well as some of their main commentators, were addressed to report the development of enactive approach. We indicate that the enactive approach transcends its original context within biology, and at a second moment within connectionism, changes the understanding of the relationships so far established between the body and the environment, and the ideas of conceptual relationships between the mind and the body. The influence on computational theories is of great importance, leading to new artificial intelligence systems as well as the proposition of complex, autopoietic and alive machines. Finally, the article stresses the importance of the enactive approach in the design of agents, understanding that previous approaches have very different cognitive architectures and that a prototypical model of enactive cognitive architecture is one of the largest challenges today.

Cognitive Model of Human Visual Search with Saliency and Scene Context for Real-world Images

The previous Adaptive Control of Thought-Rational (ACT-R) cognitive architecture model has limitations in the sense that it cannot accurately predict human visual search for real-world images because scene context which could be as important as saliency is not included. Thus, this study proposed ACT-R cognitive modeling with saliency and scene context in parallel for human visual search. Then, the validation of the model was performed by comparing with eye-tracking experimental data. Results show that the model data was quite well fit with the eye-tracking data. In conclusion, the modeling method proposed in this study should be used, in order to predict actual human visual search using both strategies selectively for real-world image.

Hume in the Light of Bayes: Towards a Unified Cognitive Science of Human Nature

David Hume hoped future advances would bring his nascent science of human nature nearer to perfection. He conjectured that since it is probable one operation and principle of the mind depends on another it might someday be possible to subsume all mental operations under one absolutely general and universal principle. I argue that the day Hume hoped for may now be upon us, as an increasing number of theorists suspect that predictive Bayesian models of cognitive architecture provide the best clue yet as to the shape of a unified theory of mind. A theory in which the manifold operations of perception, imagination, and belief formation may all be explained in terms of the same cognitive processes that implement causal inference. I show the fundamental mechanism behind such architectures, the notion of a generative model, comprises a functional analogue to Hume’s faculty of the imagination. I apply the ideas outlined to construct a theoretical model of speech perception which is able to explain perceptual illusions, such as phonemic restoration and Ganong effects as artifacts of Bayesian inference. If the unitary view spelled out in this paper is correct perception, action, understanding, and the imagination all co-emerge from the same neural and computational resources that implement error correction and causal inference. I thus conclude that predictive Bayesian models comprise a sound methodology with which to continue Hume’s project of constructing a genuinely unified cognitive science of human nature.

Attitude, Inference, Association: On the Propositional Structure of Implicit Bias

The overwhelming majority of those who theorize about implicit biases posit that these biases are caused by some sort of association. However, what exactly this claim amounts to is rarely specified. In this paper, I distinguish between different understandings of association, and I argue that the crucial senses of association for elucidating implicit bias are the cognitive structure and mental process senses. A hypothesis is subsequently derived: if associations really underpin implicit biases, then implicit biases should be modulated by counterconditioning or extinction but should not be modulated by rational argumentation or logical interventions. This hypothesis is false; implicit biases are not predicated on any associative structures or associative processes but instead arise because of unconscious propositionally structured beliefs. I conclude by discussing how the case study of implicit bias illuminates problems with popular dual‐process models of cognitive architecture.

The Perils of Being Porous: A Psychological View of Spirit Possession and Non-dogmatic Ways of Helping

SYNOPSISThe Conference on Spirit Possession and Mental Health, organized by the Ethnic Health Initiative in London, September 2013, introduced the dilemma of the practitioner of Western medicine, faced with a predominantly ethnic-minority client group with a culturally and religiously sanctioned attribution of certain mental health difficulties to possession by spirits (or Jinns in Islamic terminology). Working with traditional healers is a possible solution, but is complicated by the heterogeneity and lack of regulation of this body. By questioning the assumptions behind the Western scientific world-view regarding the impermeability of the human mind, this paper presents a model of the human being that can accommodate the reality of extraneous invasion. Based on the Interacting Cognitive Subsystems model of cognitive architecture (Teasdale and Barnard, 1995), it is possible to argue that we have the capacity in certain susceptible states of mind to step away from our individuality into such a place of vulnerability. Schizotypy research (Claridge, 1997) has explored this potential. A therapeutic approach that honours the individual's experience, based on this re-conceptualization, is presented.

Broadband Access for All: Strategies and Tactis of Wireless Traffic Sharing

Network engineers have designed an array of protocols that enabled shared access in various wired and wireless contexts at different layers of the protocol stack [1]. One approach to managing unlicensed spectrum is to rely on a technical protocol to allocate and manage shared access Lehr [2]. This paper addressed the benefits of unlicensed wireless traffic within licensed traffic (anticipated as cognitive router-based networking). A focus on shared access to non-exclusive use of the spectrum with an holistic view of technical and institutional features is suggested for effective management of ‘spectrum commons’. Using the adapted cognitive radio architectural model and its associated multi-hop ad-hoc networking strategies to implement the ‘spectrum-common’, mobility is enhanced with each node acting as a router and packet forwarder. We formulate management frameworks that can integrate well with liquid protocols for mobile nodes. Also, these frameworks incorporate new strategies of intelligently adapting the nodes to dynamically participate in setting bandwidth capacity stochastically. The projected use of dynamic bandwidth shaping algorithm for the cognitive radio-based network (CRN) when implemented will make broadband access more economical to users and the spectrum used effectively.