Tools of the Neuro-Fuzzy Model of Information Risk Management in National Security

The rapid development of information technology has strengthened the importance of the information risk management system. Integrated systems for storing and processing information, its transmission channels, as well as the information itself, are strategically essential objects of national security. The growing volumes of statistical data, as well as the traditional uncertainty and incompleteness of information on the nature of potential threats, determine the need to use new approaches for risk analysis. The neuro-fuzzy model considered in the article is based on the advantages of fuzzy logic and artificial neural networks. The proposed neuro-fuzzy network is adapted for continuous risk analysis and iterative implementation of the analysis stage. It eliminates the disadvantages of the fuzzy logical model and takes full advantage of neural networks. This system copes well with large volumes of information since there is a direct correlation between the amount of data and the speed of network learning. The data provided by the network at the output is expressed in understandable terms and sufficient to make a balanced and reasoned decision on information risk management.

Modeling the Roles of Category and Feature Information in Inference

The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known.

Predictive power in models of audiovisual integration of speech

Seeing the talking face can influence the phoneme perceived from the voice. This facilitates speech perception in the natural case where the face and voice are congruent and can cause the McGurk illusion when they are not. The classical example of the McGurk illusion is when acoustic /aba/ is perceived as /ada/ when dubbed onto a face articulating /aga/. In order to fully understand the underlying process of integrating information across the senses we need a computational account with predictive power. The Fuzzy Logical Model of Perception is one computational account of audiovisual integration in speech perception. Here we describe alternative accounts in which integration is based on an early continuous internal representation on which the phonetic classes fall. We show that these alternative accounts can provide just as good a fit when corrected for the number of free parameters. We also show, using cross-validation, that they have greater, but not great, predictive power. Finally, we show that introducing a regularization term can amend the lack of predictive power. With regularization, models based on continuous representations have the highest predictive power.

Tribute to an ideal exemplar of scientist and person

Roger Shepard's creativity and scientific contributions have left an indelible mark on Psychology and Cognitive Science. In this tribute, I acknowledge and show how his approach to universal laws helped Oden and me shape and develop our universal law of pattern recognition, as formulated in the Fuzzy Logical Model of Perception (FLMP). [Shepard; Tenenbaum & Griffiths]