The Discriminatory Power of Lexical Context for Alternations: An Information-theoretic Exploration

Abstract. This article explores the possibility of differentiating between patients suffering from Alzheimer’s disease (AD) and patients with other kinds of dementia by focusing on false alarms (FAs) on a picture recognition task (PRT). In Study 1, we compared AD and non-AD patients on the PRT and found that FAs discriminate well between these groups. Study 2 served to improve the discriminatory power of the FA score on the picture recognition task by adding associated pairs. Here, too, the FA score differentiated well between AD and non-AD patients, though the discriminatory power did not improve. The findings suggest that AD patients show a liberal response bias. Taken together, these studies suggest that FAs in picture recognition are of major importance for the clinical diagnosis of AD.

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Supplemental Material for Information-Theoretic Latent Distribution Modeling: Distinguishing Discrete and Continuous Latent Variable Models

Psychological Methods ◽

10.1037/1082-989x.11.3.228.supp ◽

2006 ◽

Keyword(s):

Latent Variable ◽

Latent Variable Models ◽

Information Theoretic ◽

Distribution Modeling ◽

Latent Distribution

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ErbB receptor signalling in MCF7 breast cancer cells: an information theoretic approach

Endocrine Abstracts ◽

10.1530/endoabs.65.oc2.3 ◽

2019 ◽

Author(s):

Robert JP Pope ◽

Gavin I Welsh ◽

Richard J M Coward ◽

Craig A McArdle

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Breast Cancer Cells ◽

Theoretic Approach ◽

Erbb Receptor ◽

Information Theoretic ◽

Receptor Signalling ◽

Information Theoretic Approach

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Large-sample confidence intervals of information-theoretic measures in linguistics

Journal of Research Design and Statistics in Linguistics and Communication Science ◽

10.1558/jrds.40134 ◽

2020 ◽

Vol 6 (1) ◽

pp. 19-54

Author(s):

Ryan Ka Yau Lai ◽

Youngah Do

Keyword(s):

Maximum Likelihood ◽

Corpus Linguistics ◽

Delta Method ◽

Confidence Bounds ◽

Likelihood Estimator ◽

Information Theoretic ◽

Leibler Divergence ◽

Information Theoretic Measures ◽

Data Points ◽

Measure Of Uncertainty

This article explores a method of creating confidence bounds for information-theoretic measures in linguistics, such as entropy, Kullback-Leibler Divergence (KLD), and mutual information. We show that a useful measure of uncertainty can be derived from simple statistical principles, namely the asymptotic distribution of the maximum likelihood estimator (MLE) and the delta method. Three case studies from phonology and corpus linguistics are used to demonstrate how to apply it and examine its robustness against common violations of its assumptions in linguistics, such as insufficient sample size and non-independence of data points.

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