The Link between Cognition and the Complexity of Engineering Systems Design

This paper focuses on the role of human cognition in the design of large complex systems. It contrasts the physical system that is the product of the design with the cognitive model that is used by the designer to “understand” the system. The complexity of the system relevant to the designer is a function not only of the physical system, but also of the cognitive model that the designer holds in his mind. Furthermore, the level of cognitive model available to an experienced designer depends on the state of domain knowledge. To be useful in answering the question, “How complex is this system to design?” the state of the domain knowledge available to the designer must be assessed with respect to the level at which the design problem is posed. The concept of conceptual distance is introduced that depends on the disparity between the present level of integrated knowledge and the conceptual level of the design problem. This “distance” is a measure of the complexity of the design task and is called the cognitive complexity of the design. To investigate the concept of cognitive complexity a model of human knowledge is proposed along with a set of graphical abstractions. It is concluded that the cognitive complexity of the design task is neither wholly intrinsic (a property of the system) nor wholly subjective (a property of the mind) but requires an objective evaluation of the engineering problem with respect to present knowledge. It is noted that the structure of knowledge in a specific domain can be mapped and therefore a research program can be launched to systematically determine the difficulty of various engineering endeavors

Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis

This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.Comment: AAAI 2023 Fall Symposiu

Successes and critical failures of neural networks in capturing human-like speech recognition

Natural and artificial audition can in principle evolve different solutions to a given problem. The constraints of the task, however, can nudge the cognitive science and engineering of audition to qualitatively converge, suggesting that a closer mutual examination would improve artificial hearing systems and process models of the mind and brain. Speech recognition - an area ripe for such exploration - is inherently robust in humans to a number transformations at various spectrotemporal granularities. To what extent are these robustness profiles accounted for by high-performing neural network systems? We bring together experiments in speech recognition under a single synthesis framework to evaluate state-of-the-art neural networks as stimulus-computable, optimized observers. In a series of experiments, we (1) clarify how influential speech manipulations in the literature relate to each other and to natural speech, (2) show the granularities at which machines exhibit out-of-distribution robustness, reproducing classical perceptual phenomena in humans, (3) identify the specific conditions where model predictions of human performance differ, and (4) demonstrate a crucial failure of all artificial systems to perceptually recover where humans do, suggesting a key specification for theory and model building. These findings encourage a tighter synergy between the cognitive science and engineering of audition

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Scientific requirements for an engineered model of consciousness

The building of a non-natural conscious system requires more than the design of physical or virtual machines with intuitively conceived abilities, philosophically elucidated architecture or hardware homologous to an animal’s brain. Human society might one day treat a type of robot or computing system as an artificial person. Yet that would not answer scientific questions about the machine’s consciousness or otherwise. Indeed, empirical tests for consciousness are impossible because no such entity is denoted within the theoretical structure of the science of mind, i.e. psychology. However, contemporary experimental psychology can identify if a specific mental process is conscious in particular circumstances, by theory-based interpretation of the overt performance of human beings. Thus, if we are to build a conscious machine, the artificial systems must be used as a test-bed for theory developed from the existing science that distinguishes conscious from non-conscious causation in natural systems. Only such a rich and realistic account of hypothetical processes accounting for observed input/output relationships can establish whether or not an engineered system is a model of consciousness. It follows that any research project on machine consciousness needs a programme of psychological experiments on the demonstration systems and that the programme should be designed to deliver a fully detailed scientific theory of the type of artificial mind being developed – a Psychology of that Machine

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

Informational Mode of the Brain Operation and Consciousness as an Informational Related System

Introduction: the objective of the investigation is to analyse the informational operating-mode of the brain and to extract conclusions on the structure of the informational system of the human body and consciousness. Analysis: the mechanisms and processes of the transmission of information in the body both by electrical and non-electrical ways are analysed in order to unify the informational concepts and to identify the specific essential requirements supporting the life. It is shown that the electrical transmission can be described by typical YES/NO (all or nothing) binary units as defined by the information science, while the inter and intra cell communication, including within the synaptic junction, by mechanisms of embodiment/disembodiment of information. The virtual received or operated information can be integrated in the cells as matter-related information, with a maximum level of integration as genetically codified info. Therefore, in terms of information, the human appears as a reactive system changing information with the environment and between inner informational subsystems which are: the centre of acquisition and storing of information (acquired data), the centre of decision and command (decision), the info-emotional system (emotions), the maintenance informational system (matter absorption/desorption/distribution), the genetic transmission system (reproduction) and info-genetic generator (genetically assisted body evolution). The dedicated areas and components of the brain are correlated with such systems and their functions are specified. Result: the corresponding cognitive centres projected into consciousness are defined and described according to their specific functions. The cognitive centres, suggestively named to appropriately include their main characteristics are detected at the conscious level respectively as: memory, decisional operation (attitude), emotional state, power/energy status and health, associativity and offspring formation, inherited predispositions, skills and mentality. The near-death and religious experiences can be explained by an Info-Connection pole. Conclusion: consciousness could be fully described and understood in informational terms

The extended mind thesis is about demarcation and use of words

The «extended mind thesis» sounds like a substantive thesis, the truth of which we should investigate. But actually the thesis a) turns about to be just a statement on where the demarcations for the «mental» are to be set (internal, external,…), i.e. it is about the «mark of the mental»; and b) the choice about the mark of the mental is a verbal choice, not a matter of scientific discovery. So, the «extended mind thesis » is a remark on how its supporters or opponents want to use the word ‘mind’, not a thesis of cognitive science or philosophy. The upshot of the extended mind discussion should not be to draw the line further out, but to drop the demarcation project