scholarly journals Generalization and source memory in acquired equivalence

2020 ◽  
Author(s):  
Maria Alexandra de Araujo Sanchez ◽  
Dagmar Zeithamova
Keyword(s):  
Knowledge Representation ◽  
Source Memory ◽  
Memory Test ◽  
Data Sets ◽  
Acquired Equivalence ◽  
On Demand ◽  
New Information ◽  
Combine Information ◽  
High Degree

Memory allows us to remember specific events but also combine information across events to infer new information. New inferences are thought to stem from integrating memories of related events during encoding but can be also generated on-demand, based on separate memories of individual events. Integrative encoding has been argued as dominant in the acquired equivalence paradigm, where people have a tendency to assume that when two faces share one preference, they also share another. A downside may be a loss of source memory, where inferred preferences are mistaken for observed ones. Here, we tested the predictions of integrative encoding across five data sets collected using small variations of the acquired equivalence paradigm. Results showed a statistically reliable but numerically small tendency to generalize preferences across faces, with stronger evidence for on-demand inferences at retrieval rather than spontaneous integration during encoding. Newly included explicit source memory test showed that participants differentiated learned from inferred preferences to a high degree, irrespective of whether they generalized preferences across faces. Overall, the results indicate that representations of individual events and retrieval-based processes may play a larger role in acquired equivalence than previously thought, informing current theories of generalization and knowledge representation.

Positive Arousal Enhances the Consolidation of Item Memory

2015 ◽  
Vol 74 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Bo Wang
Keyword(s):  
Episodic Memory ◽  
Memory Consolidation ◽  
Source Memory ◽  
Emotional Arousal ◽  
Theoretical Models ◽  
Memory Test ◽  
Learning Phase ◽  
Item Memory ◽  
Memory Tests ◽  
Immediate Memory

Emotional arousal induced after learning has been shown to modulate memory consolidation. However, it is unclear whether the effect of postlearning arousal can extend to different aspects of memory. This study examined the effect of postlearning positive arousal on both item memory and source memory. Participants learned a list of neutral words and took an immediate memory test. Then they watched a positive or a neutral videoclip and took delayed memory tests after either 25 minutes or 1 week had elapsed after the learning phase. In both delay conditions, positive arousal enhanced consolidation of item memory as measured by overall recognition. Furthermore, positive arousal enhanced consolidation of familiarity but not recollection. However, positive arousal appeared to have no effect on consolidation of source memory. These findings have implications for building theoretical models of the effect of emotional arousal on consolidation of episodic memory and for applying postlearning emotional arousal as a technique of memory intervention.

scholarly journals Unbiased least-squares modification of Stokes’ formula

2020 ◽  
Vol 94 (9) ◽  
Author(s):  
Lars E. Sjöberg
Keyword(s):  
Gravity Data ◽  
Harmonic Series ◽  
Local Error ◽  
Data Sets ◽  
Spherical Cap ◽  
Local Variance ◽  
Error Covariance ◽  
Stokes Formula ◽  
Gravity Signal ◽  
High Degree

Abstract As the KTH method for geoid determination by combining Stokes integration of gravity data in a spherical cap around the computation point and a series of spherical harmonics suffers from a bias due to truncation of the data sets, this method is based on minimizing the global mean square error (MSE) of the estimator. However, if the harmonic series is increased to a sufficiently high degree, the truncation error can be considered as negligible, and the optimization based on the local variance of the geoid estimator makes fair sense. Such unbiased types of estimators, derived in this article, have the advantage to the MSE solutions not to rely on the imperfectly known gravity signal degree variances, but only the local error covariance matrices of the observables come to play. Obviously, the geoid solution defined by the local least variance is generally superior to the solution based on the global MSE. It is also shown, at least theoretically, that the unbiased geoid solutions based on the KTH method and remove–compute–restore technique with modification of Stokes formula are the same.

Belief Movement, Uncertainty Reduction, and Rational Updating*

2021 ◽  
Author(s):  
Ned Augenblick ◽  
Matthew Rabin
Keyword(s):  
Statistical Tests ◽  
Uncertainty Reduction ◽  
The State ◽  
Data Sets ◽  
New Information ◽  
The World ◽  
Core Concepts ◽  
Market Beliefs ◽  
Over Time

Abstract When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

scholarly journals T-SNE visualization of large-scale neural recordings

2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Data Sets ◽  
Neural Recordings ◽  
Sorting Problem ◽  
Feature Spaces ◽  
High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering

2021 ◽  
Author(s):  
Benjamin Moreno-Torres ◽  
Christoph Völker ◽  
Sabine Kruschwitz
Keyword(s):  
Knowledge Representation ◽  
Civil Engineering ◽  
Common Knowledge ◽  
Measurement Data ◽  
Data Access ◽  
Semantic Knowledge ◽  
Data Sources ◽  
Data Sets ◽  
Extensive Literature ◽  
Distributed Data

<div> <p>Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.</p> <p>To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.</p> <p>A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.</p> </div>

MAYA COASTAL PRODUCTION, EXCHANGE, LIFE STYLE, AND POPULATION MOBILITY: A VIEW FROM THE PORT OF XCAMBO, YUCATAN, MEXICO

2014 ◽  
Vol 25 (1) ◽  
pp. 221-238 ◽  
Author(s):  
Thelma Sierra Sosa ◽  
Andrea Cucina ◽  
T. Douglas Price ◽  
James H. Burton ◽  
Vera Tiesler
Keyword(s):  
Geographic Mobility ◽  
New Order ◽  
Data Sets ◽  
Population Mobility ◽  
Chemical Research ◽  
Economic Production ◽  
New Information ◽  
Trade Dynamics ◽  
Maya Collapse ◽  
Coastal Marshlands

AbstractAnchored in archaeological, bioarchaeological, and chemical research conducted at the coastal enclave of Xcambo, this paper examines Classic period Maya coastal saline economic production and exchange, along with the lifestyle, ethnicity, and mobility of the traders. Nestled in the coastal marshlands of the northern Yucatan, Mexico, Xcambo functioned as a salt production center and port during its occupation, maintaining long-reaching ties with other parts of the Maya world and Veracruz. Considered together, the different data sets document a reorientation in Xcambo's exchange routes and connections, which are echoed by increasingly diverse cultural affiliations and an increasing geographic mobility of Xcambo's merchants. This new information confirms the known pattern of gradually intensifying, though still relatively independent, trade dynamics along the Maya coast in the centuries leading up to the so-called “Maya collapse” and the rise of a new merchant league under the control of Chichen Itza. It was this new order that probably led to the swift end of Xcambo soon aftera.d.700.

scholarly journals Biodiversity of Active and Inactive Bacteria in the Gut Flora of Wood-Feeding Huhu Beetle Larvae (Prionoplus reticularis)

2011 ◽  
Vol 77 (19) ◽  
pp. 7000-7006 ◽  
Author(s):  
Nicola M. Reid ◽  
Sarah L. Addison ◽  
Lucy J. Macdonald ◽  
Gareth Lloyd-Jones
Keyword(s):  
Genomic Dna ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Dna And Rna ◽  
New Information ◽  
Related Organism ◽  
The Family ◽  
Insect Symbionts ◽  
Derived Data

ABSTRACTHuhu grubs (Prionoplus reticularis) are wood-feeding beetle larvae endemic to New Zealand and belonging to the family Cerambycidae. Compared to the wood-feeding lower termites, very little is known about the diversity and activity of microorganisms associated with xylophagous cerambycid larvae. To address this, we used pyrosequencing to evaluate the diversity of metabolically active and inactive bacteria in the huhu larval gut. Our estimate, that the gut harbors at least 1,800 phylotypes, is based on 33,420 sequences amplified from genomic DNA and reverse-transcribed RNA. Analysis of genomic DNA- and RNA-derived data sets revealed that 71% of all phylotypes (representing 95% of all sequences) were metabolically active. Rare phylotypes contributed considerably to the richness of the community and were also largely metabolically active, indicating their participation in digestive processes in the gut. The dominant families in the active community (RNA data set) includedAcidobacteriaceae(24.3%),Xanthomonadaceae(16.7%),Acetobacteraceae(15.8%),Burkholderiaceae(8.7%), andEnterobacteriaceae(4.1%). The most abundant phylotype comprised 14% of the active community and affiliated withDyella ginsengisoli(Gammaproteobacteria), suggesting that aDyella-related organism is a likely symbiont. This study provides new information on the diversity and activity of gut-associated microorganisms that are essential for the digestion of the nutritionally poor diet consumed by wood-feeding larvae. Many huhu gut phylotypes affiliated with insect symbionts or with bacteria present in acidic environments or associated with fungi.

Iceberg/Seabed Interaction Events Observed During the DIGS Experiment

1991 ◽  
Vol 113 (1) ◽  
pp. 74-87 ◽  
Author(s):  
J. H. Lever ◽  
D. W. Bass ◽  
C. F. M. Lewis ◽  
K. Klein ◽  
D. Diemand ◽  
...  
Keyword(s):  
Full Scale ◽  
Labrador Sea ◽  
Data Sets ◽  
Side Scan Sonar ◽  
Time Stepping ◽  
Dynamic Events ◽  
New Information ◽  
Stability Maps ◽  
Seabed Interaction ◽  
Scale Data

The Dynamics of Iceberg Grounding and Scouring (DIGS) experiment was conducted in the Labrador Sea during August 1985. The objectives of the experiment were to obtain full-scale data sets documenting iceberg/seabed interactions, and to obtain by direct observation new information regarding the processes of iceberg scour formation and degradation. Utilizing a vessel and a helicopter, measurements were made of icebergs’ above and below-water shapes, plus local winds, waves, currents and tides. Special self-contained motion monitoring packages were deployed by helicopter on icebergs thought to be good grounding candidates. Seabed observations were made directly using the submersible Pisces IV, and extensive side-scan sonar data were collected. This paper describes two dynamic iceberg/seabed interaction events documented during DIGS: the roll/pitting behavior of the 1.2-million-ton domed iceberg “Bertha,” and the split/grounding behavior of the 7.7-million-ton tabular iceberg “Gladys.” This latter event is particularly interesting due to its very energetic nature, and the fact that it represents the only full-scale observation of any iceberg impact with sufficient documentation to yield estimates of the interaction forces. Subsequent to the experiment, the recorded above and below-water shapes were used to obtain hydrostatic stability maps for these icebergs. A time stepping procedure was also developed to re-create these two dynamic events, and comparisons between the observed and simulated motions are provided in this paper.

How to Borrow Information From Unlinked Data? A Relative Density Approach for Predicting Unobserved Distributions

2020 ◽  
pp. 004912412092621
Author(s):  
Siwei Cheng
Keyword(s):  
Relative Density ◽  
Wage Inequality ◽  
Methodological Approach ◽  
Data Sets ◽  
Social Scientists ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
The Difference ◽  
Combine Information ◽  
Base Data

One of the most important developments in the current era of social sciences is the growing availability and diversity of data, big and small. Social scientists increasingly combine information from multiple data sets in their research. While conducting statistical analyses with linked data is relatively straightforward, borrowing information across unlinked data can be much more challenging due to the absence of unit-to-unit linkages. This article proposes a new methodological approach for borrowing information across unlinked surveys to predict unobserved distributions. The gist of the proposed approach lies in the idea of using the relative density between the observed and unobserved distributions in the reference data to characterize the difference between the two distributions and borrow that information to the base data. Relying on the assumption that the relative density between the observed and unobserved distributions is similar between data sets, the proposed relative density approach has the key advantage of allowing the researcher to borrow information about the shape of the distribution, rather than a few summary statistics. The approach also comes with a method for incorporating and quantifying the uncertainty in its output. We illustrate the formulation of this approach, demonstrate with simulation examples, and finally apply it to address the problem of employment selection in wage inequality research.

GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING

2009 ◽  
Vol 23 (06) ◽  
pp. 1053-1081 ◽  
Author(s):  
KASPAR RIESEN ◽  
HORST BUNKE
Keyword(s):  
Data Sets ◽  
Object Representations ◽  
Graph Classification ◽  
Graph Edit Distance ◽  
Graph Kernels ◽  
Symbolic Data ◽  
Representational Power ◽  
High Degree ◽  
Vectorial Data ◽  
Representation Formalism

Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propose an approach transforming graphs into n-dimensional real vectors by means of prototype selection and graph edit distance computation. This approach allows one to build graph kernels in a straightforward way. It is not only applicable to graphs, but also to other kind of symbolic data in conjunction with any kind of dissimilarity measure. Thus it is characterized by a high degree of flexibility. With several experimental results, we prove the robustness and flexibility of our new method and show that our approach outperforms other graph classification methods on several graph data sets of diverse nature.

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