Adaptive Information Processing System Research Articles

A Dialectical Perspective on the Adaptive Information Processing Model and EMDR Therapy

This article proposes a dialectical perspective on the adaptive information processing (AIP) model (F. Shapiro, 1995, 2001) with application to eye movement desensitization and reprocessing (EMDR) therapy. Dialectical principles may contribute to a more detailed understanding of the way the AIP system works as well as adding new therapeutic guidelines. Our dialectical perspective is based on 2 propositions. The first is that the movement of the AIP system toward integration consists of 2 dialectical movements: horizontal and vertical. The horizontal movement is between various opposites of the individual such as danger versus safety, dependence versus independence, worthlessness versus self-worth. The vertical movement relates to whole/part shifts in which a whole becomes a part of the next higher whole. The synergetic flow of both dialectical movements is depicted as a spiral of the AIP system. The second proposition suggests that the AIP system operates through cycles of differentiation and linking. These cycles separate the condensed and fragmented memory network into parts, enabling new links to occur. Differentiation and linking are also discussed in relation to dialectical attunement and mindful dual awareness. Using clinical vignettes, we illustrate how this perspective can supply the EMDR therapist a map of the client’s associative processing, enhance attuned therapeutic presence, and promote effective dialectical interweaves when processing is stuck.

The Whorfian time warp: Representing duration through the language hourglass.

How do humans construct their mental representations of the passage of time? The universalist account claims that abstract concepts like time are universal across humans. In contrast, the linguistic relativity hypothesis holds that speakers of different languages represent duration differently. The precise impact of language on duration representation is, however, unknown. Here, we show that language can have a powerful role in transforming humans' psychophysical experience of time. Contrary to the universalist account, we found language-specific interference in a duration reproduction task, where stimulus duration conflicted with its physical growth. When reproducing duration, Swedish speakers were misled by stimulus length, and Spanish speakers were misled by stimulus size/quantity. These patterns conform to preferred expressions of duration magnitude in these languages (Swedish: long/short time; Spanish: much/small time). Critically, Spanish-Swedish bilinguals performing the task in both languages showed different interference depending on language context. Such shifting behavior within the same individual reveals hitherto undocumented levels of flexibility in time representation. Finally, contrary to the linguistic relativity hypothesis, language interference was confined to difficult discriminations (i.e., when stimuli varied only subtly in duration and growth), and was eliminated when linguistic cues were removed from the task. These results reveal the malleable nature of human time representation as part of a highly adaptive information processing system. (PsycINFO Database Record

Studies on Optimization Algorithms for Some Artificial Neural Networks Based on Genetic Algorithm (GA)

Artificial Neural Networks (ANNs) are the nonlinear and adaptive information processing systems which are combined by numerous processing units, with the characteristics of self-adapting, self-organizing and real- time learning, and play an important in pattern recognition, machine learning and data mining. But we've encountered many problems, such as the selection of the structure and the parameters of the networks, the selection of the learning samples, the selection of the initial values, the convergence of the learning algorithms and so on. Genetic algorithms (GA) is a kind of random search algorithm, on one hand, it simulates the nature selection and evolution, on the other, it has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. In this paper, some optimization algorithms for ANNs with GA are studied. Firstly, an optimizing BP neural network is set up. It is using GA to optimize the connection weights of the neural network, and using GA to optimize both the connection weights and the architecture. Secondly, an optimizing RBF neural network is proposed. It used hybrid encoding method, that is, to encode the network by binary encoding and the weights by real encoding, the network architecture is self-adapted adjusted, the weights are learned, and the network is further adjusted by pseudo- inverse method or LMS method. Then they are used in real world classification tasks, and compared with the modified BP algorithm with adaptive learning rate. Experiments prove that the network got by this method has a better architecture and stronger classification ability, and the time of constructing the network artificially is saved. The algorithm is a self-adapted and intelligent learning algorithm.

Using Genetic Algorithms to Optimize Artificial Neural Networks

Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.

Generational analysis of tension and entropy in data structures: impact on automatic data integration and on the semantic web

The move toward automatic data integration from autonomous and heterogeneous sources is viewed as a transition from a closed to an open system, which is in essence an adaptive information processing system. Data definition languages from various computing eras spanning almost 50 years to date are examined, assessing if they have moved from closed systems to open systems paradigm. The study proves that contemporary data definition languages are indistinguishable from older ones using measurements of Variety, Tension and Entropy, three characteristics of complex adaptive systems (CAS). The conclusion is that even contemporary data definition languages designed for such integration exhibit closed systems characteristics along with open systems aspirations only. Plenty of good will is insufficient to make them more suitable for automatic data integration than their oldest predecessors. A previous report and these new findings set the stage for the development and proposal of a mathematically sound data definition language based on CAS, thus potentially making it better suited for automatic data integration from autonomous heterogeneous sources.

"O Lord… You Perceive my Thoughts from Afar": Recursiveness and the Evolution of Supernatural Agency

AbstractAcross religious belief systems, some supernatural agents are nearly always granted privileged epistemic access into the self's thoughts. In addition, the ethnographic literature supports the claim that, across cultures, supernatural agents are envisioned as (1) incapable of being deceived through overt behaviors; (2) preoccupied with behavior in the moral domain; (3) punitive agents who cause general misfortune to those who transgress and; (4) committed to an implicit social contract with believers that is dependent on the rules of reciprocal altruism. The present article examines the possibility that these factors comprise a developmentally based, adaptive information-processing system that increased the net genetic fitness of ancestral human beings living within complex social groups. In particular, the authors argue that fear of supernatural punishment, whether in this life or in the hereafter, encouraged the inhibition of selfish actions that were associated with "real" punishment (and thus real selective impairments) by actual group members.

Psychoactive drug use in evolutionary perspective.

Pure psychoactive drugs and direct routes of administration are evolutionarily novel features of our environment. They are inherently pathogenic because they bypass adaptive information processing systems and act directly on ancient brain mechanisms that control emotion and behavior. Drugs that induce positive emotions give a false signal of a fitness benefit. This signal hijacks incentive mechanisms of "liking" and "wanting," and can result in continued use of drugs that no longer bring pleasure. Drugs that block negative emotions can impair useful defenses, although there are several reasons why their use is often safe nonetheless. A deeper understanding of the evolutionary origins and functions of the emotions and their neural mechanisms is needed as a basis for decisions about the use of psychoactive drugs.

Shades of Minds: Towards an Understanding of Complexities of Kinds of Minds

Minds are associated with human beings, and scholars are mainly engaged in understanding human minds. However, there are also possibilities of existence of minds of others such as animals, creatures and non-biological entities. The problem of the “other minds” is well known within the philosophy of mind. In this paper, I would like to engage in ontological and epistemological queries of kinds of minds from different perspectives including: evolutionary biology, neuroscience, and the science of complexity and information. I postulate four hypotheses for the existence of minds based on complexity and material properties, rather than relying on causal properties and a complex adaptive information processing system. I consider the essence of mind to be conscious experience instead of cognitive functions alone. I argue that a complex adaptive information processing system is a pre-requisite for a mind to exist, but not a sufficient condition.

Neural networks in clinical medicine.

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications.

Classification of chip form based on AE analysis

Application of an adaptive information processing system to chip form classification on the basis of acoustic emission (AE) signals is described. The system is composed of an AE sensor, a spectrum analyzer and an adaptive empirical modeler which automatically determines the relations between features of AE power spectrum and the chip form.

Neurocomputing: picking the human brain

The nature of neurocomputing is discussed. Neurocomputing is defined as the engineering discipline concerned with nonprogrammed adaptive information processing systems (neural networks) that develop associations (transformations or mappings) between objects in response to their environment. The operation of a neural network is described, and its hardware realization is considered. Some applications of neural networks are examined.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>