Efficiency investigation from shallow to deep neural network techniques in human activity recognition

In the last years, several researchers measured different recognition rates with different artificial neural network (ANN) techniques on public data sets in the human activity recognition (HAR) problem. However an overall investigation does not exist in the literature and the efficiency of complex and deeper ANNs over shallow networks is not clear. The purpose of this paper is to investigate the recognition rate and time requirement of different kinds of ANN approaches in HAR. This work examines the performance of shallow ANN architectures with different hyper-parameters, ANN ensembles, binary ANN classifier groups, and convolutional neural networks on two public databases. Although the popularity of binary classifiers, classifier ensembles and deep learning have been significantly increasing, this study shows that shallow ANNs with appropriate hyper-parameters in combination with extracted features can reach similar or higher recognition rate in less time than other artificial neural network methods in HAR. With a well-tuned ANN we outperformed all previous results on two public databases. Consequently, instead of the more complex ANN techniques, the usage of simple ANN with two or three layers can be an appropriate choice for activity recognition.     

Towards a literary account of mental health from James' Principles of Psychology

The field of mental health tends to treat its literary metaphors as literal realities with the concomitant loss of vague “feelings of tendency” in “unusual experiences”. I develop this argument through the prism of William James’ (1890) “The Principles of Psychology”. In the first part of the paper, I reflect upon the relevance of James' “The Psychologist's Fallacy” to a literary account of mental health. In the second part of the paper, I develop the argument that “connotations” and “feelings of tendency” are central to resolving some of the more difficult challenges of this fallacy. I proceed to do this in James' spirit of generating imaginative metaphors to understand experience. Curiously, however, mental health presents a strange paradox in William James’ (1890) Principles of Psychology. He constructs an elaborate conception of the “empirical self” and “stream of thought” but chooses not to use these to understand unusual experiences – largely relying instead on the concept of a “secondary self.” In this article, I attempt to make more use of James' central division between the “stream of thought” and the “empirical self” to understand unusual experiences. I suggest that they can be usefully understood using the loose metaphor of a “binary star” where the “secondary self” can be seen as an “accretion disk” around one of the stars. Understood as literary rather the literal, this metaphor is quite different to more unitary models of self-breakdown in mental health, particularly in its separation of “self” from “the stream of thought” and I suggest it has the potential to start a re-imagination of the academic discourse around mental health.     

Neuropsychological Contributions to the Early Identification of Alzheimer’s Disease

A wealth of evidence demonstrates that a prodromal period of Alzheimer’s disease (AD) exists for some years prior to the appearance of significant cognitive and functional declines required for the clinical diagnosis. This prodromal period of decline is characterized by a number of different neuropsychological and brain changes, and reliable identification of individuals prior to the development of significant clinical symptoms remains a top priority of research. In this review we provide an overview of those neuropsychological changes. In particular, we examine specific domains of cognition that appear to be negatively affected during the prodromal period of AD, and we review newer analytic strategies designed to examine cognitive asymmetries or discrepancies between higher-order cognitive functions versus fundamental skills. Finally, we provide a critical examination of the clinical concept of Mild Cognitive Impairment and offer suggestions for an increased focus on the impact of cerebrovascular disease (CVD) and CVD risk during the prodromal period of AD.     

What's Wrong with Creativity Testing?

Creativity testing as it is now done is often based on a defective assumption that different kinds of creativity can be compressed into a single unidimensional scale. There is no reason to believe that the different kinds of creativity represent, simply, different amounts of a single unidimensional construct. The article shows how three different ways of viewing creativity lead to different ways of measuring creativity, all at variance with current unidimensional models. The point of view presented here does not suggest that current creativity tests are invalid, but rather, that care must be taken in the nature of claims made for them. Moreover, many of the same arguments could be applied to the measurement of intelligence and even wisdom as well.     

The role of working memory in information acquisition and decision making: Lessons from the binary prediction task

The effects of memory constraints upon information acquisition and decision making were examined in two experiments using binary prediction tasks, where participants observe outcomes for two options before deciding which one to bet upon. Our studies extend previous investigations to the case where participants learn the structure of the task through observation, but where information acquisition is separated from the task of prediction. Participants with higher cognitive capacity (larger memory span or higher intelligence) were more likely to adopt the “maximizing” strategy (always selecting the more frequent alternative). This finding conflicts with some recent investigations of similar tasks, a contrast that implies that the presence of feedback on choices may be important in determining the strategic actions of high-capacity individuals. Participants selecting the optimal strategy were in turn more efficient in their data acquisition. The behaviour of participants adopting suboptimal choice strategies was consistent with prediction based upon a “narrow window of experience”—that is, seeking to match the characteristics of small samples of observations.     

Mediation analysis and categorical variables: The final frontier

Many scholars are interested in understanding the process by which an independent variable affects a dependent variable, perhaps in part directly and perhaps in part indirectly, occurring through the activation of a mediator. Researchers are facile at testing for mediation when all the variables are continuous, but a definitive answer had been lacking heretofore as to how to analyze the data when the mediator or dependent variable is categorical. This paper describes the problems that arise as well as the potential solutions. In the end, a solution is recommended that is both optimal in its statistical qualities as well as practical and easily implemented: compute z_Mediation.     

Mediation with categorical variables compleat

This rejoinder addresses the thoughtful issues raised by the commentators. We hold to the analytical solution of fitting X → M (to obtain a and sa) and X&;M→ (to obtain b and sb) via regression or logistic regression, depending on whether M and Y are continuous or categorical. Then researchers compute za, zb, and ZMediation.     

Motivated reasoning in the prediction of sports outcomes and the belief in the “hot hand”

The present paper explores the role of motivation to observe a certain outcome in people’s predictions, causal attributions, and beliefs about a streak of binary outcomes (basketball scoring shots). In two studies we found that positive streaks (points scored by the participants’ favourite team) lead participants to predict the streak’s continuation (belief in the hot hand), but negative streaks lead to predictions of its end (gambler’s fallacy). More importantly, these wishful predictions are supported by strategic attributions and beliefs about how and why a streak might unfold. Results suggest that the effect of motivation on predictions is mediated by a serial path via causal attributions to the teams at play and belief in the hot hand.     

Children can solve Bayesian problems: the role of representation in mental computation

Can children reason the Bayesian way? We argue that the answer to this question depends on how numbers are represented, because a representation can do part of the computation. We test, for the first time, whether Bayesian reasoning can be elicited in children by means of natural frequencies. We show that when information was presented to fourth, fifth, and sixth graders in terms of probabilities, their ability to estimate the Bayesian posterior probability was zero. Yet when the same information was presented in natural frequencies, Bayesian reasoning showed a steady increase from fourth to sixth grade, reaching an average level of 19, 39, and 53%, respectively, in two studies. Sixth graders' performance with natural frequencies matched the performance of adults with probabilities. But this general increase was accompanied by striking individual differences. More than half of the sixth graders solved most or all problems, whereas one third could not solve a single one. An analysis of the children's responses provides evidence for the use of three non-Bayesian strategies. These follow an overlapping wave model of development and continue to be observed in the minds of adults. More so than adults' probabilistic reasoning, children's reasoning depends on a proper representation of information.