Predicting Human Behavior in Crowds: Cognitive Modeling versus Neural Networks

2013 ◽  
pp. 73-82
Author(s):  
Mark Hoogendoorn
Keyword(s):  
Neural Networks ◽  
Human Behavior ◽  
Cognitive Modeling

Looking into a Deluded Brain through a Neuroimaging Lens

2020 ◽  
pp. 107385842093617
Author(s):  
Shokouh Arjmand ◽  
Kristi A. Kohlmeier ◽  
Mina Behzadi ◽  
Mehran Ilaghi ◽  
Shahrzad Mazhari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Animal Models ◽  
Cognitive Modeling ◽  
Human Experience ◽  
Common Symptom ◽  
Brain Disorders ◽  
False Beliefs ◽  
The Neural Networks ◽  
The Brain

Delusions are irrational, tenacious, and incorrigible false beliefs that are the most common symptom of a range of brain disorders including schizophrenia, Alzheimer’s, and Parkinson’s disease. In the case of schizophrenia and other primary delusional disorders, their appearance is often how the disorder is first detected and can be sufficient for diagnosis. At this time, not much is known about the brain dysfunctions leading to delusions, and hindering our understanding is that the complexity of the nature of delusions, and their very unique relevance to the human experience has hampered elucidation of their underlying neurobiology using either patients or animal models. Advances in neuroimaging along with improved psychiatric and cognitive modeling offers us a new opportunity to look with more investigative power into the deluded brain. In this article, based on data obtained from neuroimaging studies, we have attempted to draw a picture of the neural networks involved when delusion is present and evaluate whether different manifestations of delusions engage different regions of the brain.

The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words

2007 ◽  
Vol 8 (3) ◽  
pp. 135-142 ◽  
Author(s):  
Danilo Fum ◽  
Fabio Del Missier ◽  
Andrea Stocco
Keyword(s):  
Human Behavior ◽  
Cognitive Modeling ◽  
Better Than

Neural networks as a unifying learning model for random normal form games

2011 ◽  
Vol 19 (6) ◽  
pp. 383-408 ◽  
Author(s):  
Leonidas Spiliopoulos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Normal Form ◽  
Human Behavior ◽  
Nash Equilibria ◽  
Human Subjects ◽  
Correlation Coefficients ◽  
Normal Form Games ◽  
The Neural Network ◽  
Iterated Dominance

This article models the learning process of a population of randomly rematched tabula rasa neural network agents playing randomly generated 3 × 3 normal form games of all strategic types. Evidence was found of the endogenous emergence of a similarity measure of games based on the number and types of Nash equilibria, and of heuristics that have been found effective in describing human behavior in experimental one-shot games. The neural network agents were found to approximate experimental human behavior very well across various dimensions such as convergence to Nash equilibria, equilibrium selection, and adherence to principles of dominance and iterated dominance. This is corroborated by evidence from five studies of experimental one-shot games, because the Spearman correlation coefficients of the probability distribution over the neural networks’ and human subjects’ actions ranged from 0.49 to 0.89.

FUZZY NEURAL NETWORKS AND COGNITIVE MODELING

2000 ◽  
Vol 29 (1) ◽  
pp. 7-28
Author(s):  
M.M. GUPTA ◽  
P. MUSILEK
Keyword(s):  
Neural Networks ◽  
Cognitive Modeling ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

scholarly journals Smart Home System for Energy Saving using Genetic- Fuzzy-Neural Networks Approach

2016 ◽  
Vol 08 (1) ◽  
Keyword(s):  
Neural Networks ◽  
Energy Saving ◽  
Human Behavior ◽  
Smart Home ◽  
Cost Effective ◽  
Fuzzy Neural Networks ◽  
Behavior Patterns ◽  
Power Losses ◽  
Sustainable Improvement ◽  
Fuzzy Neural

Home energy saving is very important to realize sustainable improvement. This can be achieved by designing a smart home system that provides a productive and cost-effective environment through optimization of different factors that will be explained in this paper. In this paper, an adaptive smart home system for optimal utilization of power will be designed. The system is based on genetic-fuzzy-neural networks technique, which can capture a human behavior patterns and use it to predict the user's mood. This technique will improve the intelligence of the smart home control to minimize the power losses.

scholarly journals Pseudoneglect in visual search: Behavioral evidence and connectional constraints in simulated neural circuitry

2017 ◽  
Author(s):  
Onofrio Gigliotta ◽  
Tal Seidel Malkinson ◽  
Orazio Miglino ◽  
Paolo Bartolomeo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Visual Search ◽  
Human Behavior ◽  
Undergraduate Students ◽  
Causal Link ◽  
Brain Hemispheres ◽  
Attentional Networks ◽  
Artificial Neural ◽  
Biological Constraints

AbstractMost people tend to bisect horizontal lines slightly to the left of their true center (pseudoneglect), and start visual search from left-sided items. This physiological leftward spatial bias may depend on hemispheric asymmetries in the organization of attentional networks, but the precise mechanisms are unknown. Here we modeled relevant aspects of the ventral and dorsal attentional networks (VAN and DAN) of the human brain. First, we demonstrated pseudoneglect in visual search in 101 right-handed psychology students. Participants consistently tended to start the task from a left-sided item, thus showing pseudoneglect. Second, we trained populations of simulated neurorobots to perform a similar task, by using a genetic algorithm. The neurorobots’ behavior was controlled by artificial neural networks, which simulated the human VAN and DAN in the two brain hemispheres. Neurorobots differed in the connectional constraints that were applied to the anatomy and function of the attention networks. Results indicated that (1) neurorobots provided with a biologically plausible hemispheric asymmetry of the VAN-DAN connections, as well as with inter-hemispheric inhibition, displayed the best match with human data; however, (2) anatomical asymmetry per se was not sufficient to generate pseudoneglect; in addition, the VAN must have an excitatory influence on the ipsilateral DAN; (3) neurorobots provided with bilateral competence in the VAN but without inter-hemispheric inhibition failed to display pseudoneglect. These findings provide a proof of concept of the causal link between connectional asymmetries and pseudoneglect, and specify important biological constraints that result in physiological asymmetries of human behavior.Significance statementMost of us start our exploration of the environment from the left side. Here we demonstrated this tendency in undergraduate students, and trained artificial agents (neurorobots) to perform a similar visual search task. The neurorobots’ behavior was controlled by artificial neural networks, inspired by the human fronto-parietal attentional system. In seven distinct populations of neurorobots, different constraints were applied on the network connections within and between the brain hemispheres. Only one of the artificial populations behaved in a similar way to the human participants. The connectional constraints applied to this population included known characteristics of the human fronto-parietal networks, but had also additional properties not previously described. Thus, our findings specify biological constraints that induce physiological asymmetries of human behavior.

COGNITIVE COMPARISON MODELS FORMING SEMANTICS OF THE ENGLISH PHRASEOLOGICAL UNITS WITH A ZOONYM COMPONENT

2021 ◽  
pp. 39-46
Author(s):  
L.V. Babina ◽  
◽  
E.V. Dolgova ◽  
Keyword(s):  
Human Behavior ◽  
Cognitive Modeling ◽  
English Language ◽  
Physiological State ◽  
Human Being ◽  
Physical Quality ◽  
Cognitive Mechanism ◽  
Comparative Constructions ◽  
Subsequent Comparison

The article deals with comparison as a cognitive mechanism that determines the formation of the semantics of the phraseological units (hereinafter - PU) of the English language that convey knowledge about human being. The authors study the PU that include zoonyms and structurally represent comparative constructions of two types: comparative constructions as ... as and comparisons with the word like . The method of cognitive modeling enabled to reveal the cognitive comparison models used in creating the considered PU. The study allowed us to identify the following cognitive models of comparison: 1) HUMAN BEING AS PHYSICAL QUALITY/PHYSIOLOGICAL STATE AS ANIMAL (either physical/physiological characteristics are compared, or the characteristic is transferred from the physical/physiological to the psychological domain); 2) HUMAN BEING AS PSYCHOLOGICAL QUALITY AS ANIMAL (helps to comprehend information about psychological qualities and human behavior through the prism of ideas about animals); 3) HUMAN BEING LIKE ANIMAL (used when comparing human and animal features); 4) HUMAN BEING ACT LIKE ANIMAL (used when comparing the actions of people and animals). In conclusion the authors state the role of cognitive comparison models in forming the semantics of the PU in the English language, which is definitely important: they help to organize knowledge from different domains for their subsequent comparison.

scholarly journals Deep neural networks can predict human behavior in arcade games

2019 ◽  
Author(s):  
Holger Mohr ◽  
Radoslaw M. Cichy ◽  
Hannes Ruge
Keyword(s):  
Neural Networks ◽  
Human Behavior ◽  
Deep Neural Networks
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