Cognitive Systems Theory Research Articles

Mastering Automation: New Airline Pilots’ Perspective

ABSTRACT Aircraft accidents over the last decade have raised questions about the most effective way to transition pilots onto new aircraft automation systems. This debate can range from the details contained within aircraft flight manuals to computer-based versus simulator-based training. While some questions are still under consideration, most fall short in the understanding of how pilots “actually” learn. The researchers conducted a longitudinal study focusing on pilot learning; through interviews, observations, and field documentation, 10 pilot trainees from two regional airlines were followed for 5 months throughout their initial airline training. Using joint cognitive systems theory, and concepts from curriculum theory to understand the utility of training programs, the study revealed gaps between the airlines’ intended learning outcomes and the trainees’ actual experiences. The findings demonstrate that automation, as opposed to other aircraft systems, may require different approaches to learning. Recommendations for possible modifications to current pilot training are discussed.

Getting mixed signals: Connotations of teamwork as performance shaping factors in network controller and rail driver relationship dynamics

Signal passed at danger (SPAD) events have been a perennial issue in rail and requires continued scholarship. Much of the literature has tended to focus on activities within the train cab, placing the spotlight on “error” within the rail driving role; however a train is not propelled by a single person but by a team. This study set out to understand how network controllers perceive the relationship dynamic between the network controller and rail driver, and how these views shape system performance. Using a phenomenological approach underpinned by Joint Cognitive Systems theory, interviews were conducted with network controllers (N = 55) across 8 rail organisations in Australia and New Zealand to examine how they related to signals and to drivers. Sixteen different perspectives were identified ranging in type and varying by frequency, each with implications for coupling strength between the controller and driver roles and on system performance. Key dimensions of teamwork in train movement were emphasised, illustrating how the underlying values and philosophies in different network controlling cultures influence risk perception. The findings may be used to develop a more informed understanding of the ‘controller-signal-driver-train’ system and pave the way for strategies that can embrace diversity in different perspectives whilst optimizing system safety and performance.

The perspective over major content discoveries in Cognitive System Theory 2000-17 by Peter G Burton

The perspective over major content discoveries in Cognitive System Theory 2000-17 by Peter G Burton

Electronic information systems in human service organizations: Using theory to inform future design

The problems with current forms of electronic information systems (IS) implemented in human service organizations have been well documented and attention is now focussed on how they might be redesigned for the future. The aim in this article is to demonstrate how previous research and theory can provide useful insights into these problems, which, in turn, can provide guidance for future research-based approaches to redesign. Ideas from ‘cognitive systems theory’ (CSE) and more specifically ‘joint cognitive systems’ (JCS) are explored in relation to the main problems that have been identified with current forms of IS.

Grand Challenges for Computational Intelligence

The expansive research field of computational intelligence combines various nature-inspired computational methodologies and draws on rigorous quantitative approaches across computer science, mathematics, physics, and life sciences. Some of its research topics, such as artificial neural networks, fuzzy logic, evolutionary computation, and swarm intelligence, are traditional to computational intelligence. Other areas have established their relevance to the field fairly recently: embodied intelligence (Pfeifer and Bongard, 2006; Der, 2014), information theory of cognitive systems (Lungarella and Sporns, 2006; Polani et al., 2007; Ay et al., 2008), guided self-organization (Prokopenko, 2009; Der and Martius, 2012), and evolutionary game theory (Vincent and Brown, 2005). The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent. In short, innovation and creativity shown within a rich space shaped by diverse, “entropic” forces, appeal to us as cognitive traits (Wissner-Gross and Freer, 2013). Can this intuition be formalized within rigorous and generic computational frameworks? What are the crucial obstacles on such a path? Intuitively, intelligent behavior is expected to be predictable and stable, but sensitive to change. Attempts to formalize this duality date back at least to cybernetics. For example, Ashby’s well-known Law of Requisite Variety states that an active controller requires as much variety (number of states) as that of the controlled system to be stable (Ashby, 1956). In order to explain the generation of behavior and learning in machines and living systems, Ashby also linked the concepts of ultrastability and homeostatic adaptation (Di Paolo, 2000; Fernandez et al., 2014). The balance between robustness and adaptivity is often attained near “the edge of chaos” (Langton, 1990), and the corresponding phase transitions are typically detected via high sensitivities to underlying control parameters (thermodynamic variables) (Prokopenko et al., 2011). Stability in self-organizing systems can be generally related to negentropy, the entropy that the system exports (dissipates) to keep its own entropy low (Schrodinger, 1944). Despite significant advances in this direction, the fundamental question whether stability, within processes developing far from an equilibrium, necessitates specific entropy dynamics is still unanswered. Clarifying the connections between entropy dynamics and stable but adaptive behavior is one of the grand challenges for computational intelligence. Put simply, we need to know whether learning and self-organization necessitate phase transitions in certain spaces, in terms of some order parameters. Is it possible to characterize the richness of self-generated choice, intrinsic to intelligent behavior, with respect to generic thermodynamic principles? The notion of generating and actively exploiting new behaviors, which adequately match the environment highlights that to be intelligent is to be complex in creating innovations. And so a mechanism producing computational novelty needs to exceed some threshold of complexity. To be truly impressive in generating endogenous innovation, it needs to be capable of universal computation, or to approach this capability in finite implementations (Casti, 1994; Markose, 2004). In other words, computational novelty may be fundamentally related to undecidability. Again, serious advances have been made in this foundational area of computer science. For example, Casti (1991) analyzed deeper interconnections between dynamical systems, Turing Machines, and formal logic systems: in particular, the complex, class IV, cellular automata were related to formal systems with undecidable statements (Godel’s incompleteness theorem) and the Halting Problem. Nevertheless, the question whether universal computation is the ultimate innovation-generator is still unresolved, offering another grand challenge: how computational intelligence, including mechanisms producing richness of choice and novelty, is related to

Cognitive system theory: exposing gaps in human cognition

Cognitive system theory: exposing gaps in human cognition

Toward an Integrated Approach to Perception and Action: Conference Report and Future Directions

This article was motivated by the conference entitled “Perception & Action – An Interdisciplinary Approach to Cognitive Systems Theory,” which took place September 14–16, 2010 at the Santa Fe Institute, NM, USA. The goal of the conference was to bring together an interdisciplinary group of neuroscientists, roboticists, and theorists to discuss the extent and implications of action–perception integration in the brain. The motivation for the conference was the realization that it is a widespread approach in biological, theoretical, and computational neuroscience to investigate sensory and motor function of the brain in isolation from one another, while at the same time, it is generally appreciated that sensory and motor processing cannot be fully separated. Our article summarizes the key findings of the conference, provides a hypothetical model that integrates the major themes and concepts presented at the conference, and concludes with a perspective on future challenges in the field.

A likely cybernetic perennial: studies in cognitive systems theory

Reviews the work of Robert Vallée and considers in particular his latest contribution, Cognition et Système.

A Basic Notion in the Theory of Cognitive Systems

IN a previous communication1 a network was described which would resolve linear relations among continuous input signals. It was indicated that for times greater than the resolution time (for some intrinsic error criterion) this net could follow changing relations and thus, by means of other nets, one could observe the relations between relations and so on. The process described is essentially that of ascending a scale of logical types. To characterize this more generally, consider a binary time series y(t) of discrete time t. If this contains 2 r≤2m structures of length it can be described by a logical function: