Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

Squirrel Monkeys ◽

Perceptual Similarity ◽

Specific Data ◽

Response Variable ◽

Ravignani et al. (2013) habituated squirrel monkeys to sound sequences conforming to an ABnA grammar (n = 1, 2, 3), then tested them for their reactions to novel grammatical and non-grammatical sequences. Although they conclude that the monkeys “consistently recognized and generalized the sequence ABnA,” I remark that this conclusion is not robust. The statistical significance of results depends on specific choices of data analysis, namely dichotomization of the response variable and omission of specific data points. Additionally, there is little evidence of generalization to novel patterns (n = 4, 5), which is important to conclude that the monkeys recognized the ABnA grammar beyond the habituation patterns. Lastly, many test sequences were perceptually similar to habituation sequences, raising the possibility that the monkeys may have generalized based on perceptual similarity rather than based on grammaticality.

Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

10.7287/peerj.preprints.2963v1 ◽

2017 ◽

Author(s):

Stefano Ghirlanda

Keyword(s):

Data Analysis ◽

Discrimination Task ◽

Squirrel Monkeys ◽

Response Variable ◽

Auditory Sequence ◽

Ravignani et al. (2013) habituated squirrel monkeys to sound sequences conforming to an ABnB grammar, then tested them for the ability to identify novel grammatical sequences as well as non-grammatical ones. Although they conclude that the monkeys "consistently recognized and generalized the sequence ABnA," the data indicate very poor generalization. Pattern grammaticality accounted for at most 6% of the variance in responding. In addition, the statistical significance of results depends on specific choices of data analysis (dichotomization of the response variable and omission of certain data points) which appear to have a weak rationale. I also suggest that the task used by Ravignani et al. (2013) may be fruitfully analyzed as an auditory sequence discrimination task that does not require specific proto-linguistic abilities.

Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

10.7287/peerj.preprints.2963 ◽

2017 ◽

Author(s):

Stefano Ghirlanda

Keyword(s):

Data Analysis ◽

Discrimination Task ◽

Squirrel Monkeys ◽

Response Variable ◽

Auditory Sequence ◽

Bayesian modeling and significant features exploration in wavelet power spectra

Nonlinear Processes in Geophysics ◽

10.5194/npg-14-79-2007 ◽

2007 ◽

Vol 14 (1) ◽

pp. 79-88 ◽

Cited By ~ 9

Author(s):

D. V. Divine ◽

F. Godtliebsen

Keyword(s):

Data Analysis ◽

Traditional Approach ◽

Power Spectra ◽

Simulated Data ◽

Scale Space ◽

Independent Random Variables ◽

Discrete Wavelet ◽

Wavelet Coefficients ◽

Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

XRD Data Visualization, Processing and Analysis with d1Dplot and d2Dplot Software Packages

Proceedings ◽

10.3390/proceedings2020062009 ◽

2020 ◽

Vol 62 (1) ◽

pp. 9

Author(s):

Oriol Vallcorba ◽

Jordi Rius

Keyword(s):

Data Analysis ◽

Data Visualization ◽

X Ray Diffraction ◽

Specific Data ◽

X Ray ◽

Software Packages ◽

Compound Database ◽

Synchrotron Light ◽

User Friendly ◽

Transformation Processes

The d1Dplot and d2Dplot computer programs have been developed as user-friendly tools for the inspection and processing of 1D and 2D X-ray diffraction (XRD) data, respectively. d1Dplot provides general tools for data processing and includes the ability to generate comprehensive 2D plots of multiple patterns to easily follow transformation processes. d2Dplot is a full package for 2D XRD data. Besides general processing tools, it includes specific data analysis routines for the application of the through-the-substrate methodology [Rius et al. IUCrJ 2015, 2, 452–463]. Both programs allow the creation of a user compound database for the identification of crystalline phases. The software can be downloaded from the ALBA Synchrotron Light Source website and can be used free of charge for non-commercial and academic purposes.

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

American Journal of Evaluation ◽

10.1177/1098214018804195 ◽

2018 ◽

Vol 41 (1) ◽

pp. 125-144 ◽

Cited By ~ 2

Author(s):

Rebecca Campbell ◽

Rachael Goodman-Williams ◽

Hannah Feeney ◽

Giannina Fehler-Cabral

Keyword(s):

Large Scale ◽

Contextual Information ◽

Policy Makers ◽

Specific Data ◽

Qualitative And Quantitative ◽

Stakeholder Groups ◽

Data Points ◽

Evaluation Project ◽

Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

Improving Data Analysis in Political Science

World Politics ◽

10.2307/2009670 ◽

1969 ◽

Vol 21 (4) ◽

pp. 641-654 ◽

Cited By ~ 28

Author(s):

Edward R. Tufte

Keyword(s):

Data Analysis ◽

Measurement Error ◽

Political Science ◽

Large Body ◽

Small Collection

Students of politics use statistical and quantitative techniques to: summarize a large body of numbers into a small collection of typical values;confirm (and perhaps sanctify) the results of the analysis by using tests of statistical significance that help protect against sampling and measurement error;discover what's going on in their data and expose some new relationships; andinform their audience what's going on in the data.

Impact of the COVID-19 Outbreak on Emergency Resources – An Observational Multicenter Study Including All Hospitals in a Major Urban Center of the Rhein Ruhr Metropolitan Region

10.21203/rs.3.rs-97392/v1 ◽

2020 ◽

Author(s):

Sebastian Bergrath ◽

Tobias Strapatsas ◽

Michael Tuemen ◽

Thorsten Reith ◽

Marc Deussen ◽

...

Keyword(s):

Data Analysis ◽

Multicenter Study ◽

Social Life ◽

Hospital Admissions ◽

Urban Center ◽

Medical Specialties ◽

Work Related ◽

Ed Visits ◽

The Impact

Abstract Background: The outbreak of the coronavirus disease 2019 (COVID-19) caused by the severe respiratory distress syndrome coronavirus 2 (SARS-CoV-2) led to severe disruption in social life and economics. The present study should analyze the impact of the local COVID-19 epidemic on emergency resources for all hospitals in a major urban center (Moenchengladbach, Germany). Methods: An observational multicenter study was performed involving all four acute care hospitals. Systemic parameters department (ED) parameters from week 4 to 24 in 2020 were compared to the corresponding period in 2019 for each hospital and in a summative data analysis using a logistic regression model. Outcomes: ED visits, ED to hospital admission, ED to Intensive Care Unit (ICU) admission, medical specialties of admitted patients, work related accidents. Results: In week 9/2020 the first SARS-CoV-2 positive patients were detected in our region. All hospitals decided to minimize elective admissions to ensure operational capability for COVID-19 patients. The summative number of ED visits dropped from 34,659 to 28,008. Numbers decreased from week 8 on between 38% and 48% per week per hospital at the maximum and began to rise again from week 16 on. The pooled data analysis showed statistically significant decreases in outpatient ED visits (20,152 vs. 16,477, p=<0.001), hospital admissions of ED patients (14,507 vs. 11,531, p=<0.001), and work-related accidents (2,290 vs. 1,468, p=<0.001). The decrease in admissions from ED to ICU did not reach statistical significance (2,093 vs. 1,566, p=0.255). The decline in ED cases was mainly caused by a decrease in non-trauma and non-surgical patients. Conclusion: The regional COVID-19 outbreak led to significantly reduced ED contacts after the first COVID-19 cases appeared. Even the admissions to the hospitals and the number of ED to ICU-admissions decreased, which is potentially dangerous, because the ratio of emergency outpatients vs. inpatients remained stable. Therefore, one can assume that patients with severe medical problems did not seek ED care in many cases. The decline of patients was earlier than in other German hospitals and in contrast to the findings in the U.S. and Italy where ED visits and hospital admissions in medical disciplines increased.

Value Investment Based on Data Analysis with Comparison of Three Insurance Corporations in the US

E3S Web of Conferences ◽

10.1051/e3sconf/202021401001 ◽

2020 ◽

Vol 214 ◽

pp. 01001

Author(s):

Jinnan Sun

Keyword(s):

Data Analysis ◽

Crucial Role ◽

Insurance Industry ◽

Investment Analysis ◽

Insurance Companies ◽

Specific Data ◽

The Us ◽

Investment Advice

Value investment analysis plays a crucial role in people’s judgment of whether an enterprise is worthy of continuing investment. Because it helps people reduce the likelihood of making a bad investment, whether it’s worth it, and how do you combine the various factors. The purpose of this paper is to analyze the value of the company’s investment in the insurance industry. AIG, ALL and MET were selected from a large number of insurance companies. Using P/E and P/S ratio to compare the prospects of several companies of the same type through specific data, investment analysis. Finally, the best companies to invest in among the three companies are obtained by combining the display situation, and give final investment advice.

Industrial Internet of Things and Cyber-Physical Systems - Advances in Computer and Electrical Engineering ◽

Analyzing the Performance and Efficiency of IT-Compliant Audit Module Using Clustering Methods

10.4018/978-1-7998-2803-7.ch018 ◽

2020 ◽

pp. 351-376 ◽

Cited By ~ 1

Author(s):

Soobia Saeed ◽

N. Z. Jhanjhi ◽

Mehmood Naqvi ◽

Mamoona Humayun ◽

Vasaki Ponnusamy

Keyword(s):

Data Analysis ◽

Service Quality ◽

Human Beings ◽

Clustering Methods ◽

Minimum Period ◽

The Impact

Human beings have a knack for errors. Counter-effective actions rendered to specify and rectify such errors in a minimum period of time are required when effectiveness and swift advancement depends on the capability of acknowledging the faults and errors and repair quickly. The software as audit module application in IT complaint is in review in this commentary as is another significant instrument created in the field of data analysis that digs deep into quickly and successfully assessing the imprecisions or grievances identified by the users in a certain company. The target of this study is to evaluate the statistical significance in relationship between client reporting attitude and client reliability and to evaluate the impact of strong responsiveness on client reliability, to measure the statistically noteworthy effect of client grievance conduct on service quality, and to test the impact of service quality on client dedication.

Anomaly Detection for Inferring Social Structure

Social Computing ◽

10.4018/978-1-60566-984-7.ch118 ◽

2010 ◽

pp. 1797-1803

Author(s):

Lisa Friedland

Keyword(s):

Data Analysis ◽

Anomaly Detection ◽

Social Structure ◽

Small Groups ◽

Analysis Data ◽

Data Sets ◽

Complex Data ◽

Detection Approach ◽

Complex Data Sets ◽

In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate multiple types of entities and links; and temporal information describes the dynamics of these systems. With such semantically complex data sets, a greater variety of patterns can be described and views constructed of the data. This article describes a specific social structure that may be present in such data sources and presents a framework for detecting it. The goal is to identify tribes, or small groups of individuals that intentionally coordinate their behavior—individuals with enough in common that they are unlikely to be acting independently. While this task can only be conceived of in a domain of interacting entities, the solution techniques return to the traditional data analysis questions. In order to find hidden structure (3), we use an anomaly detection approach: develop a model to describe the data (1), then identify outliers (2).