AI News, BOOK REVIEW: Original articleCognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution

Original articleCognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution

The experimental results show that the model in human–computer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot.

This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.

Discrete Dynamics in Nature and Society

This paper integrated Gross cognitive process into the HMM (hidden Markov model) emotional regulation method and implemented human-robot emotional interaction with facial expressions and behaviors.

The two random quantities in emotional transition process—the emotional family and the specific emotional state in the AVS (arousal-valence-stance) 3D space—were used to simulate human emotion selection.

Experimental results show that the emotional regulation model does not simply provide the typical classification and jump in terms of a set of emotional labels but that it operates in a 3D emotional space enabling a wide range of intermediary emotional states to be obtained.

Lazarus believes that the growing importance of cognitive-mediational or value-expectancy approaches to mind and behavior in social sciences has promoted the prosperity of emotions as discrete categories [4].

In his research, six prototypical emotions (anger, fear, sorrow, happiness, disgust, and surprise) are implemented with innate personality and the capacity of acquired learning [8].

put forward the artificial emotional states (happy, confident, concerned, and frustrated) for multiagent systems, and the emotions are released depending on the task process [9].

Unlike discrete models, the emotional space models consider a continuous multidimensional space where each point stands for an emotional state and each dimension stands for a fundamental property common to all emotions.

model the OCC reasoning process that can produce the cognitive emotions and touch off complex emotional experiences via the trend of events (including event, object, and agent) [18].

First, based on Gross cognitive strategy, the cognitive reappraisal model satisfied Fischna-Weber law was built, and a physiological endurance coefficient was defined to meet the psychological diversity.

Second, emotional state space in the active field was established on the solid basis of the physiological energy distribution, and the transition probability among the emotional families was figured out.

Gross believes that spontaneity cognitive reappraisal, as an essential part of psychological defense mechanism, could help individuals to consider the emotional stimulus from the peaceful perspective [23].

Under the spontaneity guidance cognitive reappraisal, the feel-intensity of the negative emotion can be drawn from formula (2): Here, is the feel-intensity before spontaneity cognitive reappraisal, is the feel-intensity after spontaneity cognitive reappraisal, is the stimulus-intensity, and and are constant.

There are 7 typical experiment scenes corresponding to 7 external stimulus emotions and 500 volunteers of random selection in different ages including five groups of 11~20, 21~30, 31~40, 41~50, and 51~60 years.

By processing and analysis of each scene’s data with the hypothesis test based on Gaussian iteration, we can find that after the cognitive reappraisal the influence of positive scenes has no obvious change and the change of negative emotion obeys Gauss distribution shown in Figure 3.

Different to traditional limited emotional states, emotional regulation process is defined in the case of continuous time and continuous state space for making the robot more lively and anthropopathic.

So the stimulus emotional state space isAccording to the microexpression recognition method in [24], the facial microexpression of participants in the communication is mapped into 7 prototypical emotions in order to simplify the experiment.

According to the reactions to external excitations [25–28], in this research, the facial emotion type and the action range correspond to the position of energy source and the energy size, respectively.

From the field theory, the activated intensity at any point in emotional state space iswhere is the number of emotional sources, is the distance from emotional source to point , is ’s direction vector, is a coefficient, and is the intensity of emotional source .

The computing method about emotional potential energy having activated emotional states is (Figure 4) Individual emotional state is driven and produced by psychological energy.

In , the sum of emotional potential energy for each point along the field direction is The transition probability from the current emotional family to the next is Human emotional regulation can be divided into two steps;

Figure 8 graphically shows an example of psychological energy distribution in the active field state space, where both the current emotional state and the stimulus emotional state after cognitive reappraisal as psychological energy resources are trying to influence emotional regulation process.

Based on the relative positions of the next emotional state and emotional source, that the emotional family contains higher energy will has greater transition probability than the emotional family which contains lower energy.

According to Section 3.3, the distribution of emotional family’s probability is calculated under a pair of emotions, robot’s cognitive stimulus emotional state and its own current emotional state, and then the output of robot emotional state has 26 kinds of possibility on the basis of the spatial relationship among the current emotion, stimulus emotion, and next emotion.

This method highlights the capability to find a large amount of intermediate emotional states, which are extremely vital since they enrich the output of the robot emotional regulation system.

and we can observe the transition probability’s microvariation of emotional families where 6 prototypical emotions (except sadness, because the psychological energy of sadness exceeds the threshold value, and it will not happen) are with robot’s own emotional state changes.

In this scale, participants forecast robot’s next output state by the simplified affect scale (as Figure 2) including 7 typical stimulus states and 7 typical own emotional sates (a total of 7 ×

Objectively speaking, the use of the emotion model based on the cognitive reappraisal in active field allows robot to imitate the hominine emotional regulation, and that is, in fact, the aim of our work.

But the obtained results are difficult to compare with other emotional regulation studies that can be found in literature, because most of such studies do not recognize stimulus emotions in microexpressions and transfer emotional states in arousal-valence-stance terms.

Moreover the few studies that do have not been tested under physical robot experimental conditions (specific robot device and experimental platform refer to [30, 31]) and do not provide as output the coordinates of the studied emotional state in the 3D space.

In this paper, the noteworthy feature of emotional regulation work was out of the simply interactive mode providing the classification and jump in terms of a set of emotional labels, and it operated in a 3D emotional space enabling a wide range of intermediary emotional states obtained under the external stimulus.

Moreover, this system focused on the research field of emotional regulation depending on natural facial expression cognition and proposed a microexpression cognition and emotional regulation model based on Gross reappraisal strategy.

Gross cognitive reappraisal strategy effectively decreased negative emotional experience and behavioral expression, so it could provide an intelligent cognition style to computer/robot acting as a positive role in HCI.

Following from this, future works should be oriented to the study of nature inspired cognitive-affective computing by means of emotion modeling in continuous active space and especially need pay more attention to the multimodal external stimulus and the pervasive emotion computing.

Robot Emotion and Performance Regulation Based on HMM

Emotion is a high-level intelligent behaviour that reflects human thinking activity and is represented as an implied state in the HMM model.

When the object's facial expression is recognized and mapped into the emotion space, the robot produces the facial expression plan and behaviour plan via the HMM model.

The scale includes involved with the advantageous projects, such as interactive fluency, facial expressive degree, facial expression accuracy, the plenty degree of behaviours/expressions, behaviour accuracy and friendliness.

Advantageous projects from very satisfied to discontented is divided into five levels, which is given a mark as 5, 4, 3, 2, 1 credit respectively.

Disadvantageous project from very agreement to disagreement is also divided into five levels, which is given a mark as 1, 2, 3, 4, 5 credit respectively.

The experimental evaluation results show that compared with the Stimulation-response model, robot performance is more expressive and friendly in the HMM model and the accuracy and exaggeration degree is not raised obviously.

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