Steven Phillips

National Institute of Advanced Industrial Science and Technology

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Daniel J. Gaspar

Pacific Northwest National Laboratory

Andrey Astrelin

BASIS International (United States)

Susanne B. Jones

Battelle

Massimiliano Ferrucci

Lawrence Livermore National Laboratory

Bala Muralikrishnan

National Institute of Standards and Technology

Daniel Sawyer

National Institute of Standards and Technology

William H. Wilson

University of North Dakota

John Palmateer

Boeing (United States)

Yuji Takeda

National Institute of Advanced Industrial Science and Technology

ПП

П. П. Петров

Crimean Astrophysical Observatory

Cooperative Institutions

Pacific Northwest National Laboratory

Battelle

National Renewable Energy Laboratory

Richland College

Government of the United States of America

National Technical Information Service

Office of Scientific and Technical Information

Washington State University

National Institute of Standards and Technology

Argonne National Laboratory

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Measuring and predicting the vapor pressure of gasoline containing oxygenates

Fuel (2019)

Daniel J. Gaspar Steven Phillips Evgueni Polikarpov Karl Albrecht Susanne B. Jones

Oxygenate

10.1016/j.fuel.2019.01.137

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Citations (49)

Evaluation of a laser scanner for large volume coordinate metrology: a comparison of results before and after factory calibration

Measurement Science and Technology (2014)

Massimiliano Ferrucci Bala Muralikrishnan Daniel Sawyer Steven Phillips П. П. Петров

Large volume laser scanners are increasingly being used for a variety of dimensional metrology applications. Methods to evaluate the performance of these scanners are still under development and there are currently no documentary standards available. This paper describes the results of extensive ranging and volumetric performance tests conducted on a large volume laser scanner. The results demonstrated small but clear systematic errors that are explained in the context of a geometric error model for the instrument. The instrument was subsequently returned to the manufacturer for factory calibration. The ranging and volumetric tests were performed again and the results are compared against those obtained prior to the factory calibration.

Factory (object-oriented programming)

Ranging

Laser Scanning

Dimensional Metrology

10.1088/0957-0233/25/10/105010

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Citations (14)

Second-Order Systematicity of Associative Learning: A Paradox for Classical Compositionality and a Coalgebraic Resolution

PLoS ONE (2016)

Steven Phillips William H. Wilson

Systematicity is a property of cognitive architecture whereby having certain cognitive capacities implies having certain other "structurally related" cognitive capacities. The predominant classical explanation for systematicity appeals to a notion of common syntactic/symbolic structure among the systematically related capacities. Although learning is a (second-order) cognitive capacity of central interest to cognitive science, a systematic ability to learn certain cognitive capacities, i.e., second-order systematicity, has been given almost no attention in the literature. In this paper, we introduce learned associations as an instance of second-order systematicity that poses a paradox for classical theory, because this form of systematicity involves the kinds of associative constructions that were explicitly rejected by the classical explanation. Our category theoretic explanation of systematicity resolves this problem, because both first and second-order forms of systematicity are derived from the same categorical construction: universal morphisms, which generalize the notion of compositionality of constituent representations to (categorical) compositionality of constituent processes. We derive a model of systematic associative learning based on (co)recursion, which is an instance of a universal construction. These results provide further support for a category theory foundation for cognitive architecture.

Principle of compositionality

Associative property

Category theory

Morphism

Categorical variable

Associative learning

10.1371/journal.pone.0160619

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Citations (8)

Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals

PLoS ONE (2012)

Steven Phillips Yuji Takeda Archana Singh

The capacity to integrate multiple sources of information is a prerequisite for complex cognitive ability, such as finding a target uniquely identifiable by the conjunction of two or more features. Recent studies identified greater frontal-parietal synchrony during conjunctive than non-conjunctive (feature) search. Whether this difference also reflects greater information integration, rather than just differences in cognitive strategy (e.g., top-down versus bottom-up control of attention), or task difficulty is uncertain. Here, we examine the first possibility by parametrically varying the number of integrated sources from one to three and measuring phase-locking values (PLV) of frontal-parietal EEG electrode signals, as indicators of synchrony. Linear regressions, under hierarchical false-discovery rate control, indicated significant positive slopes for number of sources on PLV in the 30–38 Hz, 175–250 ms post-stimulus frequency-time band for pairs in the sagittal plane (i.e., F3-P3, Fz-Pz, F4-P4), after equating conditions for behavioural performance (to exclude effects due to task difficulty). No such effects were observed for pairs in the transverse plane (i.e., F3-F4, C3-C4, P3-P4). These results provide support for the idea that anterior-posterior phase-locking in the lower gamma-band mediates integration of visual information. They also provide a potential window into cognitive development, seen as developing the capacity to integrate more sources of information.

Stimulus (psychology)

Visual Search

Information integration

Feature (linguistics)

10.1371/journal.pone.0032502

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Citations (22)

Volumetric performance evaluation of a laser scanner based on geometric error model

Precision Engineering (2014)

Bala Muralikrishnan Massimiliano Ferrucci Daniel Sawyer Grant Gerner V. Lee

Laser Scanning

Identification

Geometric Modeling

Prism

10.1016/j.precisioneng.2014.11.002

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Citations (50)

Statistical Detection of EEG Synchrony Using Empirical Bayesian Inference

PLoS ONE (2015)

Archana Singh Hideki Asoh Yuji Takeda Steven Phillips

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

False Discovery Rate

Multiple comparisons problem

Statistical power

Statistical Inference

10.1371/journal.pone.0121795

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Citations (2)

Analogy, Cognitive Architecture and Universal Construction: A Tale of Two Systematicities

PLoS ONE (2014)

Steven Phillips

Cognitive science recognizes two kinds of systematicity: (1) as the property where certain cognitive capacities imply certain other related cognitive capacities (Fodor and Pylyshyn); and (2) as the principle that analogical mappings based on collections of connected relations are preferred over relations in isolation (Gentner). Whether these kinds of systematicity are two aspects of a deeper property of cognition is hitherto unknown. Here, it is shown that both derive from the formal, category-theoretic notion of universal construction. In conceptual/psychological terms, a universal construction is a form of optimization of cognitive resources: optimizing the re-utilization of common component processes for common task components. Systematic cognitive capacity and the capacity for analogy are hallmarks of human cognition, which suggests that universal constructions (in the category-theoretic sense) are a crucial component of human cognitive architecture.

Component (thermodynamics)

Isolation

10.1371/journal.pone.0089152

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Citations (11)

Correlation between the surface temperature and thickness of Arctic sea ice

AGU Fall Meeting Abstracts (2012)

A. H. Frazier Antonino Vaccaro Steven Phillips D. Blake R. B. Herman

Source

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Citations (0)

Electrifying the production of sustainable aviation fuel: the risks, economics, and environmental benefits of emerging pathways including CO₂