information

Abstract. Manifold initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms. This paper reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis. Projects investigating the nature of computations by cortical columns are discussed which exemplify the application of this standard method. New findings are mentioned indicating that the concept of the basic uniformity of the cortex is untenable. The claim is discussed that non-decomposability is not an intrinsic property of complex, integrated systems but is only in our eyes, due to insufficient mathematical techniques. Using Rosen’s modeling relation, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e., cognitive and other complex systems are decomposable, must be abandoned. 1

Nearly a half-century ago, J.C.R. Licklider expressed a vision for “man-machine symbiosis, ” coupling human brains and computing machines in a partnership that “will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today. ” Until relatively recently, this vision was largely left idle by human factors engineering (HFE) research that grew over the decades from an initial focus on design of equipment to accommodate human limitations to cognitive systems engineering research to a more recent perspective focusing on design of human-information interaction. These perspective shifts and insights have brought a degree of success to the field in design efforts aimed at enhancing human-system performance. In recent years, the research area of augmented cognition has begun to shift the focus once more not only to enhancing the interaction environment, but also the cognitive abilities of the human operators and decision makers themselves. Ambitious goals of increasing total cognitive capacity through augmented cognition technologies are still on the horizon of this research program. This paper describes a framework within which augmented cognition research may identify requirements that compensate for human information processing shortcomings and augment human potential.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection

Abstract — Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software and hardware optimization. On the software implementation side, appropriate data structures can improve the performance of the software by more than 5,000X. On the hardware implementation side, the cogent confabulation algorithm is an ideal candidate for parallel processing and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost tradeoffs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve good cost performance tradeoffs. I.

The purpose of this paper is to re-address the vision of human-computer symbiosis as originally expressed by J.C.R. Licklider nearly a half-century ago and to argue for the relevance of this vision to the field of cognitive informatics. We describe this vision, place it in some historical context relating to the evolution of human factors research, and observe that the field is now in the process of re-invigorating Licklider’s vision. A central concept of this vision is that humans need to be incorporated into computer architectures. We briefly assess the state of the technology within the context of contemporary theory and practice, and we describe what we regard as this emerging field of neo-symbiosis. Examples of neo-symbiosis are provided, but these are nascent examples and the potential of neo-symbiosis is yet to be realized. We offer some initial thoughts on requirements to define functionality of neo-symbiotic systems and discuss research challenges associated with their development and evaluation. Methodologies and metrics for assessing neo-symbiosis are discussed.