The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Bob Rehder - One of the best experts on this subject based on the ideXlab platform.

  • how Causal Knowledge affects classification a generative theory of categorization
    Journal of Experimental Psychology: Learning Memory and Cognition, 2006
    Co-Authors: Bob Rehder, Shinwoo Kim
    Abstract:

    Several theories have been proposed regarding how Causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the Causal Knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's Causal Knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's Causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.

  • feature inference and the Causal structure of categories
    Cognitive Psychology, 2005
    Co-Authors: Bob Rehder, Russell C Burnett
    Abstract:

    The purpose of this article was to establish how theoretical category Knowledge—specifically, Knowledge of the Causal relations that link the features of categories—supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that Causal Knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes nets generally predicted participants feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes nets—the Causal Markov condition—because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a ‘‘well functioning’’ category member when it has most or all of the categorys characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

  • A Causal-Model Theory of Conceptual Representation and Categorization
    Journal of Experimental Psychology: Learning Memory and Cognition, 2003
    Co-Authors: Bob Rehder
    Abstract:

    This article presents a theory of categorization that accounts for the effects of Causal Knowledge that relates the features of categories. According to Causal-model theory, people explicitly represent the probabilistic Causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught Causal Knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this Knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, Causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.

  • Causal Knowledge and categories: the effects of Causal beliefs on categorization, induction, and similarity.
    Journal of experimental psychology. General, 2001
    Co-Authors: Bob Rehder, Reid Hastie
    Abstract:

    Despite the recent interest in the theoretical Knowledge embedded in human representations of categories, little research has systematically manipulated the structure of such Knowledge. Across four experiments this study assessed the effects of interattribute Causal laws on a number of category-based judgments. The authors found that (a) any attribute occupying a central position in a network of Causal relationships comes to dominate category membership, (b) combinations of attribute values are important to category membership to the extent they jointly confirm or violate the Causal laws, and (c) the presence of Causal Knowledge affects the induction of new properties to the category. These effects were a result of the Causal laws, rather than the empirical correlations produced by those laws. Implications for the doctrine of psychological essentialism, similarity-based models of categorization, and the representation of Causal Knowledge are discussed.

York Hagmayer - One of the best experts on this subject based on the ideXlab platform.

  • spontaneous Causal learning while controlling a dynamic system
    The Open Psychology Journal, 2010
    Co-Authors: York Hagmayer, Bjorn Meder, Magda Osman, Stefan Mangold, David A Lagnado
    Abstract:

    When dealing with a dynamic Causal system people may employ a variety of different strategies. One of these strategies is Causal learning, that is, learning about the Causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a Causal model when learning to control the state of an outcome value in a dynamic Causal system. After the control task, we modified the Causal structure of the environment and assessed decision makers' sensitivity to this manipulation. While purely instrumental Knowledge does not support inferences given the new modified structure, Causal Knowledge does. The results showed that most participants learned the structure of the underlying Causal system. However, participants acquired surprisingly little Knowledge of the system's parameters when the Causal processes that governed the system were not perceptually separated (Experiment 1). Knowl- edge improved considerably once processes were separated and feedback was made more transparent (Experiment 2). These findings indicate that even without instruction, Causal learning is a favored strategy for interacting with and control- ling a dynamic Causal system.

  • seeing versus doing two modes of accessing Causal Knowledge
    Journal of Experimental Psychology: Learning Memory and Cognition, 2005
    Co-Authors: Michael R Waldmann, York Hagmayer
    Abstract:

    The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true Causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from Causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed ("seeing") or was actively manipulated ("doing"). The predictions reflect sensitivity both to the structure of the Causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of Causal Bayes net theories provide tools for modeling this competency.

  • estimating Causal strength the role of structural Knowledge and processing effort
    Cognition, 2001
    Co-Authors: Michael R Waldmann, York Hagmayer
    Abstract:

    The strength of Causal relations typically must be inferred on the basis of statistical relations between observable events. This article focuses on the problem that there are multiple ways of extracting statistical information from a set of events. In Causal structures involving a potential cause, an effect and a third related event, the assumed Causal role of this third event crucially determines whether it is appropriate to control for this event when making Causal assessments between the potential cause and the effect. Three experiments show that prior assumptions about the Causal roles of the learning events affect the way contingencies are assessed with otherwise identical learning input. However, prior assumptions about Causal roles is only one factor influencing contingency estimation. The experiments also demonstrate that processing effort affects the way statistical information is processed. These findings provide further evidence for the interaction between bottom-up and top-down influences in the acquisition of Causal Knowledge. They show that, apart from covariation information or Knowledge about mechanisms, abstract assumptions about Causal structures also may affect the learning process.

Judea Pearl - One of the best experts on this subject based on the ideXlab platform.

Miquel Payaro - One of the best experts on this subject based on the ideXlab platform.

  • On the Precoder Design of a Wireless Energy Harvesting Node in Linear Vector Gaussian Channels with Arbitrary Input Distribution
    2013
    Co-Authors: Maria Gregori, Student Member, Miquel Payaro
    Abstract:

    in linear vector Gaussian channels with arbitrarily distributed input symbols is considered in this paper. The precoding strategy that maximizes the mutual information along N independent channel accesses is studied under non-Causal Knowledge of the channel state and harvested energy (commonly known as offline approach). It is shown that, at each channel use, the left singular vectors of the precoder are equal to the eigenvectors of the Gram channel matrix. Additionally, an expression that relates the optimal singular values of the precoder with the energy harvesting profile through the Minimum Mean-Square Error (MMSE) matrix is obtained. Then, the specific situation in which the right singular vectors of the precoder are set to the identity matrix is considered. In this scenario, the optimal offline power allocation, named Mercury Water-Flowing, is derived and an intuitive graphical representation is presented. Two optimal offline algorithms to compute the Mercury Water-Flowing solution are proposed and an exhaustive study of their computational complexity is performed. Moreover, an online algorithm is designed, which only uses Causal Knowledge of the harvested energy and channel state. Finally, the achieved mutual information is evaluated through simulation. Index Terms—Energy harvesting, mutual information, arbitrary input distribution, precoder optimization, power allocation, linear vector Gaussian channels, MMSE. I

  • on the precoder design of a wireless energy harvesting node in linear vector gaussian channels with arbitrary input distribution
    arXiv: Information Theory, 2013
    Co-Authors: Maria Gregori, Miquel Payaro
    Abstract:

    A Wireless Energy Harvesting Node (WEHN) operating in linear vector Gaussian channels with arbitrarily distributed input symbols is considered in this paper. The precoding strategy that maximizes the mutual information along N independent channel accesses is studied under non-Causal Knowledge of the channel state and harvested energy (commonly known as offline approach). It is shown that, at each channel use, the left singular vectors of the precoder are equal to the eigenvectors of the Gram channel matrix. Additionally, an expression that relates the optimal singular values of the precoder with the energy harvesting profile through the Minimum Mean-Square Error (MMSE) matrix is obtained. Then, the specific situation in which the right singular vectors of the precoder are set to the identity matrix is considered. In this scenario, the optimal offline power allocation, named Mercury Water-Flowing, is derived and an intuitive graphical representation is presented. Two optimal offline algorithms to compute the Mercury Water- Flowing solution are proposed and an exhaustive study of their computational complexity is performed. Moreover, an online algorithm is designed, which only uses Causal Knowledge of the harvested energy and channel state. Finally, the achieved mutual information is evaluated through simulation.

  • optimal power allocation for a wireless multi antenna energy harvesting node with arbitrary input distribution
    International Conference on Communications, 2012
    Co-Authors: Maria Gregori, Miquel Payaro
    Abstract:

    The lifetime of autonomous wireless nodes can be extended by using energy harvesters in the node. This additional source of energy implies a loss of optimality of the traditional transmission strategies such as waterfilling. In this paper, the precoding strategy along N independent channel uses that maximizes the mutual information in linear vector Gaussian channels for arbitrarily distributed inputs is studied, under non-Causal Knowledge of the harvested energy over time. Precisely, the optimal right singular vectors and singular values of each precoder are derived. The derivation of the left singular vectors is a computationally hard problem, similarly as in the case of non-harvesting nodes, and is left as an open problem. At each channel use, the observed channel is diagonalized and power is allocated following the proposed MIMO Mercury Water-Flowing solution.

Anant Sahai - One of the best experts on this subject based on the ideXlab platform.

  • writing on rayleigh faded dirt a computable upper bound to the outage capacity
    International Symposium on Information Theory, 2007
    Co-Authors: Pulkit Grover, Anant Sahai
    Abstract:

    A transmitter may have non-Causal Knowledge of the interference signal being transmitted by another user. Recently, Tarokh and others have raised the possibility of exploiting this Knowledge to increase the data rates of a cognitive radio. However, there is a difference between knowing the signal transmitted by the primary and the actual interference at our receiver since there is a wireless channel between these two points. This raises the interesting problem of finding the achievable rates for a compound Gel'fand-Pinsker channel. The problem was addressed recently in the work by Mitran et al, where the authors gave some upper and lower bounds to the achievable rates, with an emphasis on fading channels. But the upper bounds in that work are sometimes non-computable. In this work, we derive computable upper bounds on the outage capacity for a channel where the primary signal can be Rayleigh faded at our receiver.