464 research outputs found

    Information-Theoretic Perspectives on Unconscious Priming and Group Decisions: Retrieving Maximum Information From Human Responses

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    Seitdem die Informationstheorie in die psychologische Forschungsliteratur wäh-rend der Mitte des letzten Jahrhunderts eingeführt worden ist, hat das Thema Informationsverarbeitung im menschlichen Gehirn mehr und mehr an Aufmerksamkeit gewonnen. Von besonderem Interesse war die Frage, zu welchem Ausmaß Menschen Informationen unterbewusst verarbeiten können. Um unterbewusste Informationsverarbeitung nachzuweisen, war in den letzten zwei Jahrzehnten eines der prominentesten Paradigmen das unterbewusste Priming. Studien in diesem Paradigma folgen häufig einem \emph{Standardverfahren}: Diese Studien zeigen, dass Versuchsteilnehmer nahe am Rateniveau sind, wenn sie kaum sichtbare Stimuli identifizieren sollen. Gleichzeitig produzieren dieselben Stimuli eindeutige Effekte in indirekten Messungen wie zum Beispiel in Reaktionszeiten oder in Gehirnaktivierung. Von diesem Ergebnismuster wird im Standardverfahren geschlussfolgert, dass die Versuchsteilnehmer mehr Information über die Stimuli verarbeitet haben, als ihnen bewusst ist. Wir zeigen hier, dass das Standardverfahren fehlerhaft ist. Die Effekte auf die indirekten Messungen können meist vollständig durch residuale, bewusste Verarbeitung erklärt werden. Diese schwache, bewusste Verarbeitung zeigt sich in der Identifikationsleistung der Studienteilnehmer, welche zwar nahe am Zufallsniveau aber doch nicht exakt auf diesem liegt. Der irreführende Eindruck einer überlegenen, unterbewussten Verarbeitung entsteht durch einen methodisch unangemessenen Vergleich. Dabei erscheinen die direkt gegebenen Antworten, als basierten sie auf weniger Information über die Stimuli als die indirekten Messungen. Wir entwickeln hier eine Reanalysemethode für Ergebnisse aus früheren Studien und zeigen, dass große Teile der vielzitierten Forschungsliteratur zu unterbewusstem Priming wenig bis keine Beweise für die weitreichenden Interpretationen über unterbewusste Verarbeitung liefern. Im Forschungsfeld Gruppenentscheidungen gibt es einen analogen Fehler. In solchen Studien werden echte Gruppenentscheidungsprozesse simuliert, indem Aussagen individueller Gruppenmitglieder statistisch zusammengeführt werden. Diese statistischen Zusammenführungen dienen als \emph{simulierte} Gruppenentscheidungen, die dann mit den \emph{echten} Gruppenentscheidungen aus interaktiven Gruppendiskussionen verglichen werden. Ein Ergebnis solcher Studien ist, dass echte Gruppen häufiger korrekte Entscheidungen treffen als simulierte Gruppen. Aber die meisten Studien nutzen nicht die theoretisch optimale Methode, Mehrheitsbeschluss mit Stimmgewichtung (Confidence Weighted Majority Voting, CWMV). Ähnlich zum Problem beim unterbewussten Priming führen suboptimale Methoden bei der Simulation von Gruppenentscheidungen potenziell zu ungerechtfertigten Interpretationen beim Vergleich von echten mit simulierten Gruppen. Unterschiede können allein durch methodische Disparitäten auftreten und dürfen nicht ohne weiteres auf einen zugrundeliegenden, wahren Unterschied zwischen echten und simulierten Gruppen zurückgeführt werden. Wir stellen die theoretisch optimale Methode CWMV in den Blickpunkt und zeigen in einem Experiment, dass diese Methode gleiche Vorhersagegenauigkeit wie echte Gruppenentscheidungen erreicht. Das macht CWMV zu einem geeigneten Kandidaten, um echte Gruppenprozesse zu modellieren. Obwohl die Vorhersagegenauigkeit übereinstimmt, unterscheiden sich echte Gruppen systematisch von den Simulationen mit CWMV. Wir modellieren diese Abweichungen und zeigen, dass echte Gruppen die Aussagen ihrer Mitglieder gleichmäßiger gewichten und insgesamt weniger Sicherheitsbewertung in die Gruppenentscheidung legen als Simulationen mittels CWMV. Beide Forschungsbereiche -- unterbewusstes Priming und Gruppenentscheidungsprozesse -- vereint, dass die volle Information in den Antworten der Versuchsteilnehmer oft nicht vollständig berücksichtigt wird. Unsere Ergebnisse im Bereich der Gruppenentscheidungen werfen die zu-sätzliche Frage auf, ob die Vorhersagegenauigkeit der einzelnen Gruppenmitglieder die \emph{Vorhersagegenauigkeit der Gruppe} bestimmt. Diese Frage ist insbesondere im Bereich des maschinellen Lernens relevant, bei der nicht Menschen eine Gruppe bilden sondern einzelne Klassifikationsalgorithmen ein sogenanntes Ensemble. Wir erarbeiten hier ein Modell, in dem wir ein Negativergebnis nachweisen: Die Vorhersagegenauigkeit des Ensembles kann Werte in einer überraschend breiten Spanne annehmen, selbst wenn die Vorhersagegenauigkeit der einzelnen Klassifikationsalgorithmen konstant gehalten wird. Der Grund liegt in dem drastisch unterschiedlichen Informationsgehalt, den ein Klassifikationsalgorithmus trotz gleich gehaltener Genauigkeit übertragen kann. Wir beweisen, welche Vorhersagegenauigkeit eine Ensemble im besten und schlechtesten Fall bei gegebener Genauigkeit der einzelnen Algorithmen annehmen kann. Zusätzlich beweisen wir engere Schranken für den Fall, dass nicht nur die Klassifikationsgenauigkeit sondern auch der Informationsgehalt der einzelnen Algorithmen gegeben ist. Aus unseren konstruktiven Beweisen gehen Prinzipien für die Auswahl und Implementation von Klassifikationsalgorithmen für die Verwendung in Ensembles hervor. Diese Prinzipien gehen über die einfache Heuristik hinaus, dass Klassifikationsalgorithmen mit höherem Informationsgehalt gewählt werden sollten. Diese drei Forschungsgegenstände unterstreichen die Relevanz von Aspekten menschlicher und maschineller Vorhersagen, welche jenseits der Vorhersagegenauigkeit liegen. Diese Aspekte sind auf den ersten Blick leicht übersehen, da viele psychologische Forschungsbereiche sich auf das herkömmliche Maß der Klassifikationsgenaugkeit beschränken. Sie spielen nichtsdestotrotz eine wichtige thereotische und praktische Rolle, wie wir in den drei Bereichen zeigen.Since Information Theory was introduced to the field of psychology in the middle of the last century, Information processing in the human brain has gained attention. A question of particular interest has been: To what degree can humans process information unconsciously? For the past two decades, one of the most prominent paradigms in which this question has been investigated was \emph{unconscious priming}. Studies in this paradigm have frequently used a \emph{standard reasoning}: These studies show that participants perform close to random guessing when they have to identify barely visible stimuli but the same stimuli nevertheless produce clear effects in indirect measures such as reaction times or neuroimaging measures. From this pattern of results, the standard reasoning concludes that participants processed information about the stimuli beyond what they are consciously aware of. But we show here that the standard reasoning is flawed. The clear effects in indirect measures can often be fully explained by residual conscious processing that is reflected in participants' close to (but not exactly equal to) chance level guessing in consciously given responses. The erroneous appearance of more unconscious processing is due to an inappropriate comparison making conscious responses appear as if they were based on less information than the indirect measures. We develop a reanalysis method for results from these studies and demonstrate that a large body of heavily cited literature in the paradigm has little to no evidence for their strong claims on unconscious processing. In the field of group decision making, a similar methodological problem occurs. Here, researchers aim to model real group discussions via statistical aggregations of individual group members' responses. The statistically aggregated responses serve as \emph{simulated} group decisions that are then compared to the \emph{real} group decisions coming from an interactive group discussion. A common result is that real group decisions are more accurate than simulated group decisions. But most studies do not use the theoretically optimal method of Confidence Weighted Majority Voting (CWMV) to simulate group decisions. Similar to unconscious priming, suboptimal methods for simulations lead to inappropriate comparisons between simulated vs. real decisions. This in turn may lead to unwarranted interpretations due to methodological bias. We bring forward the theoretically optimal method and demonstrate in an experiment that simulated and real group decisions are equally accurate with this method. Despite matching in accuracy, real groups systematically deviate from CWMV simulations. We capture these deviations in a formal cognitive model showing that real groups treat each group member's vote more equally than CWMV predicts. Moreover, real groups exhibit an overall lower confidence than CWMV simulations. What ties group decision making and unconscious priming together is that the full information from human participants' responses was not fully taken into account when making comparisons. Our results raise the additional question of whether, based on the accuracies of the individuals, we can a priori determine the \emph{accuracy of the group}. This is particularly interesting in machine learning where not individual humans form a group but individual classifiers form an ensemble. We introduce a model in which we demonstrate a negative result: The ensemble accuracy can take values in a surprisingly large range even when the individual classifiers' accuracies are held constant. This is because individual classifiers with a fixed accuracy can still convey drastically varying amounts of information. We prove best- and worst-case ensemble accuracies for when the individual classifiers' accuracies are known. Additionally, we provide tighter bounds for cases in which not only accuracies but also individual classifiers' transmitted information is known. Our constructive proofs yield guiding principles for selecting and constructing classifiers for ensembles. These principles go beyond the simple notion of preferring classifiers with highest mutual information. These three strands of research highlight the relevance of certain aspects from responses given by humans or classifiers that go beyond classification accuracy. Such aspects are prima facie easily overlooked in many scenarios. But they still affect mutual information measures and can have theoretical and practical impact as we demonstrate in unconscious priming, decision making, and ensemble accuracy

    Quantification Learning with Applications to Mortality Surveillance

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    \chapter*{Abstract} This thesis is motivated by estimating the cause specific mortality fraction (CSMF) for children deaths in Mozambique. In countries where many deaths are not assigned a cause of death, CSMF estimation is often performed by performing a verbal autopsy (VA) for a large number of deaths. A cause for each VA is then assigned via one or more computer coded verbal autopsy (CCVA) algorithms, and these cause assignments are aggregated to estimate the CSMF. We show that CSMF estimation from CCVAs is poor if there is substantial misclassification due to CCVAs being informed by non-local data. We develop a parsimonious Bayesian hierarchical model that uses a small set of labeled data that includes deaths with both a VA and a gold-standard cause of death. The labeled data is used to learn the misclassification rates from one or multiple CCVAs, and in-turn these estimated rates are used to produce a calibrated CSMF estimate. A shrinkage prior ensures that the CSMF estimate from our Bayesian model coincides with that from a CCVA in the case of no labeled data. To handle probabilistic CCVA predictions and labels, we develop an estimating equations approach that uses the Kullback-Liebler loss-function for transformation-free regression with a compositional outcome and predictor. We then use Bayesian updating of this loss function, which allows for calibrated CSMF estimation from probabilistic predictions and labels. This method is not limited to CSMF estimation and can be used for general quantification learning, which is prevalence estimation for a test population using predictions from a classifier derived from training data. Finally, we obtain CSMF estimates for child deaths in Mozambique by applying all of the developed methods to VA data collected from the Countrywide Mortality Surveillance for Action (COMSA)-Mozambique and VA and gold-standard COD data collected from the Child Health and Mortality Prevention project

    Methods and Systems for Fault Diagnosis in Nuclear Power Plants

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    This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

    The Constructive Nature of Color Vision and Its Neural Basis

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    Our visual world is made up of colored surfaces. The color of a surface is physically determined by its reflectance, i.e., how much energy it reflects as a function of wavelength. Reflected light, however, provides only ambiguous information about the color of a surface as it depends on the spectral properties of both the surface and the illumination. Despite the confounding effects of illumination on the reflected light, the visual system is remarkably good at inferring the reflectance of a surface, enabling observers to perceive surface colors as stable across illumination changes. This capacity of the visual system is called color constancy and it highlights that color vision is a constructive process. The research presented here investigates the neural basis of some of the most relevant aspects of the constructive nature of human color vision using machine learning algorithms and functional neuroimaging. The experiments demonstrate that color-related prior knowledge influences neural signals already in the earliest area of visual processing in the cortex, area V1, whereas in object imagery, perceived color shared neural representations with the color of the imagined objects in human V4. A direct test for illumination-invariant surface color representation showed that neural coding in V1 as well as a region anterior to human V4 was robust against illumination changes. In sum, the present research shows how different aspects of the constructive nature of color vision can be mapped to different regions in the ventral visual pathway

    Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

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    Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naïve Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis

    Evidence-Based Managerial Decision-Making With Machine Learning: The Case of Bayesian Inference in Aviation Incidents

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    Understanding the factors behind aviation incidents is essential, not only because of the lethality of the accidents but also the incidents’ direct and indirect economic impact. Even minor incidents trigger significant economic damage and create disruptions to aviation operations. It is crucial to investigate these incidents to understand the underlying reasons and hence, reduce the risk associated with physical and financial safety in a precarious industry like aviation. The findings may provide decision-makers with a causally accurate means of investigating the topic while untangling the difficulties concerning the statistical associations and causal effects. This research aims to identify the significant variables and their probabilistic dependencies/relationships determining the degree of aircraft damage. The value and the contribution of this study include (1) developing a fully automatic ML prediction based DSS for aircraft damage severity, (2) conducting a deep network analysis of affinity between predicting variables using probabilistic graphical modeling (PGM), and (3) implementing a user-friendly dashboard to interpret the business insight coming from the design and development of the Bayesian Belief Network (BBN). By leveraging a large, real-world dataset, the proposed methodology captures the probability-based interrelations among air terminal, flight, flight crew, and air-vehicle-related characteristics as explanatory variables, thereby revealing the underlying, complex interactions in accident severity. This research contributes significantly to the current body of knowledge by defining and proving a methodology for automatically categorizing aircraft damage severity based on flight, aircraft, and PIC (pilot in command) information. Moreover, the study combines the findings of the Bayesian Belief Networks with decades of aviation expertise of the subject matter expert, drawing and explaining the association map to find the root causes of the problems and accident relayed variables

    A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

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    Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results
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