Applications In Cognitive Systems Research Articles

The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion.

Bayesian cognitive modeling has become a prominent tool for the cognitive sciences aiming at a deeper understanding of the human mind and applications in cognitive systems, e.g., humanoid or wearable robotics. Such approaches can capture human behavior adequately with a focus on the crossmodal processing of sensory information. The rubber foot illusion is a paradigm in which such integration is relevant. After experimental stimulation, many participants perceive their real limb closer to an artificial replicate than it actually is. A measurable effect of this recalibration on localization is called the proprioceptive drift. We investigate whether the Bayesian causal inference model can estimate the proprioceptive drift observed in empirical studies. Moreover, we juxtapose two models employing informed prior distributions on limb location against an existing model assuming uniform prior distribution. The model involving empirically informed prior information yields better predictions of the proprioceptive drift regarding the rubber foot illusion when evaluated with separate experimental data. Contrary, the uniform model produces implausibly narrow position estimates that seem due to the precision ratio between the contributing sensory channels. We conclude that an informed prior on limb localization is a plausible and necessary modification to the Bayesian causal inference model when applied to limb illusions. Future research could overcome the remaining discrepancy between model predictions and empirical observation by investigating the changes in sensory precision as a function of distance between the eyes and respective limbs.

Cognitive Intelligence

The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Cognitive Self-Optimization for Quality Control Loops – Potentials and Future Challenges in Research

On all company levels, control loops are a proper way to control and optimize processes. As energy and resource efficiency come into focus and the increasing wish of the customer for individualized products adds even more complexity to the control task, standard control systems are unable to meet these increased demands. In the field of Self-Optimization, researchers try to enhance these control loops with more degrees of freedom by an adaption of the inner rules and objectives of the controller and the application of cognitive systems. This paper presents some promising developments that have been made in the field of self-optimization and shows which challenges research will have to face, like the missing standardization, the complexity of multi-parameter optimization, the problem of complex and conflicting goal systems and the technical risk management for non-deterministic system behavior.