The TEACCH Program for People with Autism: Elements, Outcomes, and Comparison with Competing Models

<p>The PCL (Weathers et al., 1993) is a useful and widely used measure to assess PTSD symptoms in clinical and research contexts, exhibiting adequate psychometric properties across its several versions and translations (e. g. Carvalho et al., 2015; Wilkins et al., 2011). The current study analyzed the psychometric properties (latent structure, internal consistency, temporal reliability, and convergent validity) of the Portuguese version of the PCL for the DSM-5 (PCL-5, Weathers et al., 2013) in a sample of firefighters. This study also aimed to contribute with empirical data to clarify the best latent structure of DSM-5 PTSD symptoms. Specifically, the DSM-5 four-factor model and other competing models for PTSD symptoms (four-factor Dysphoria model, five-factor Dysphoric Arousal model, six-factor Anhedonia model, six-factor Externalizing Behavior model, and seven-factor Hybrid model) applied to PCL-5 were analyzed and compared in this paper.<br></p>

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The nature of metacognitive inefficiency in perceptual decision making

10.31234/osf.io/g9qzh ◽

2020 ◽

Author(s):

Medha Shekhar ◽

Dobromir Rahnev

Keyword(s):

Decision Making ◽

Mechanistic Model ◽

Perceptual Decision Making ◽

Large Dataset ◽

Substantial Body ◽

Confidence Levels ◽

Continuous Scale ◽

Competing Models ◽

Metacognitive Ability ◽

Confidence Ratings

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.

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Validating Game-Theoretic Models of Terrorism: Insights from Machine Learning

Games ◽

10.3390/g12030054 ◽

2021 ◽

Vol 12 (3) ◽

pp. 54

Author(s):

James T. Bang ◽

Atin Basuchoudhary ◽

Aniruddha Mitra

Keyword(s):

Machine Learning ◽

Terror Attacks ◽

Nonlinear Relationships ◽

Game Theoretic ◽

Competing Models

There are many competing game-theoretic analyses of terrorism. Most of these models suggest nonlinear relationships between terror attacks and some variable of interest. However, to date, there have been very few attempts to empirically sift between competing models of terrorism or identify nonlinear patterns. We suggest that machine learning can be an effective way of undertaking both. This feature can help build more salient game-theoretic models to help us understand and prevent terrorism.

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The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland

Symmetry ◽

10.3390/sym13071156 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1156

Author(s):

Mohamed Yusuf Hassan

Keyword(s):

Time Series ◽

Deep Learning ◽

Respiratory Infection ◽

Acute Respiratory Infection ◽

Short Term Memory ◽

Mean Deviation ◽

Sarima Model ◽

Under Five ◽

Competing Models ◽

Long Short Term Memory

The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.

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Queering the New Woman: Ideals of Modern Femininity in The Ladies’ Journal, 1915–1931

NAN Nü ◽

10.1163/15685268-00162p05 ◽

2014 ◽

Vol 16 (2) ◽

pp. 341-362

Author(s):

Joshua A. Hubbard

Keyword(s):

New Woman ◽

Chinese Woman ◽

Sexual Morality ◽

Sexual Liberation ◽

Social Trends ◽

The New Woman ◽

Competing Models ◽

Political Conditions ◽

Over Time

This case study of Republican China’s most widely read women’s periodical, The Ladies’ Journal (Funü zazhi), argues that the New Woman remained a highly contested ideal throughout the journal’s publication from 1915 to 1931. Editors and contributors endorsed competing models of modern femininity that shifted over time, shaped by volatile political conditions and social trends. With a focus on sexual morality, this article subjects normative visions of the modern Chinese woman, as depicted in The Ladies’ Journal, to a queer reading. By exploring the tension between widely circulated heteronormative discourses and their inherent slippages that revealed and fostered subversion, this article demonstrates that, rather than advocating for a clearly defined and radically new icon of sexual liberation, The Ladies’ Journal presented a vision of the New Woman that was capricious, contested, and in some ways conservative. 

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AutoSense Model for Word Sense Induction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016212 ◽

2019 ◽

Vol 33 ◽

pp. 6212-6219 ◽

Cited By ~ 1

Author(s):

Reinald Kim Amplayo ◽

Seung-won Hwang ◽

Min Song

Keyword(s):

Latent Variable ◽

Word Sense ◽

Name Disambiguation ◽

Variable Model ◽

Fine Grained ◽

Word Sense Induction ◽

Author Name Disambiguation ◽

Competing Models ◽

Word Senses ◽

Better Than

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.

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Methods for Studying Morphological Integration and Modularity

The Paleontological Society Papers ◽

10.1017/s1089332600001881 ◽

2010 ◽

Vol 16 ◽

pp. 213-243 ◽

Cited By ~ 32

Author(s):

Anjali Goswami ◽

P. David Polly

Keyword(s):

Distance Matrix ◽

Japanese Macaques ◽

Morphological Integration ◽

Matrix Analysis ◽

Euclidean Distance Matrix ◽

Phenotypic Data ◽

Graphical Modelling ◽

Landmark Data ◽

Competing Models ◽

Matrix Correlation

Morphological integration and modularity are closely related concepts about how different traits of an organism are correlated. Integration is the overall pattern of intercorrelation; modularity is the partitioning of integration into evolutionarily or developmentally independent blocks of traits. Modularity and integration are usually studied using quantitative phenotypic data, which can be obtained either from extant or fossil organisms. Many methods are now available to study integration and modularity, all of which involve the analysis of patterns found in trait correlation or covariance matrices. We review matrix correlation, random skewers, fluctuating asymmetry, cluster analysis, Euclidean distance matrix analysis (EDMA), graphical modelling, two-block partial least squares, RV coefficients, and theoretical matrix modelling and discuss their similarities and differences. We also review different coefficients that are used to measure correlations. We apply all the methods to cranial landmark data from and ontogenetic series of Japanese macaques,Macaca fuscatato illustrate the methods and their individual strengths and weaknesses. We conclude that the exploratory approaches (cluster analyses of various sorts) were less informative and less consistent with one another than were the results of model testing or comparative approaches. Nevertheless, we found that competing models of modularity and integration are often similar enough that they are not statistically distinguishable; we expect, therefore, that several models will often be significantly correlated with observed data.

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