A New Model for Predicting Emotional Behavior Based on Bidirectional Associative Memory

A person's emotional state changes due to the emotional stimulus he is experiencing. During a conversation, these changes are noticeably reflected in his voice and tone of speaking as well as in the linguistic components of his speech. On the receiving side, the listener can recognize the emotional content of the received message and also, predict the emotional behavior associated with it. The human nervous and cognitive system is able to perform this process efficiently and effortlessly despite all the complexities involved in computationally defining a model for this process. In other words, it recognizes the emotional content in the speech and predict a set of related behaviors for the speaker according to that recognized emotional state. This issue is crucial in human-machine interaction studies. Here, a model based on Bidirectional Associative Memory is presented, which can predict the corresponding emotional behavior based on the emotional state. The results of the simulation show that this model is able to perform the process of mapping bidirectionally between emotional states and the set of behaviors related to emotional states and is robust against some degrees of information incompleteness and the presence of some level of noise.