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Register a new userHuman and computer learning: An experimental study
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Added by
Francisco Antonio Tamarit
Publication date
2004
fields
Biology
and research's language is
English
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Simple memorizing tasks have been chosen such as a binary code on a matrix. After the establishment of an appropriate protocol, the codified matrices were individually presented to 150 university students who had to memorize them. A computer simulation for a similar task is available which uses a perceptron on which an algorithm was implemented allowing for some degree of globality (technically referred to as entropic nonextensivity within a current generalization of the usual, Boltzmann-Gibbs, statistical mechanics). Our main observation is that, for the very specific learning task on which we focus here, humans perform similarly to slightly nonextensive perceptrons.
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Depression has been associated with impaired neural processing of reward and punishment. However, to date, little is known regarding the relationship between depression and intertemporal choice for gain and loss. We compared impulsivity and inconsistency in intertemporal choice for monetary gain and loss (quantified with parameters in the q-exponential discount function based on Tsallis statistics) between depressive patients and healthy control subjects. This examination is potentially important for advances in neuroeconomics of intertemporal choice, because depression is associated with reduced serotonergic activities in the brain. We observed that depressive patients were more impulsive and time-inconsistent in intertemporal choice action for gain and loss, in comparison to healthy controls. The usefulness of the q-exponential discount function for assessing the impaired decision-making by depressive patients was demonstrated. Furthermore, biophysical mechanisms underlying the altered intertemporal choice by depressive patients are discussed in relation to impaired serotonergic neural systems. Keywords: Depression, Discounting, Neuroeconomics, Impulsivity, Inconsistency, Tsallis statistics
This paper presents an experimental study to investigate the learning and decision making behavior of individuals in a human society. Social learning is used as the mathematical basis for modelling interaction of individuals that aim to perform a perceptual task interactively. A psychology experiment was conducted on a group of undergraduate students at the University of British Columbia to examine whether the decision (action) of one individual affects the decision of the subsequent individuals. The major experimental observation that stands out here is that the participants of the experiment (agents) were affected by decisions of their partners in a relatively large fraction (60%) of trials. We fit a social learning model that mimics the interactions between participants of the psychology experiment. Misinformation propagation (also known as data incest) within the society under study is further investigated in this paper.
Though it goes without saying that linear algebra is fundamental to mathematical biology, polynomial algebra is less visible. In this article, we will give a brief tour of four diverse biological problems where multivariate polynomials play a central role -- a subfield that is sometimes called algebraic biology. Namely, these topics include biochemical reaction networks, Boolean models of gene regulatory networks, algebraic statistics and genomics, and place fields in neuroscience. After that, we will summarize the history of discrete and algebraic structures in mathematical biology, from their early appearances in the late 1960s to the current day. Finally, we will discuss the role of algebraic biology in the modern classroom and curriculum, including resources in the literature and relevant software. Our goal is to make this article widely accessible, reaching the mathematical biologist who knows no algebra, the algebraist who knows no biology, and especially the interested student who is curious about the synergy between these two seemingly unrelated fields.
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets or the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurf
In this paper we propose an $infty-$dimensional cerebellar model of neural controller for realistic human biodynamics. The model is developed using Feynmans action-amplitude (partition function) formalism. The cerebellum controller is acting as a supervisor for an autogenetic servo control of human musculo-skeletal dynamics, which is presented in (dissipative, driven) Hamiltonian form. The $infty-$dimensional cerebellar controller is closely related to entropic motor control. Keywords: realistic human biodynamics, cerebellum motion control, $infty-$dimensional neural network
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