One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study (...) is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery. (shrink)

A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance of each classifier (...) is assessed and validated with real created faults, achieving successful overall results. (shrink)

The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium iron (...) phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results. (shrink)

Detecting intrusions in large networks is a highly demanding task. In order to reduce the computation demand of analysing every single packet travelling along one of such networks, some years ago flows were proposed as a way of summarizing traffic information. Very few research works have addressed intrusion detection in flows from a visualizations perspective. In order to bridge this gap, the present paper proposes the application of a novel projection method (Beta Hebbian Learning) under this framework. With the aim (...) to validate this method, 8 traffic segments, containing many flows, have been analysed by means of this projection method. The promising results obtained for these segments, extracted from the University of Twente dataset, validate the proposed application. (shrink)

As it is well known, mobile phones have become a basic gadget for any individual that usually stores sensitive information. This mainly motivates the increase in the number of attacks aimed at jeopardizing smartphones, being an extreme concern above all on Android OS, which is the most popular platform in the market. Consequently, a strong effort has been devoted for mitigating mentioned incidents in recent years, even though few researchers have addressed the application of visualization techniques for the analysis of (...) malware. Within this field, the present work proposes the extension of a new technique called Hybrid Unsupervised Exploratory Plots to visualize Android malware datasets. More precisely, the novel Beta-Hebbian Learning (BHL) method is applied for the first time and validated under the frame of Hybrid Unsupervised Exploratory Plots, in conjunction with clustering methods. The informative visualization achieved provides a picture of the structure of the malware families, allowing subsequent analysis of their organization. To validate the Hybrid Unsupervised Exploratory Plot extension and its tuning, the popular Android Malware Genome dataset has been used in the experimental setting. Promising results have been obtained, suggesting that BHL applied in combination with clustering techniques in Hybrid Unsupervised Exploratory Plots are a viable resource for the visualization of malware families. (shrink)

This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set (...) of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily. (shrink)

Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing (...) data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms. (shrink)

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection (...) is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high. (shrink)

The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued with the (...) GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances. (shrink)

Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the current state (...) of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient. (shrink)

In this study, a hybrid model based on intelligent techniques is developed to predict the active power generated in a bioclimatic house by a low power wind turbine. Contrary to other researches that predict the generated power taking into account the speed and the direction of the wind, the model developed in this paper only uses the speed of the wind, measured mainly in a weather station from the government meteorological agency (MeteoGalicia). The wind speed is measured at different heights, (...) against the usual measurements in others researches, which uses the wind speed and the direction measured in a weather station on the wind turbine nacelle. The prediction is performed 30 minutes ahead, what ensures that the Building Management System knows the energy generated by the low power wind turbine 30 minutes before, and it can adapt the consumption of different equipment in the house to optimize the power use. The main objective is to allow the Building Management System to optimize the uses of energy, taking into account the predicted amount of energy that will be produced and the energy consumed in the house. The developed model uses a hybrid topology with four clusters to improve the prediction, achieving an error lower than 6.5% for Mean Absolute Error measured in a final test. To perform this test, part of the original dataset was isolated from the beginning of the training process to check the model with a dataset that is not used before, simulating the model as it is receiving new data. (shrink)

Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the (...) monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance. (shrink)

Headache disorders can be considered as the predominant neurological condition. In the field of neurological diseases, migraine was estimated to cost a total of €27 billion per year for the loss through reduced work productivity in the European Community. Medical data and information in turn provide knowledge based on which physicians make scientific decisions for diagnosis and treatments. It is, therefore, very useful to create diagnostic tools to help physicians make better decisions. This paper is focused on a new hybrid (...) clustering system combining analytic hierarchy process and weighted fuzzy c-means clustering method for diagnosing children with primary headache disorder. The proposed three-stage hybrid diagnosing system is tested on data set collected from hospitalized children in the Clinical Centre of Vojvodina, Novi Sad, Serbia. (shrink)

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning, is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by (...) means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware. (shrink)

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During (...) recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks. (shrink)

Nowadays, batteries play an important role in a lot of different applications like energy storage, electro-mobility, consumer electronic and so on. All the battery types have a common factor that is their complexity, independently of its nature. Usually, the batteries have an electrochemical nature. Several different test are accomplished to check the batteries performance, and commonly, it is predictable how they work depending of their technology. The present research describes the hybrid intelligent system created to accomplish fault detection over a (...) Lithium Iron Phosphate—LiFePO4 power cell type, commonly used in electro-mobility applications. The approach is based on the cell temperatures behaviour for voltage and current specific values. Taken into account the operating range of a real system based on a LiFePO4 cell, a large set of points of operation have been used to achieve the dataset. The different behaviour zones have been obtained by clustering as a first step. Then, different regression techniques have been used over each cluster. Polynomial regression, artificial neural networks and support vector regression were the combined techniques to develop the hybrid intelligent model proposed. The intelligent system gives very good results over the operating range, detecting all the faults tested during the validation. (shrink)

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis (...) and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification. (shrink)

Student performance and its evaluation remain a serious challenge for education systems. Frequently, the recording and processing of students’ scores in a specific curriculum have several flaws for various reasons. In this context, the absence of data from some of the student scores undermines the efficiency of any future analysis carried out in order to reach conclusions. When this is the case, missing data imputation algorithms are needed. These algorithms are capable of substituting, with a high level of accuracy, the (...) missing data for predicted values. This research presents the hybridization of an algorithm previously proposed by the authors called adaptive assignation algorithm, with a well-known technique called multivariate imputation by chained equations. The results show how the suggested methodology outperforms both algorithms. (shrink)

The harmful consequences of fossil fuels use has resulted in the promotion of clean and renewable energies. During the past decades, green technologies have experienced a strong development, paying especial attention to wind energy, that covers a significant share of the electric energy demand. In this context, the main efforts are focused on the optimization of wind generator facilities, not only in the mechanic design but also in the energy management. Then, the present work deals with the prediction of the (...) energy generated in a small wind turbine placed in a bioclimatic house located on the north west region of Spain. This includes an analysis of the characteristics of the atmospheric variables registered during the turbine operation for a period of one year and an exploratory examination of a range of regression techniques in order to assess the suitability of using the registered information to predict the installation’s power generation levels on the short term. The study detailed in this work proves that this objective is an attainable one with a good degree of accuracy. (shrink)

This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index. Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the possible (...) relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia. (shrink)

There currently is sufficient scientific evidence directly linking acquisition, exposure and prevalence of the human papillomavirus to cervical cancer. The article addresses HPV in women by taking NaProTechnology into account, which makes it posible to combine scientific evidence with ethical approaches. It looks at both the biological aspect of sexuality and the ability to become a person within one’s sexual nucleus. There is an analysis of sex education programs based on preventive health and on the anthropology of sexuality, and supported (...) by family institutions, that have demonstrated better acceptance in dealing with the risk of sexually transmitted diseases, including HPV infection. (shrink)

Este artículo detalla la labor realizada dentro del proyecto de innovación docente El Ágora que se desarrolla en la Facultad de Comunicación de la Universidad de Castilla-la Mancha con la colaboración del Ente Publico Radiotelevisión de Castilla-La Mancha. Semanalmente, los estudiantes elaboran un programa de radio de 30 minutos que se emite en la radio pública. Además, generan contenidos audiovisuales para otros canales y plataformas. A lo largo de tres años (2019-), 168 estudiantes han participado en el proyecto. Sus valoraciones (...) son positivas tanto sobre la adquisición de conocimientos como a nivel experiencial. (shrink)

This article argues that bodily awareness is a basic form of self-consciousness through which perceiving agents are directly conscious of the bodily self. It clarifies the nature of bodily awareness, categorises the different types of body-relative information, and rejects the claim that we can have a sense of ownership of our own bodies. It explores how bodily awareness functions as a form of self-consciousness and highlights the importance of certain forms of bodily awareness that share an important epistemological property with (...) canonical forms of self-consciousness such as introspection. (shrink)

It is now 25 years since Gareth Evans introduced the distinction between conceptual and nonconceptual content in The Varieties of Reference. This is a fitting time to take stock of what has become a complex and extended debate both within philosophy and at the interface between philosophy and psychology. Unfortunately, the debate has become increasingly murky as it has become increasingly ramified. Much of the contemporary discussion does not do full justice to the powerful theoretical tool originally proposed by Evans (...) and subsequently refined by theorists in the late 1980’s and early 1990’s – most effectively, I think, by Christopher Peacocke (particularly in his 1992). Even worse, significant parts of the discussion are somewhat confused. This paper makes a start on clarifying what I think ought to be the central issues in debates about nonconceptual content. I begin in §1 by pointing out how narrowly focused contemporary discussion is relative to Evans’s original discussion. We are not making as much use as we should of nonconceptual content as a tool for understanding subpersonal information processing and the complexities of its status relative to perception and thought at the personal level. In §2 I turn to what is the central focus of contemporary discussion, namely, the content of perception and identify a “master argument” for nonconceptualism based on the relation between conceptual capacities and capacities for perceptual discrimination. The aim of §3 is to clarify the relation between the claim that perception has nonconceptual content and some superficially similar claims discussed by philosophers of perception. Finally, in §4 I explain why the attention recently focused on what is sometimes called the state version of the nonconceptualist thesis seems to me to be misdirected. (shrink)

Philosophical accounts of self-deception can be divided into two broad groups – the intentionalist and the anti-intentionalist. On intentionalist models what happens in the central cases of self-deception is parallel to what happens when one person intentionally deceives another, except that deceiver and deceived are the same person. This paper offers a positive argument for intentionalism about self-deception and defends the view against standard objections.

Frames and framing make one dimension of a decision problem particularly salient. In the simplest case, framesprimeresponses (as in, e.g., the Asian disease paradigm, where the gain frame primes risk-aversion and the loss frame primes risk-seeking). But in more complicated situations frames can function reflectively, by making salient particular reason-giving aspects of a thing, outcome, or action. For Shakespeare's Macbeth, for example, his feudal commitments are salient in one frame, while downplayed in another in favor of his personal ambition. The (...) role of frames in reasoning can give rise torational framing effects. Macbeth can prefer fulfilling his feudal duty to murdering the king, while also preferring bravely taking the throne to fulfilling his feudal duty, knowing full well that bravely taking the throne just is murdering the king. Such patterns ofquasi-cyclicalpreferences can be correct and appropriate from the normative perspective of how one ought to reason. The paper explores three less dramatic types of rational framing effects: (1) Consciously framing and reframing long-term goals and short-term temptations can be important tools for self-control. (2) In the prototypical social interactions modeled by game theory, allowing for rational framing effects solves longstanding problems, such as the equilibrium selection problem and explaining the appeal of non-equilibrium solutions (e.g., the cooperative solution in the Prisoner's Dilemma). (3) Processes for resolving interpersonal conflicts and breaking discursive deadlock, because they involve internalizing multiple and incompatible ways of framing actions and outcomes, in effect create rational framing effects. (shrink)

In a recent paper, Fredérique de Vignemont has argued that there is a positive quale of bodily ownership . She thinks that tactile and other forms of somatosensory phenomenology incorporate a distinctive feeling of myness and takes issue with my defense in Bermúdez of a deflationary approach to bodily ownership. That paper proposed an argument deriving from Elizabeth Anscombe’s various discussions of what she terms knowledge without observation . De Vignemont is not convinced and appeals to the Rubber Hand Illusion (...) to undercut my appeal to Anscombe. Section 1 of this article restates the case against the putative quale of ownership. Section 2 explains why de Vignemonts’ objections miss the mark. Section 3 discusses in more detail how to draw a principled distinction between bodily awareness and ordinary perceptual awareness. (shrink)

Table of Contents Acknowledgments 1 Self-Consciousness and the Body: An Interdisciplinary Introduction by Naomi Eiland, Anthony Marcel and José Luis Bermúdez 2 The Body Image and Self-Consciousness by John Campbell 3 Infants’ Understanding of People and Things: From Body Imitation to Folk Psychology by Andrew N. Meltzoff and M. Keith Moore 4 Persons, Animals, and Bodies by Paul F. Snowdon 5 An Ecological Perspective on the Origins of Self by George Butterworth 6 Objectivity, Causality, and Agency by Thomas Baldwin (...) 7 At Two with Nature: Agency and the Development of Self-World Dualism by James Russell 8 Ecological Perception and the Notion of a Nonconceptual Point of View by José Luis Bermúdez 9 Proprioception and the Body Image by Brian O’Shaughnessy 10 Awareness of One’s Own Body: An Attentional Theory of Its Nature, Development, and Brain Basis by Marcel Kinsbourne 11 Body Schema and Intentionality by Shaun Gallagher 12 Living without Touch and Peripheral Information about Body Position and Movement: Studies with Deafferented Subjects by Jonathan Cole and Jacques Paillard 13 Bodily Awareness: A Sense of Ownership by M. G. F. Martin 14 Bodily Awareness and the Self by Bill Brewer 15 Introspection and Bodily Self-Ascription by Quassim Cassam 16 Consciousness and the Self by Naomi Eilan Contributors Index. (shrink)

My topic in this paper is social understanding. By this I mean the cognitive skills underlying social behaviour and social coordination. Normal, encultured, non-autistic and non-brain-damaged human beings are capable of an impressive degree of social coordination. We navigate the social world with a level of skill and dexterity fully comparable to that which we manifest in navigating the physical world. In neither sphere, one might think, would it be a trivial matter to identify the various competences which underly this (...) impressive level of performance. Nonetheless, at least as far as interpersonal interactions are concerned, philosophers show a rare degree of unanimity. What grounds our success in these interactions is supposed to be our common mastery of folk psychology. (shrink)

In Thinking without Words I develop a philosophical framework for treating some animals and human infants as genuine thinkers. This paper outlines the aspects of this account that are most relevant to those working in animal ethics. There is a range of different levels of cognitive sophistication in different animal species, in addition to limits to the types of thought available to non-linguistic creatures, and it may be important for animal ethicists to take this into account in exploring issues of (...) moral significance and the obligations that we might or might not have to non-human animals. (shrink)

In their recent book The Inessential Indexical Herman Cappelen and Josh Dever take issue with what has become close to philosophical orthodoxy – the view, most often associated with John Perry and David Lewis, that psychological explanations are essentially indexical. Cappelen and Dever claim that claims of essential indexicality are typically driven by intuitions rather than supported by arguments. They issue a challenge to supporters of essential indexicality: Produce an argument to back up the intuitions. This paper answers their challenge.

Debates about bodily ownership and psychological ownership have typically proceeded independently of each other. This paper explores the relation between them, with particular reference to how each is illuminated by psychopathology. I propose a general framework for studying ownership that is applicable both to bodily ownership and psychological ownership. The framework proposes studying ownership by starting with explicit judgments of ownership and then exploring the bases for those judgments. Section 3 discusses John Campbell’s account of ψ-ownership in the light of (...) that general framework, emphasizing in particular his fractionation of ψ-ownership into two dissociable components. Section 4 briefly presents an account of φ-ownership that I have developed in more detail elsewhere. Section 5 explores the suggestion, originating with Alexandre Billon, that there needs to be an integrated account of φ-ownership and ψ-ownership because depersonalization disorders typically involve breakdowns of both φ-ownership and ψ-ownership. The argument from depersonalization is not compelling, but Section 6 proposes a different way of reaching the same conclusion. Section 7 shows how reflecting on agency and practical reasoning offers a common thread between the models of φ-ownership and ψ-ownership discussed earlier in the paper. (shrink)

Philosophers have often argued that ascriptions of content are appropriate only to the personal level states of folk psychology. Against this, this paper defends the view that the familiar propositional attitudes and states defined over them are part of a larger set of cognitive proceses that do not make constitutive reference to concept possession. It does this by showing that states with nonconceptual content exist both in perceptual experience and in subpersonal information-processing systems. What makes these states content-involving is their (...) satisfaction of certain basic conditions deriving from a general account of representation-driven behaviour that is neutral on the question of concept possession. It is also argued that creatures can be in states with nonconceptual content even though they possess no conceptual abilities at all. (shrink)

Cognitive Science combines the interdisciplinary streams of cognitive science into a unified narrative in an all-encompassing introduction to the field. This text presents cognitive science as a discipline in its own right, and teaches students to apply the techniques and theories of the cognitive scientist's 'toolkit' - the vast range of methods and tools that cognitive scientists use to study the mind. Thematically organized, rather than by separate disciplines, Cognitive Science underscores the problems and solutions of cognitive science, rather than (...) those of the subjects that contribute to it - psychology, neuroscience, linguistics, etc. The generous use of examples, illustrations, and applications demonstrates how theory is applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been updated and enhanced to incorporate new studies and key experiments since the first edition. A new chapter on consciousness has also been added. (shrink)

Decision Theory and Rationality offers a challenging new interpretation of a key theoretical tool in the human and social sciences. This accessible book argues, contrary to orthodoxy in politics, economics, and management science, that decision theory cannot provide a theory of rationality.