Neural network modeling of emotion

Levine, Daniel S.

doi:10.1016/j.plrev.2006.10.001

Cited by 47 publications

(23 citation statements)

References 128 publications

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“…Various attempts have dealt with human emotional state using a computational model, such as the analysis of emotion based on the facial expression [18] and emotional model based on computational neural network modeling [22].…”

Section: Emotional Model To Human Robot Interactionmentioning

confidence: 99%

Emotional Empathy Model For Robot Partners Using Recurrent Spiking Neural Network Model With Hebbian-Lms Learning

Woo

Botzheim

Kubota

2017

MJCS

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This paper discusses the development of an emotion model for robot partner system. In our previous studies, we have focused only on the robot's emotional state. However, the emotional state of the other party is also an important factor for smooth conversation in human society. Therefore, the robot partner has two emotional structures for human: empathy and robot emotion. First, human empathy uses a perceptual based emotion model to know the human's emotional state based on the sensory information. Next, we propose a recurrent simple spike response model to improve the robot's emotional model, and we apply "Hebbian-LMS" learning to modify the weights in the spiking neural network. The robot's emotional state is calculated by using the human's emotional information, internal and external information. The robot partner can use the emotional results to control the facial and gesture expression. The utterance style is also changed by the robot's emotional state. As a result, the robot partner can interact emotionally and naturally with human. First, we explain the related works and the development of the robot partner "iPhonoid-C". Next, we define the architecture of the emotional model to realize emotional empathy towards human. Then, we discuss the algorithms and the methods for developing the emotional model. Finally, we show experimental results of the proposed method, and discuss the effectiveness of the proposed structure.

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Section: Emotional Model To Human Robot Interactionmentioning

confidence: 99%

Emotional Empathy Model For Robot Partners Using Recurrent Spiking Neural Network Model With Hebbian-Lms Learning

Woo

Botzheim

Kubota

2017

MJCS

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“…The experimental results showed that it was difficult to study by using environmental propensity for stiff competition learner, and periodic associative memory was more important in stiff competition [16]. Levine presented a neural network modeling method for affective computing and systematically discussed the neural mechanisms of emotion and cognition [17]. Chartier et al introduced nonlinear dynamical systems into research on ANNs and proposed so-called nonlinear dynamical artificial neural networks (NDANNs), which can compensate for the deficiencies of traditional ANNs with respect to nonlinear reasoning.…”

Section: Introductionmentioning

confidence: 99%

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen

Tan

2018

Complexity

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Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNsbased cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing-artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.

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“…They focus on various aspects of it, like computational models of emotion (FLA-ME, EMA, ParleE, Greta, GALAAD, etc. ), behaviourbased robotics, agents systems, and many others (El-Nasr et al, 2000;Gratch and Marsella, 2004;Bui et al, 2002;Poggi et al, 2005;Levine, 2007;Bonarini et al, 2006). Still, there is a lack of projects which result in applicable models of human motivation psychology, and most of the available results are generally not suitable for our purposes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent decision-making system for autonomous robots

Kowalczuk¹,

Czubenko²

2011

International Journal of Applied Mathematics and Computer Science

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The paper gives an account of research results concerning a project on creating a fully autonomous robotic decisionmaking system, able to interact with its environment and based on a mathematical model of human cognitive-behavioural psychology, with some key elements of personality psychology included. The principal idea of the paper is focused on the concept of needs, with a certain instrumental role of emotions.

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Neural network modeling of emotion

Cited by 47 publications

References 128 publications

Emotional Empathy Model For Robot Partners Using Recurrent Spiking Neural Network Model With Hebbian-Lms Learning

Emotional Empathy Model For Robot Partners Using Recurrent Spiking Neural Network Model With Hebbian-Lms Learning

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Intelligent decision-making system for autonomous robots

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