Discrimination of mental tasks based on EEMD and information theoretic pattern selection

2015 ◽  
Author(s):  
Somayeh Noshadi ◽  
Abbas Ebrahimi Moghadam ◽  
Morteza Khademi
Keyword(s):  
Pattern Selection ◽  
Information Theoretic ◽  
Mental Tasks

Pleasure as a Sign of Efficacy of Mental Activity

1997 ◽  
Vol 2 (3) ◽  
pp. 226-234 ◽  
Author(s):  
Michel Cabanac ◽  
Chantal Pouliot ◽  
James Everett
Keyword(s):  
Evolutionary Psychology ◽  
Magnitude Estimation ◽  
Mental Activity ◽  
Point Of View ◽  
Macintosh Computer ◽  
Science Students ◽  
Cognitive Efficiency ◽  
Sensory Pleasure ◽  
The Difference ◽  
Mental Tasks

Previous work has shown that sensory pleasure is both the motor and the sign of optimal behaviors aimed at physiological ends. From an evolutionary psychology point of view it may be postulated that mental pleasure evolved from sensory pleasure. Accordingly, the present work tested empirically the hypothesis that pleasure signals efficacious mental activity. In Experiment 1, ten subjects played video-golf on a Macintosh computer. After each hole they were invited to rate their pleasure or displeasure on a magnitude estimation scale. Their ratings of pleasure correlated negatively with the difference par minus performance, i.e., the better the performance the greater the pleasure reported. In Experiments 2 and 3, the pleasure of reading poems was correlated with comprehension, both rated by two groups of subjects, science students and arts students. In the majority of science students pleasure was significantly correlated with comprehension. Only one arts student showed this relationship; this result suggests that the proposed relationship between pleasure and cognitive efficiency is not tautological. Globally, the results support the hypothesis that pleasure is aroused by the same mechanisms, and follows the same laws, in physiological and cognitive mental tasks and also leads to the optimization of performance.

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

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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