Intentional Cognitive Models with Volition

Man’s intellectual capacity remains an enigma, as it is both the subject and the means of analysis. If one is to assume quantum-wave dualism in physics then the state of the world depends on the instruments we use for observation. The “paradoxical” nature of investigating human cognition may thus bear inherent limitations. However, studying cognitive models may be less of a seemingly inconsistent endeavor, if “contradictions” may be classified. In this brief exposition, a variety of aspects related to cognitive models are discussed. The authors maintain that modeling the functional “paradoxical nature” of human cognition remains the greatest challenge. Therefore, consciousness aside, models of conscious systems, or rather conscious models of conscious systems, are the main objects of exploration. While intentional systems may seem a good starting point for such an exploration, they lack two important constructs: volition and reflexion. Both concepts, and especially volition, unlike rationality for example, are less discussed in the discourse of cognitive models. Although not devoted to volition or reflexion, this work proposes an increased research interest in these areas

Intention Awareness in the Nutshell

Intention awareness is a necessary condition for collective structures to cooperate. This short paper explains the concept of intention awareness, both internal and external. It highlights the importance of adopting a view of intention as an emergent as well as for system design

The Clash of Perceptions

This study challenges Samuel Huntington’s well-known clash of civilizations paradigm based on a philosophical reasoning of cognitive proofs. The authors propose the clash of perceptions, an alternative paradigm that better reflects the complexity of individual and collective interactions. Building on case studies and recent cognitive science and informatics research, this paradigm offers greater insight into the dynamics of international relations. In the first section, the authors explain the conceptual and methodological limits of Huntington’s paradigm before proposing in the second section a new approach geared toward individual and group phenomena aiming to model the clash of perceptions. New concepts such as percepts, misperception, misconception and perception prototypes are introduced in order to explain this complex process. These concepts help better understand the complexity of conflict among individuals, groups and nation states

Deep Cognitive Neural Network (DCNN)

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and recurrent networks , and replaces multi - layer perceptron (MLP) based sigmoidal neural structures with a queuing theory-driven design. For example, in an embodiment, a circuit may comprise a plurality of layers of neural network circuitry, each layer comprising a plurality of neuron circuits, each neuron comprising a plurality of computational circuits, and each neuron connected to a plurality of other neurons in the same layer by synapse circuitry, wherein the plurality of layers of neural network circuitry are adapted to process symbolic and conceptual information.United State

Comparison of median frequency between traditional and functional sensor placements during activity monitoring

Long-term monitoring is of great clinical relevance. Accelerometers are often used to provide information about activities of daily living. The median frequency (f[subscript m]) of acceleration has recently been suggested as a powerful parameter for activity recognition. However, compliance issues arise when people need to integrate activity recognition sensors into their daily lives. More functional placements should provide higher levels of conformity, but may also affect the quality and generalizability of the signals. How f[subscript m] changes as a result of a more functional sensor placement remains unclear. This study investigates the agreement in f[subscript m] for a sensor placed on the back with one in the pocket across a range of daily activities. The translational and gravitational accelerations are also computed to determine if the accelerometer should be fused with additional sensors to improve agreement. Twelve subjects were tested over four tasks and only the “vertical” x-axis showed a moderate agreement (Intraclass Correlation Coefficient of 0.54) after correction for outliers. Generalizability across traditional and functional sensor locations might therefore be limited. Differentiation of the signal into a translational and gravitational component decreased the level of agreement further, suggesting that combined information streams are more robust to changing locations then singular data streams. Integrating multiple sensor modalities to obtain specific components is unlikely to improve agreement across sensor locations. More research is needed to explore measurement signals of more user friendly sensor configurations that will lead to a greater clinical acceptance of body worn sensor systems

Music Genre Classification: A Semi-supervised Approach

Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections

The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU) is proposed as a means to better quantify the intelligent thought process at multiple levels of analysis. From the linguistic and behavioral output, FCU produces to the chemical and physical processes within the brain that drive it. The proposed method efficiently model the most complex decision-making process performed by the brain