Natural Cognitive Systems Research Articles

Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction.

In this study, we explore the potential benefits of integrating natural cognitive systems (medical professionals' expertise) and artificial cognitive systems (deep learning models) in the realms of medical image analysis and sports injury prediction. We focus on analyzing medical images of athletes to gain valuable insights into their health status. To synergize the strengths of both natural and artificial cognitive systems, we employ the ResNet50-BiGRU model and introduce an attention mechanism. Our goal is to enhance the performance of medical image feature extraction and motion injury prediction. This integrated approach aims to achieve precise identification of anomalies in medical images, particularly related to muscle or bone damage. We evaluate the effectiveness of our method on four medical image datasets, specifically pertaining to skeletal and muscle injuries. We use performance indicators such as Peak Signal-to-Noise Ratio and Structural Similarity Index, confirming the robustness of our approach in sports injury analysis. Our research contributes significantly by providing an effective deep learning-driven method that harnesses both natural and artificial cognitive systems. By combining human expertise with advanced machine learning techniques, we offer a comprehensive understanding of athletes' health status. This approach holds potential implications for enhancing sports injury prevention, improving diagnostic accuracy, and tailoring personalized treatment plans for athletes, ultimately promoting better overall health and performance outcomes. Despite advancements in medical image analysis and sports injury prediction, existing systems often struggle to identify subtle anomalies and provide precise injury risk assessments, underscoring the necessity of a more integrated and comprehensive approach.

Reframing Cognitive Science as a Complexity Science

Complexity science is an investigative framework that stems from a number of tried and tested disciplines-including systems theory, nonlinear dynamical systems theory, and synergetics-and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self-organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science both transforms understandings of cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline.

Computational modeling of insight processes and artificial cognitive ontogeny

Many approaches were proposed to model complex socially emotional behavior in virtual actors, such as intelligent tutors, creative assistants, or team partners. They still lack the ‘magic’ of human-level cognition: systems built for one paradigm appear clueless outside of its boundary. Natural cognitive systems, on the other hand, can adapt to unexpected environments and paradigms. To capture the robustness of natural cognitive development, a new approach is proposed here that enables the formation of new higher cognitive abilities in a model system, embedded in an unexpected environment. This is achieved based on the naturally developing grounding of innate abstract constructs (schemas). The mechanism producing this binding is that of creative insight. In this study, principles of insight processes borrowed from psychology are formalized and adapted for computer modeling. To do this, several examples of insight phenomena at different evolutionary levels and in different species are analyzed before the model is formulated based on the dual process theory, the signal model of insight, and the eBICA cognitive architecture framework. Results of its computer simulations prove the concept. One specific finding is that the accumulation of activation during the incubation period increases creative abilities of the system. It is argued that the proposed approach can explain a range of facts and mysteries associated with the human cognitive ontogeny and can provide the basis for a self-sustained evolution of future Artificial Intelligence.

Why Open Theism Is Natural and Classical Theism Is Not

The cognitive science of religion (CSR) indicates that belief in supernatural agents, or “gods”, is underpinned by maturationally natural cognitive biases and systems (Natural Religion). It is unclear, however, whether theism is natural. Does the god concept that our cognitive biases and systems give rise to approximate theism? In other words, is Natural Religion “theism-tracking”? As Christian theologians have different views of what God is like, we argue that the answer depends partly on one’s model of God. We discuss two models: classical theism and open theism. We argue that classical theism is far from being natural. The classical divine attributes are very hard to comprehend. Moreover, people naturally conceptualize God as a special sort of person, but the classical God strongly deviates from our cognitive expectations about persons. Open theism is much more natural. However, recent findings in CSR challenge the suggestion that Natural Religion tracks open theism. The possibility that we are “born idolaters” rather than “born believers” might undermine the Christian doctrine of general revelation and attempts to make CSR compatible with theology.

A proposal for modeling cognitive ontogeny based on the brain-inspired generic framework for social-emotional intelligent actors

Many approaches were proposed to model socially emotional behavior in virtual actors, such as intelligent tutors, creative assistants or team partners. They still lack the ‘magic’ of human-level cognition and emotionality: systems built for one paradigm appear uncanny outside of its boundary. Natural cognitive systems, on the other hand, can adapt to unexpected environments and paradigms. To capture the robustness of natural cognitive development, a new approach is proposed here that enables the formation of higher cognitive abilities in a model cognitive system, embedded in an unexpected environment. This is achieved on the basis of a naturally developing binding of innate constructs to channels of the interface and features of the environment. The eBICA cognitive architecture used as a basis for this study combines rational, cognitive and somatic factors. Its building blocks are moral schemas and semantic maps. Building our previous study of the eBICA cognitive architecture, the present work takes it to the new level. It is argued that the proposed approach is capable of explaining, based on a unified standpoint, a number of facts and mysteries associated with the human cognitive ontogeny and can provide a basis for the strong Artificial Intelligence.

Beyond spatial reasoning: Challenges for ecological problem solving

This vision piece reflects upon virtues of early computer science due to scarcity and high cost of computational resources. It critically assesses divergences between real-world problems and their computational counterparts in commonsense problem solving. The paper points out the different objectives of commonsense versus scientific approaches to problem solving. It describes how natural cognitive systems exploit space and time without explicitly representing their properties and why purely computational approaches are less efficient than their natural role models, as they depend on explicit representations. We argue for investigating spatio-temporally integrated methods to spatial problem solving. We contrast these methods to sequential computational approaches that require digital twins of the environment and cannot make direct use of simultaneous spatio-temporal interactions. The paper concludes with predicting future developments in problem solving, praising the relative merits of different routes to be taken. It advocates the translation of fundamental cognitive principles into technical robotic solutions.

Theory of Mind, Religiosity, and Autistic Spectrum Disorder: a Review of Empirical Evidence Bearing on Three Hypotheses

AbstractThe cognitive science of religions’ By-Product Theory contends that much religious thought and behavior can be explained in terms of the cultural activation of maturationally natural cognitive systems. Those systems address fundamental problems of human survival, encompassing such capacities as hazard precautions, agency detection, language processing, and theory of mind. Across cultures they typically arise effortlessly and unconsciously during early childhood. They are not taught and appear independent of general intelligence. Theory of mind (mentalizing) undergirds an instantaneous and automatic intuitive understanding of minds, mental representations, and their implications for agents’ actions. By-Product theorists hypothesize about a social cognition content bias, holding that mentalizing capacities inform participants’ implicit understanding of religious representations of agents with counter-intuitive properties. That hypothesis, in combination with Baron-Cohen’s account of Autistic Spectrum Disorder (ASD) in terms of diminished theory of mind capacities (what he calls “mind-blindness”), suggests an impaired religious understanding hypothesis. It proposes that people with ASD have substantial limitations in intuitive understanding of and creative inferences from such representations. Norenzayan argues for a mind-blind atheism hypothesis, which asserts that the truth of these first two hypotheses suggests that people with ASD have an increased probability, compared to the general population, of being atheists. Numerous empirical studies have explored these three hypotheses’ merits. After carefully pondering distinctions between intuitive versus reflective mentalizing and between explicit versus implicit measures and affective versus cognitive measures of mentalizing, the available empirical evidence provides substantial support for the first two hypotheses and non-trivial support for the third.

The Naturalness of Religion: What It Means and Why It Matters

Summary“Religion is natural” has become a common thesis in Cognitive Science of Religion (CSR). The claim, however, is often ambiguous. This paper seeks to clarify and evaluate the naturalness of religion thesis that flows from CSR theories pointing to the optimal compatibility between recurrent religious concepts and the ordinary operations of the human mind. For the naturalness thesis to be scientifically valid, some criteria for naturalness are needed. Robert McCauley has suggested four typical marks for natural cognitive systems, but his account suffers from the inability to point to any causal operations in human development responsible for the naturalness of religion. Even if naturalness is a problematic concept, the science behind it may nevertheless carry interesting implications. First, since Christian theologians have traditionally viewed man as naturally religious, CSR offers new material for theological considerations. Second, it may also help us make predictions about the future of religion. Third, it has been argued that the naturalness thesis offers support for freedom of religion.

Steps to a “Properly Embodied” cognitive science

Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science”, which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems.

CogInfoCom Systems from an Interaction Perspective – A Pilot Application for EtoCom –

Cognitive Infocommunications (CogInfoCom) is a newly emerging research field that investigates the link between infocommunications and the cognitive sciences, with the goal of creating engineering systems in which artificial and natural cognitive systems can work together more effectively. In this paper, we describe the structure of CogInfoCom systems from an interaction perspective. Through the discussions in this paper, our goal is to further clarify the relationship between CogInfoCom and the various research areas that deal with behavioral and structural systems modeling. In order to demonstrate the theoretical aspects of the subject, we describe a pilot application which was developed during the EtoCom project.

Editorial of E-Book on Action and Language Integration

Increasing theoretical and experimental research on action and language processing in humans and animals clearly demonstrates the strict interaction and co-dependence between language and action. This has been extensively demonstrated in neuroscientific investigations (e.g., Rizzolatti and Arbib, 1998; Cappa and Perani, 2003; Pulvermuller, 2003), psychology experiments (e.g., Glenberg and Kaschak, 2002; Pecher and Zwaan, 2005; Barsalou, 2008), evolutionary psychology (e.g., Corballis, 2002), and computational modeling (e.g., Cangelosi and Parisi, 2004; Massera et al., 2007; Cangelosi, 2010). All these studies have important implication both for the understanding of the action basis of cognition in natural and artificial cognitive systems, as well as for the design of cognitive and communicative capabilities in robots (Cangelosi et al., 2010). The journal “Frontiers in Neurorobotics” published a collection of articles on the topic of action and language integration both in natural cognitive systems (e.g., humans and animals) and in artificial cognitive agents (robots and simulated agents). These articles are now collected in an e-book, for wider dissemination. This set of chapters provides an up to date overview of current advances in the grounding of language into sensorimotor knowledge. The first chapters primarily focus on experimental evidence from cognitive psychology (Symes et al., 2010), cognitive neuroscience studies (Borghi et al., 2010), and comparative experimental/simulation studies (Greco and Caneva, 2010). Two chapters then use neural network simulation for motor chains for sentence processing (Chersi et al., 2010) and a computational model of gaze planning in word recognition and reading (Ferro et al., 2010). Finally, four chapters use cognitive systems and robotics methodologies to investigate general principles of action–language grounding (Parisi, 2010), teleological representations of action and language for human–robot interaction experiments (Lallee et al., 2010), verbal and non-verbal communication in neurorobotics models (Bicho et al., 2010), and action bases of action words (Marocco et al., 2010).

Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals

Controlling cognitive systems like domestic robots or intelligent assistive environments requires striking an appropriate balance between responsiveness and persistence. Basic goal arbitration is an essential element of low level action selection for cognitive systems, necessarily preceding even deliberate control in the direction of attention. In natural intelligence, chemically regulated motivation systems focus an agent’s behavioural attention on one problem at a time. Such simple durative decision state can improve the efficiency of artificial action selection by avoiding dithering, but taken to extremes such systems can be inefficient and produce cognitively implausible results. This article describes and demonstrates an easy-to-implement, general-purpose latching method that allows for a balance between persistence and flexibility in the presence of interruptions. This appraisal-based system facilitates automatic reassessment of the current focus of attention by existing action-selection mechanisms. The proposed mechanism, flexible latching, drastically improves efficiency in handling multiple competing goals at the cost of a surprisingly small amount of additional code (or cognitive) complexity. We discuss implications of these results for understanding natural cognitive systems.

Incremental learning of Bayesian sensorimotor models: from low-level behaviours to large-scale structure of the environment

This paper concerns the incremental learning of hierarchies of representations of space in artificial or natural cognitive systems. We propose a mathematical formalism for defining space representations (Bayesian Maps) and modelling their interaction in hierarchies of representations (sensorimotor interaction operator). We illustrate our formalism with a robotic experiment. Starting from a model based on the proximity to obstacles, we learn a new one related to the direction of the light source. It provides new behaviours, like phototaxis and photophobia. We then combine these two maps so as to identify parts of the environment where the way the two modalities interact is recognisable. This classification is a basis for learning a higher level of abstraction map that describes the large-scale structure of the environment. In the final model, the perception–action cycle is modelled by a hierarchy of sensorimotor models of increasing time and space scales, which provide navigation strategies of increasing complexities.

Cognitive vision: The case for embodied perception

This paper considers arguments for the necessity of embodiment in cognitive vision systems. We begin by delineating the scope of cognitive vision, and follow this by a survey of the various approaches that can be taken to the realization of artificial cognitive vision systems, focussing on cognitive aspects. These range from the cognitivist symbolic representational paradigm, through connectionist systems and self-organizing dynamical systems, to the enactive cognition paradigm. We then consider various arguments for embodiment, beginning with paradigm-specific cases, and concluding with a paradigm-independent argument for embodied perception and cognition. We explore briefly different forms of embodiment and their relevance to the foregoing viewpoints. We highlight some of the key problems associated with embodied cognitive vision, including the phylogeny/ontogeny trade-off in artificial systems and the developmental limitations imposed by real-time environmental coupling. Finally, we conclude by considering some aspects of natural cognitive systems to see how they can provide insights to help in addressing these problems.

Neural architectures for robot intelligence.

We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.

Catastrophic forgetting in connectionist networks.

All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Plausible models of human cognition should therefore exhibit similar patterns of gradual forgetting of old information as new information is acquired. Only rarely does new learning in natural cognitive systems completely disrupt or erase previously learned information; that is, natural cognitive systems do not, in general, forget 'catastrophically'. Unfortunately, though, catastrophic forgetting does occur under certain circumstances in distributed connectionist networks. The very features that give these networks their remarkable abilities to generalize, to function in the presence of degraded input, and so on, are found to be the root cause of catastrophic forgetting. The challenge in this field is to discover how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks are examined. The review will consider how the brain might have overcome this problem and will also explore the consequences of this solution for distributed connectionist networks.

Vers un sujet narratif de la connaissance dans les modélisations de l 'apprentissage

Monique LINARD : Towards a narrative knowing subject in models of learning The purpose of this paper is to make a plea for a more existential, less objectivist approach to models of learning. The first theoretical part attempts to show how some trends in developmental psychology converge with neurobiology and language philosophy and recognize that, for a natural living intelligence, pure information simply does not exist. Human knowledge, in particular, is seen as the knowledge of someone for something in a context. The incarnation of the process of knowing in a bio¬ physical Mid psychic body itself imbedded in a socio-relational symbolic type of environment is neither a negligible nor a separable fact. It is a building component of cognitive activity (phenomenological stand). Consequently, cognitive objects and tasks don't stand out by themselves ; they exist inasmuch as they are produced by subjects self-directed by an interrelated set of intentions, mobiles and meanings. It is stated that in neglecting the "subject" of knowledge, the existing I.A. modélisations deprive themselves of a double means of selective control, common to natural cognitive systems : upstream, the selective orientation by intention and motivation and, downstream, the selective adaptative structuration by feedbacks of actions and decisions identified as interesting by the system. The second part of the paper describes a tentative step towards the formal modelling of a knowing subject from both an actional and narrative perspective. A.J. Greimas' actantial model and semio-narrative theory are summarily expounded. Within this frame, the act of learning is interpreted as the quest of objects of a particular nature (objects of knowledge) by actor-subjects playing their roles in a context of social interactions of a polemico-contractual type. The model provides an operational description which integrates within a narrative framework some of the roles, attitudinal modalities and situational parameters at work in the ordinary course of human activity. Lastly, the main features of INTERACTOR, a simulation derived from the above model, are explained (draft version in Prolog and Visual Basic). The simulation has been conceived as a training tool to explore the dynamic complexity of the underlying attitudinal socio-affective and situational interactions that pre-condition the cognitive activity of individuals in a learning context.