Results for 'statistical structure'

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  1. Statistical structure and sequence-specific learning in a serial rt task.Ma Stadler - 1990 - Bulletin of the Psychonomic Society 28 (6):518-518.
  2.  2
    Perception of the statistical structure of a random series of binary symbols.Harold W. Hake & Ray Hyman - 1953 - Journal of Experimental Psychology 45 (1):64.
  3. How the statistical structure of the environment affects perception of the Müller-Lyer illusion.Stephen Blessing & Martina Svetlik - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 827--832.
     
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  4.  2
    The effect of order of approximation to the statistical structure of English on the emission of verbal responses.Kurt Salzinger, Stephanie Portnoy & Richard S. Feldman - 1962 - Journal of Experimental Psychology 64 (1):52.
  5.  16
    Segmenting dynamic human action via statistical structure.Dare Baldwin, Annika Andersson, Jenny Saffran & Meredith Meyer - 2008 - Cognition 106 (3):1382-1407.
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  6.  6
    Quantum probability, choice in large worlds, and the statistical structure of reality.Don Ross & James Ladyman - 2013 - Behavioral and Brain Sciences 36 (3):305-306.
    Classical probability models of incentive response are inadequate in where the dimensions of relative risk and the dimensions of similarity in outcome comparisons typically differ. Quantum probability models for choice in large worlds may be motivated pragmatically or metaphysically: statistical processing in the brain adapts to the true scale-relative structure of the universe.
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  7.  28
    Changing Structures in Midstream: Learning Along the Statistical Garden Path.Andrea L. Gebhart, Richard N. Aslin & Elissa L. Newport - 2009 - Cognitive Science 33 (6):1087-1116.
    Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low‐level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit (...)
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  8.  5
    Phenomenological Structure for the Large Deviation Principle in Time-Series Statistics: A method to control the rare events in non-equilibrium systems.Takahiro Nemoto - 2016 - Singapore: Imprint: Springer.
    This thesis describes a method to control rare events in non-equilibrium systems by applying physical forces to those systems but without relying on numerical simulation techniques, such as copying rare events. In order to study this method, the book draws on the mathematical structure of equilibrium statistical mechanics, which connects large deviation functions with experimentally measureable thermodynamic functions. Referring to this specific structure as the "phenomenological structure for the large deviation principle", the author subsequently extends it (...)
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  9.  9
    Sophisticated Statistics Cannot Compensate for Method Effects If Quantifiable Structure Is Compromised.Damian P. Birney, Jens F. Beckmann, Nadin Beckmann & Steven E. Stemler - 2022 - Frontiers in Psychology 13.
    Researchers rely on psychometric principles when trying to gain understanding of unobservable psychological phenomena disconfounded from the methods used. Psychometric models provide us with tools to support this endeavour, but they are agnostic to the meaning researchers intend to attribute to the data. We define method effects as resulting from actions which weaken the psychometric structure of measurement, and argue that solution to this confounding will ultimately rest on testing whether data collected fit a psychometric model based on a (...)
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  10. Two Statistical Problems for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays.Tianjaio Chu - unknown
    Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper (...)
     
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  11.  5
    Two statistical problems for inference to regulatory structure from associations of Gene expression measurements with microarrays.Clark Glymour - unknown
    Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper (...)
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  12.  17
    The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many (...)
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  13.  15
    Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling.Clark Glymour, Richard Scheines, Peter Spirtes & Kevin Kelly - 1987 - Academic Press.
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  14.  6
    Structured statistical models of inductive reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  15.  6
    Statistically Induced Chunking Recall: A Memory‐Based Approach to Statistical Learning.Erin S. Isbilen, Stewart M. McCauley, Evan Kidd & Morten H. Christiansen - 2020 - Cognitive Science 44 (7):e12848.
    The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants (...)
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  16.  9
    The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many (...)
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  17.  6
    The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth.Mark Steyvers & Joshua B. Tenenbaum - 2005 - Cognitive Science 29 (1):41-78.
    We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many (...)
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  18.  4
    “Structured statistical models of inductive reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  19.  2
    Structure of quasicrystals described by statistical methods.B. Kozakowski, J. Wolny & P. Kuczera - 2008 - Philosophical Magazine 88 (13-15):1921-1927.
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  20.  3
    Statistical approach to multiple-qmodulated structures: average Patterson analysis.Grzegorz Urban & Janusz Wolny † - 2004 - Philosophical Magazine 84 (27):2905-2918.
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  21.  3
    Statistical Identification of Parameters for Damaged FGM Structures with Material Uncertainties in Thermal Environment.Yalan Xu, Yu Qian & Kongming Guo - 2018 - Complexity 2018:1-21.
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  22.  5
    Structural priming is most useful when the conclusions are statistically robust.Kyle Mahowald, Ariel James, Richard Futrell & Edward Gibson - 2017 - Behavioral and Brain Sciences 40.
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  23.  12
    Discovering syntactic deep structure via Bayesian statistics.Jason Eisner - 2002 - Cognitive Science 26 (3):255-268.
    In the Bayesian framework, a language learner should seek a grammar that explains observed data well and is also a priori probable. This paper proposes such a measure of prior probability. Indeed it develops a full statistical framework for lexicalized syntax. The learner's job is to discover the system of probabilistic transformations (often called lexical redundancy rules) that underlies the patterns of regular and irregular syntactic constructions listed in the lexicon. Specifically, the learner discovers what transformations apply in the (...)
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  24.  17
    Multimodal integration in statistical learning: evidence from the McGurk illusion.Aaron D. Mitchel, Morten H. Christiansen & Daniel J. Weiss - 2014 - Frontiers in Psychology 5:85721.
    Recent advances in the field of statistical learning have established that learners are able to track regularities of multimodal stimuli, yet it is unknown whether the statistical computations are performed on integrated representations or on separate, unimodal representations. In the present study, we investigated the ability of adults to integrate audio and visual input during statistical learning. We presented learners with a speech stream synchronized with a video of a speaker’s face. In the critical condition, the visual (...)
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  25.  8
    Solving probabilistic and statistical problems: a matter of information structure and question form.Vittorio Girotto & Michel Gonzalez - 2001 - Cognition 78 (3):247-276.
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  26.  30
    Galton's Blinding Glasses. Modern Statistics Hiding Causal Structure in Early Theories of Inheritance.Bert Leuridan - 2007 - In Federica Russo & Jon Williamson (eds.), Causality and Probability in the Sciences. College Publications. pp. 243--262.
    ABSTRACT. Probability and statistics play an important role in contemporary -philosophy of causality. They are viewed as glasses through which we can see or detect causal relations. However, they may sometimes act as blinding glasses, as I will argue in this paper. In the 19th century, Francis Galton tried to statistically analyze hereditary phenomena. Although he was a far better statistician than Gregor Mendel, his biological theory turned out to be less fruitful. This was no sheer accident. His knowledge of (...)
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  27.  5
    Base-rate respect: From statistical formats to cognitive structures.Aron K. Barbey & Steven A. Sloman - 2007 - Behavioral and Brain Sciences 30 (3):287-292.
    The commentaries indicate a general agreement that one source of reduction of base-rate neglect involves making structural relations among relevant sets transparent. There is much less agreement, however, that this entails dual systems of reasoning. In this response, we make the case for our perspective on dual systems. We compare and contrast our view to the natural frequency hypothesis as formulated in the commentaries.
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  28. The Statistical Nature of Causation.David Papineau - 2022 - The Monist 105 (2):247-275.
    Causation is a macroscopic phenomenon. The temporal asymmetry displayed by causation must somehow emerge along with other asymmetric macroscopic phenomena like entropy increase and the arrow of radiation. I shall approach this issue by considering ‘causal inference’ techniques that allow causal relations to be inferred from sets of observed correlations. I shall show that these techniques are best explained by a reduction of causation to structures of equations with probabilistically independent exogenous terms. This exogenous probabilistic independence imposes a recursive order (...)
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  29.  8
    Trans-statistical Behavior of a Multiparticle System in an Ontology of Properties.Matías Pasqualini & Sebastian Fortin - 2022 - Foundations of Physics 52 (4):1-19.
    In the last years, the surprising bosonic behavior that a many-fermion system may acquire has raised interest because of theoretical and practical reasons. This trans-statistical behavior is usually considered to be the result of approximation modeling methods generally employed by physicists when faced with complexity. In this paper, we take a tensor product structure and an ontology of properties approach and provide two versions of a toy model in order to argue that trans-statistical behavior allows for a (...)
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  30.  3
    A framework for statistical modelling of plastic yielding initiated cleavage fracture of structural steels.Wei-Sheng Lei - 2016 - Philosophical Magazine 96 (35):3586-3631.
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  31.  13
    Applications of quantum statistics in psychological studies of decision processes.Diedrik Aerts & Sven Aerts - 1995 - Foundations of Science 1 (1):85-97.
    We present a new approach to the old problem of how to incorporate the role of the observer in statistics. We show classical probability theory to be inadequate for this task and take refuge in the epsilon-model, which is the only model known to us caapble of handling situations between quantum and classical statistics. An example is worked out and some problems are discussed as to the new viewpoint that emanates from our approach.
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  32.  13
    Uncovering the Richness of the Stimulus: Structure Dependence and Indirect Statistical Evidence.Florencia Reali & Morten H. Christiansen - 2005 - Cognitive Science 29 (6):1007-1028.
    The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses of child-directed speech showing that there is indirect statistical information useful for correct auxiliary fronting in polar interrogatives and that such information is sufficient for distinguishing between grammatical and (...)
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  33.  10
    Quantum Bose–Einstein Statistics for Indistinguishable Concepts in Human Language.Lester Beltran - 2021 - Foundations of Science 28 (1):43-55.
    We investigate the hypothesis that within a combination of a ‘number concept’ plus a ‘substantive concept’, such as ‘eleven animals’, the identity and indistinguishability present on the level of the concepts, i.e., all eleven animals are identical and indistinguishable, gives rise to a statistical structure of the Bose–Einstein type similar to how Bose–Einstein statistics is present for identical and indistinguishable quantum particles. We proceed by identifying evidence for this hypothesis by extracting the statistical data from the World-Wide-Web (...)
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  34.  8
    Combining Statistical-Thermodynamics and Relativity Theory: Methodological and Foundations Problems.John Earman - 1978 - PSA Proceedings of the Biennial Meeting of the Philosophy of Science Association 1978 (2):156-185.
    Classical statistical mechanics has commanded a modest but steady amount of attention from philosophers of science. By contrast, there has been an almost total neglect of relativistic statistical mechanics, or more precisely, a neglect of the prospects and problems of producing a relativistic version of classical statistical mechanics. The neglect is undeserved, for this area offers a fascinating array of case studies for those concerned with the history and sociology of science, with the structure and dynamics (...)
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  35.  11
    Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.Chi-Hsin Chen, Lisa Gershkoff-Stowe, Chih-Yi Wu, Hintat Cheung & Chen Yu - 2017 - Cognitive Science 41 (6):1485-1509.
    Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross‐situational learning paradigm to test whether English speakers were able to use co‐occurrences to learn word‐to‐object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership (...)
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  36.  5
    How Statistical Learning Can Play Well with Universal Grammar.Lisa S. Pearl - 2021 - In Nicholas Allott, Terje Lohndal & Georges Rey (eds.), A Companion to Chomsky. Wiley. pp. 267–286.
    A key motivation for Universal Grammar (UG) is developmental: UG can help children acquire the linguistic knowledge that they do as quickly as they do from the data that's available to them. Some of the most fruitful recent work in language acquisition has combined ideas about different hypothesis space building blocks with domain‐general statistical learning. Statistical learning can then provide a way to help navigate the hypothesis space in order to converge on the correct hypothesis. Reinforcement learning is (...)
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  37.  6
    Two Approaches to Fractional Statistics in the Quantum Hall Effect: Idealizations and the Curious Case of the Anyon.Elay Shech - 2015 - Foundations of Physics 45 (9):1063-1100.
    This paper looks at the nature of idealizations and representational structures appealed to in the context of the fractional quantum Hall effect, specifically, with respect to the emergence of anyons and fractional statistics. Drawing on an analogy with the Aharonov–Bohm effect, it is suggested that the standard approach to the effects— the topological approach to fractional statistics—relies essentially on problematic idealizations that need to be revised in order for the theory to be explanatory. An alternative geometric approach is outlined and (...)
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  38.  6
    Statistical Practice: Putting Society on Display.Michael Mair, Christian Greiffenhagen & W. W. Sharrock - 2016 - Theory, Culture and Society 33 (3):51-77.
    As a contribution to current debates on the ‘social life of methods’, in this article we present an ethnomethodological study of the role of understanding within statistical practice. After reviewing the empirical turn in the methods literature and the challenges to the qualitative-quantitative divide it has given rise to, we argue such case studies are relevant because they enable us to see different ways in which ‘methods’, here quantitative methods, come to have a social life – by embodying and (...)
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  39. New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic Plithogenic Optimizations.Florentin Smarandache & Yanhui Guo - 2022 - Basel, Switzerland: MDPI.
    This volume presents state-of-the-art papers on new topics related to neutrosophic theories, such as neutrosophic algebraic structures, neutrosophic triplet algebraic structures, neutrosophic extended triplet algebraic structures, neutrosophic algebraic hyperstructures, neutrosophic triplet algebraic hyperstructures, neutrosophic n-ary algebraic structures, neutrosophic n-ary algebraic hyperstructures, refined neutrosophic algebraic structures, refined neutrosophic algebraic hyperstructures, quadruple neutrosophic algebraic structures, refined quadruple neutrosophic algebraic structures, neutrosophic image processing, neutrosophic image classification, neutrosophic computer vision, neutrosophic machine learning, neutrosophic artificial intelligence, neutrosophic data analytics, neutrosophic deep learning, and neutrosophic (...)
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  40.  5
    Statistical Indicators System regarding Religious Phenomena.Claudiu Herteliu - 2007 - Journal for the Study of Religions and Ideologies 6 (16):115-131.
    The approaching ways in religious phenomenon quantitative studies are, most of the times, based only on the evolution of adherent flows and population structure from a religious point of view. In this pape, an integrated statistical indicators system will be designed. The main purpose of the system is to enhance the quality and coherence of the religious phenomenon. The most important indicators from the integrated system are: context indicators (political, economical, socio-cultural, demographical), basic indicators, level and structure (...)
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  41. Statistical Thinking between Natural and Social Sciences and the Issue of the Unity of Science: from Quetelet to the Vienna Circle.Donata Romizi - 2012 - In Dennis Dieks, Wenceslao J. Gonzalez, Stephan Hartmann, Michael Stöltzner & Marcel Weber (eds.), Probabilities, Laws, and Structures. Springer.
    The application of statistical methods and models both in the natural and social sciences is nowadays a trivial fact which nobody would deny. Bold analogies even suggest the application of the same statistical models to fields as different as statistical mechanics and economics, among them the case of the young and controversial discipline of Econophysics . Less trivial, however, is the answer to the philosophical question, which has been raised ever since the possibility of “commuting” statistical (...)
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  42.  10
    Factor Structure of the “Top Ten” Positive Emotions of Barbara Fredrickson.Leopold Helmut Otto Roth & Anton-Rupert Laireiter - 2021 - Frontiers in Psychology 12:641804.
    In order to contribute to the consolidation in the field ofPositive Psychology, we reinvestigated the factor structure of top 10 positive emotions of Barbara Fredrickson. Former research in experimental settings resulted in a three-cluster solution, which we tested withexploratoryandconfirmatorymethodology against different factor models. Within our non-experimental data (N= 312), statistical evidence is presented, advocating for a single factor model of the 10 positive emotions. Different possible reasons for the deviating results are discussed, as well as the theoretical significance (...)
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  43.  10
    Statistical models of syntax learning and use.Mark Johnson & Stefan Riezler - 2002 - Cognitive Science 26 (3):239-253.
    This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the practical problems (...)
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  44. Organizational Structure and its Relation to the Prevailing Pattern of Communication in Palestinian Universities.Suliman A. El Talla, Mazen J. Al Shobaki, Samy S. Abu-Naser & Youssef M. Abu Amuna - 2018 - International Journal of Engineering and Information Systems (IJEAIS) 2 (5):22-43.
    The aim of the study was to identify the organizational structure and its relation to the prevailing pattern of communication in the Palestinian universities. The researchers used the analytical descriptive method through a questionnaire randomly distributed among Palestinian university workers in the Gaza Strip. The study was conducted on a sample of (274) administrative staff from the three universities, and the response rate was (81.87%). The study found that there is a high satisfaction with the nature of the organizational (...)
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  45.  4
    Prompting teaching modulates children's encoding of novel information by facilitating higher-level structure learning and hindering lower-level statistical learning.Hanna Marno, Róbert Danyi, Teodóra Vékony, Karolina Janacsek & Dezső Németh - 2021 - Cognition 213 (C):104784.
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  46.  4
    Quantum statistics, identical particles and correlations.Dennis Dieks - 1990 - Synthese 82 (1):127 - 155.
    It is argued that the symmetry and anti-symmetry of the wave functions of systems consisting of identical particles have nothing to do with the observational indistinguishability of these particles. Rather, a much stronger conceptual indistinguishability is at the bottom of the symmetry requirements. This can be used to argue further, in analogy to old arguments of De Broglie and Schrödinger, that the reality described by quantum mechanics has a wave-like rather than particle-like structure. The question of whether quantum statistics (...)
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  47.  2
    Unsupervised statistical learning in vision: computational principles, biological evidence.Shimon Edelman - unknown
    Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these (...)
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  48.  2
    Verisimilitude, cross classification and prediction logic. Approaching the statistical truth by falsified qualitative theories.Roberto Festa - 2007 - Mind and Society 6 (1):91-114.
    In this paper it is argued that qualitative theories (Q-theories) can be used to describe the statistical structure of cross classified populations and that the notion of verisimilitude provides an appropriate tool for measuring the statistical adequacy of Q-theories. First of all, a short outline of the post-Popperian approaches to verisimilitude and of the related verisimilitudinarian non-falsificationist methodologies (VNF-methodologies) is given. Secondly, the notion of Q-theory is explicated, and the qualitative verisimilitude of Q-theories is defined. Afterwards, appropriate (...)
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  49.  9
    Statistical Data and Mathematical Propositions.Cory Juhl - 2015 - Pacific Philosophical Quarterly 96 (1):100-115.
    Statistical tests of the primality of some numbers look similar to statistical tests of many nonmathematical, clearly empirical propositions. Yet interpretations of probability prima facie appear to preclude the possibility of statistical tests of mathematical propositions. For example, it is hard to understand how the statement that n is prime could have a frequentist probability other than 0 or 1. On the other hand, subjectivist approaches appear to be saddled with ‘coherence’ constraints on rational probabilities that require (...)
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  50.  1
    Statistics without probability: Significance testing as typicality and exchangeability in data analysis.John R. Vokey - 1998 - Behavioral and Brain Sciences 21 (2):225-226.
    Statistical significance is almost universally equated with the attribution to some population of nonchance influences as the source of structure in the data. But statistical significance can be divorced from both parameter estimation and probability as, instead, a statement about the atypicality or lack of exchangeability over some distinction of the data relative to some set. From this perspective, the criticisms of significance tests evaporate.
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