Differences Between Natural and Artificial Cognitive Systems

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.

In previous work, we have argued that a sophisticated cognitive system with a complex body must possess configurable models of itself (or at least its body) and the world, along with the necessary infrastructure to use the modelled interactions between these two components to select relatively advantageous actions. These models may be used to generate representations of the future (imagination) and the past (episodic memory). In this paper we will explore some problems surrounding the representation of the present arising from the use of such models in the artificial cognitive system under development within the ECCEROBOT project. There are two aspects to consider: the representation of the state of the robot's body within the self model, and the representation of the state of the external world within the world model. In both natural and robotic systems, the processing of the sensory data carrying state information takes a considerable time, and so any estimates of the present states of both the agent and the world would have to be obtained by using predictive models. However, it appears that there is no need for any such representations to be generated in the course of selecting a course of action using self and world models, since representations are only of the future or the past. This may call into question the utility and timing of the apparent perception of the present in humans.

What will artificial cognitive systems of the future look like? If we are asked to imagine robots, or intelligent software agents, several features come to our mind such as the capability to adapt to their environments and to satisfy their goals with only limited human intervention, to plan sequences of actions for realizing long-term objectives, to act collectively in view of complex objectives, to interact and cooperate with us, with and without natural language, to take decisions (also in our place), etc.Currently these capabilities are far beyond the possibilities of robots and other artificial systems. In the next years a huge effort will be required for scaling up the potentialities of the artificial systems that we are able to build nowadays. One way to overcome these limitations is to take inspiration form the functioning of living organisms. A large body of evidence, which we review in this chapter, indicates that natural cognitive systems are not reactive but essentially anticipatory systems. We do not think that this is a mere coincidence. On the contrary, we claim that anticipation is a crucial—and foundational—phenomenon in natural cognition. Individual behavior is guided by anticipatory mechanisms that are used for behavioral control, perceptual processing, goal-directed behavior, and learning. And also effective social behavior relies on the anticipation of the behavior of other agents. We argue that anticipation is a key ingredient for the design of autonomous, artificial cognitive agents of the future: Only cognitive systems with anticipation mechanisms can be credible, adaptive, and successful in interaction with both the environment and other autonomous systems and humans. This is the challenge that we anticipate for the future of cognitive systems research: the passage from reactive to anticipatory cognitive embodied systems.KeywordsCognitive SystemForward ModelInternal ModelMirror NeuronMental Time TravelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cognitive vision: The case for embodied perception

The technological development of humanity has currently created the conditions for a transition to a civilization of cognitive technologies, within which a key need will be artificial cognitive systems for intelligent management of machines that produce goods. Cognitive systems will be integrated into all areas of information technology: the economy, society, and the intellectual sphere. An important factor in the development and expansion of the role of artificial cognitive systems is the expansion of their functionality through a toolset of multi-system integration of knowledge, which will allow them to reach the level of general (creative) artificial intelligence that possesses reasoning and is capable of solving creative tasks. In this regard, the issue of preserving humanity's leading role in civilization becomes pertinent. This will require humans to enhance their intellectual and communication capabilities. The research presented in the article is based on the following ideas: the development of civilization, the next stage of which is the civilization of cognitive technologies; the possibility of developing artificial intelligence to a level sufficient to attain reasoning; the delegation of functions by humans to artificial cognitive systems; and the inevitable competition between humans and machines, regardless of whether the latter possess subjectivity. The prospects for expanding the intellectual and communication capabilities of humans have two main scenarios for implementation. The first scenario is related to the further development of the trend of humans delegating their functions: initially to relatively simple digital systems and then to artificial cognitive systems, including artificial intelligence systems. The second scenario involves the enhancement of the human being itself in line with transhumanist concepts. Both scenarios are associated with significant risks that must be considered and mitigated through imposed restrictions. At the same time, the implementation of the second scenario appears inevitable due to the predictable choices of people seeking to expand their capabilities and abilities, as well as humanity's need to maintain control over the development of civilization, which can only be achieved through the enhancement of the human being.

Numerical cognition is a distinctive component of human intelligence such that the observation of its practice provides a window in high‐level brain function. The modelling of numerical abilities in artificial cognitive systems can help to confirm existing child development hypotheses and define new ones by means of computational simulations. Meanwhile, new research will help to discover innovative principles for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms (e.g. deep learning) that can be highly effective but difficult to understand for humans. This study promotes new investigation by providing a common resource for researchers with different backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are interested in pursuing scientific collaboration on mutually stimulating research on this topic. The study emphasises the fundamental role of embodiment in the initial development of numerical cognition in children. This strong relationship with the body motivates the cognitive developmental robotics (CDR) approach for new research that can (among others) help standardise data collection and provide open databases for benchmarking computational models. Furthermore, the authors discuss the potential application of robots in classrooms and argue that the CDR approach can be extended to assist educators and favour mathematical education.

Aviation Fatigue Risk Management Systems (FRMS) are crucial for ensuring operational safety by systematically monitoring and mitigating the risks associated with human fatigue in complex and high-demand aviation environments. This paper explores the integration of Artificial Cognitive Systems (ACS) into FRMS, focusing on how these intelligent systems can enhance human decision-making and fatigue management, contributing to improved safety and efficiency in aviation operations. ACS possess the capability to process vast amounts of real-time data and make context-aware decisions, enabling more accurate identification of fatigue risks through predictive analytics, pattern recognition, and human-machine interaction. ACS can complement traditional fatigue management methods in the aviation sector by continuously assessing physiological data, work schedules, environmental conditions, and operational demands to dynamically adapt fatigue risk mitigation strategies. These systems can proactively alert pilots, air traffic controllers, ground staff, and flight crews when fatigue thresholds are reached, enhancing the overall effectiveness of FRMS. This paper analyzes key methodologies and frameworks—including the International Civil Aviation Organization’s Fatigue Risk Management guidelines and regulations by the European Union Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA)—to illustrate how ACS can be integrated into current fatigue risk systems while adhering to international safety standards. Additionally, we will examine worldwide case studies where ACS has been applied in fatigue monitoring and management within the aviation industry, highlighting the impact of AI-powered decision support systems in reducing fatigue-related incidents and accidents. The analysis also addresses the human factors implications of implementing ACS within FRMS, emphasizing the balance between human oversight and machine-driven recommendations. Understanding the relationship between human cognitive limitations and the capabilities of ACS is critical in ensuring that these systems enhance, rather than hinder, human performance. Through a human-centric approach, ACS can help reduce workload, improve situational awareness, and ultimately provide more reliable fatigue risk management without leading to over-reliance on automated systems. In conclusion, this paper will propose a framework for integrating ACS into FRMS, demonstrating how artificial intelligence-driven solutions can complement human expertise to reduce fatigue-related risks, improve safety, and create a more resilient aviation system. By focusing on both technological advancements and challenges related to human factors, this paper provides a comprehensive roadmap for the future of fatigue risk management in aviation.

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).

first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Why Robots Must Have Synthetic Emotions? The Role of Emotions in the Artificial Cognitive Systems † by Jordi Vallverdú Philosophy Departmet, Fac. Lletres, Universitat Autònoma de Barcelona, 08193 Bellaterra (BCN) Catalonia, Spain † Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017. Proceedings 2017, 1(3), 272; https://doi.org/10.3390/IS4SI-2017-04088 Published: 9 June 2016 (This article belongs to the Proceedings of Proceedings of the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.) Download Download PDF Download PDF with Cover Download XML Versions Notes Not only we are attending to the exponential implementation of robotic platforms into several fields but also has arisen a public debate about the several challenges of this robot revolution. Among the long list of possible debates, there is one especially important: do must robots have emotions? Beyond the classic approaches related to affective computing which help to design better Human-Robot Interactions (henceforth, HRI), the presence of emotions into robotic systems is considered in a new light. Taking into consideration artificial cognitive architectures, should emotions, or a kind of synthetic emotions, be a fundamental part of these machines? We know that emotional values and mechanisms determine and shape the whole experience and rationing human processes, and it could affect/help/modify robotic ones. From an individual or a social perspective, the emotional skills of our robots can define a new scenario for the HRI processes as well as for the internal robotic revolution. From three different perspectives and disciplines, Anthropoogy, Engineering and Cognitive Philosophy, we will discuss these ideas in more detail, thanks to the collaborations of Lola Cañamero (University of Hertfordshire, UK), Rodolphe Gelin (Softbankrobotics, France), and Kathleen Richardson (De Montfort University, Leicester, UK). Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2017 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Vallverdú, J. Why Robots Must Have Synthetic Emotions? The Role of Emotions in the Artificial Cognitive Systems. Proceedings 2017, 1, 272. https://doi.org/10.3390/IS4SI-2017-04088 AMA Style Vallverdú J. Why Robots Must Have Synthetic Emotions? The Role of Emotions in the Artificial Cognitive Systems. Proceedings. 2017; 1(3):272. https://doi.org/10.3390/IS4SI-2017-04088 Chicago/Turabian Style Vallverdú, Jordi. 2017. "Why Robots Must Have Synthetic Emotions? The Role of Emotions in the Artificial Cognitive Systems" Proceedings 1, no. 3: 272. https://doi.org/10.3390/IS4SI-2017-04088 Find Other Styles Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here. Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.

What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems (Zlatev, 2002) like animals, or even plants (Krampen, 1992). It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that (at least some) semiotic processes are in fact cognitive processes. In consequence, it is natural to ask the question about the relation between semiotic studies and research on artificial cognitive systems within cognitive science. Consequently, my main question concerns the problem of inclusion or exclusion from the semiotic spectrum at least some artificial (computational) systems. I would like to consider some arguments against the possibility of artificial semiotic systems and I will try to repeal them. Then I will present an existing natural-language using agent of the SNePS system and interpret it in terms of Peircean theory of signs. I would like also to show that some properties of semiotic systems in Peircean sense could be also found in a discussed artificial system. Finally, I will have some remarks on the status of semiotics in general.

It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cognition could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of computers, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cognitive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the set-effect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infinitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the outside are not used to produce action but rather to either justify anticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and inferred rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again. In this scenario, superstition and mental disorders are the result of a profound and/or random restructuring of already existing cognitive components (e.g., action sequences). This means that whenever a genuinely cognitive system is exposed to pattern processing it may start to behave superstitiously. The closer we get to autonomous self-motivated artificial cognitive systems, the bigger the danger becomes of superstitious information processing machines that blow up rather than behave usefully and effectively. Therefore, to avoid superstition in cognitive systems they should be designed as information constructing entities.

Creative cognitive systems are rarely assessed with the same tools as human creativity. In this paper, an approach is proposed for building cognitive systems which can solve human creativity tests. The importance of using cognitively viable processes, cognitive knowledge acquisition and organization, and cognitively comparable evaluation when implementing creative problem-solving systems is emphasized. Two case studies of artificial cognitive systems evaluated with human creativity tests are reviewed. A general approach is put forward. The applicability of this general approach to other creativity tests and artificial cognitive systems, together with ways of performing cognitive knowledge acquisition for these systems are then explored.

In the near future, the capabilities of commonly used artificial systems will reach a level where we will be able to permit them to make moral decisions autonomously as part of their proper daily functioning—autonomous cars, personal assistants, household robots, stock trading bots, autonomous weapons, etc. are examples of the types of systems that will deal with simple to complex moral situations that require some level of moral judgment. In the research field of machine ethics, we distinguish several types of artificial moral agents, each of which has a different level of moral agency. In this paper, we focus on the moral agency of Explicit and Full-blown artificial moral agents. We form an opinion regarding their level of moral agency, and then examine the question of whether it is morally right to align the values of (artificial) moral agents. If we assume or are able to determine that certain types of artificial agents are indeed moral agents, then we ought to examine whether it is morally right to construct them in such a way that they are “committed” to human values. We discuss an analogy to human moral agents and the implications of granting or denying moral agency from artificial agents.

Artificial agents have become increasingly prevalent in human social life. In light of the diversity of new human–machine interactions, we face renewed questions about the distribution of moral responsibility. Besides positions denying the mere possibility of attributing moral responsibility to artificial systems, recent approaches discuss the circumstances under which artificial agents may qualify as moral agents. This paper revisits the discussion of how responsibility might be distributed between artificial agents and human interaction partners (including producers of artificial agents) and raises the question of whether attributions of responsibility should remain entirely on the human side. While acknowledging a crucial difference between living human beings and artificial systems culminating in an asymmetric feature of human–machine interactions, this paper investigates the extent to which artificial agents may reasonably be attributed a share of moral responsibility. To elaborate on criteria that can justify a distribution of responsibility in certain human–machine interactions, the role of types of criteria (interaction-related criteria and criteria that can be deferred from socially constructed responsibility relationships) is examined. Thereby, the focus will lay on the evaluation of potential criteria referring to the fact that artificial agents surpass in some aspects the capacities of humans. This is contrasted with socially constructed responsibility relationships that do not take these criteria into account. In summary, situations are examined in which it seems plausible that moral responsibility can be distributed between artificial and human agents.

This chapter aims to explore how and to what extent Artificial Cognition can and is being unified with Hardware Innovation, aiming the highest level of performance in Artificial Cognitive Systems, by consolidating a synergetic realm, through the hybridization of their interplay design tasks. Artificial Cognitive Systems, while inspired by Human Cognition, incorporate a segregative approach in their operation modes, thus their Artificial Cognition and Hardware components and results have a suboptimal level of performance, pending advancements that solve inherent, existing limitations. In the presented essay, we advance a working hypothesis: by realigning the design of Artificial Cognition and Hardware subtasks of Artificial Cognitive Systems, by aiming their integrated system level performance, updating and complementing cognitive tasks, requirements and limitations for Artificial Cognition and Hardware, that motivated and define current Artificial Cognitive Systems designs, and implementing within experimental setups implementations and testbeds, new solutions may come into the light that boost the overall level of performance of Artificial Cognitive Systems