31,497 research outputs found

    Implicit cognitions in awareness: Three empirical examples and implications for conscious identity.

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    open accessAcross psychological science the prevailing view of mental events includes unconscious mental representations that result from a separate implicit system outside of awareness. Recently, scientific interest in consciousness of self and the widespread application of mindfulness practice have made necessary innovative methods of assessing awareness during cognitive tasks and validating those assessments wherever they are researched. Studies from three areas of psychology, self-esteem, sustainability thinking, and the learning of control systems questioned the unconscious status of implicit cognitions. The studies replicated published results using methods of investigating (a) unselective learning of a control task (b) implicit attitudes using IAT, and (c) the Name-letter effect. In addition, a common analytic method of awareness assessment and its validation was used. Study 1 demonstrated that learned control of a dynamic system was predicted by the validity of rules of control in awareness. In Study 2, verbal reports of hesitations and trial difficulty predicted IAT scores for 34 participants’ environmental attitudes. In Study 3, the famous Name-letter effect was predicted by the validity of university students’ reported awareness of letter preference reasons. The repeated finding that self knowledge in awareness predicted what should be cognitions outside of awareness, according to the dual processing view, suggests an alternative model of implicit mental events in which associative relations evoke conscious symbolic representations. The analytic method of validating phenomenal reports will be discussed along with its potential contribution to research involving implicit cognitions

    Technological Change and Institutions: A Case Study

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    The arena of mobile telecommunication in Europe has undergone a technological transition from analogue (first generation) to digital (second generation) technologies. While this transition is immediately attributable to shifts in demand and supply patterns, closer examination reveals that there are numerous other intervening factors that have facilitated this transition. This paper utilizes a conceptual framework for institutional analysis developed in earlier work to identify and discuss some of these factors. The paper concludes with a discussion of the implications from this study for an institutional perspective on technological change.economics of technology ;

    Neurocognitive Informatics Manifesto.

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    Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

    A ferrofluid based neural network: design of an analogue associative memory

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    We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.Comment: submitted to Neural Network

    Affective neuroscience, emotional regulation, and international relations

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    International relations (IR) has witnessed an emerging interest in neuroscience, particularly for its relevance to a now widespread scholarship on emotions. Contributing to this scholarship, this article draws on the subfields of affective neuroscience and neuropsychology, which remain largely unexplored in IR. Firstly, the article draws on affective neuroscience in illuminating affect's defining role in consciousness and omnipresence in social behavior, challenging the continuing elision of emotions in mainstream approaches. Secondly, it applies theories of depth neuropsychology, which suggest a neural predisposition originating in the brain's higher cortical regions to attenuate emotional arousal and limit affective consciousness. This predisposition works to preserve individuals' self-coherence, countering implicit assumptions about rationality and motivation within IR theory. Thirdly, it outlines three key implications for IR theory. It argues that affective neuroscience and neuropsychology offer a route towards deep theorizing of ontologies and motivations. It also leads to a reassessment of the social regulation of emotions, particularly as observed in institutions, including the state. It also suggests a productive engagement with constructivist and poststructuralist approaches by addressing the agency of the body in social relations. The article concludes by sketching the potential for a therapeutically-attuned approach to IR

    Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

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    Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

    A Dynamic Approach to Recognition Memory

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    Thesis (Ph.D.) - Indiana University,Psychological and Brain Sciences/Cognitive Science, 2015We argue that taking a dynamic approach to the understanding of memory will lead to advances that are not possible via other routes. To that end, we present a model of recognition memory that specifies how memory retrieval and recognition decisions jointly evolve over time and show that it is able to jointly predict accuracy, response time, and speed-accuracy trade-off functions. The model affords insights into the effects of study time, list length, and instructions. The model leads to a novel qualitative and quantitative test of the source of word frequency effects in recognition, showing that the relatively high distinctiveness of the features of low frequency words provide the best account. We also show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process
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