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海马体在情感调节中的作用海马体是大脑内部的一个重要结构，对于情感调节起着至关重要的作用。
本文将介绍海马体的结构和功能，以及它在情感调节中的作用。
一、海马体的结构和功能海马体位于大脑内侧颞叶的深处，由两个海马体回组成，其形状类似于海马。
海马体与记忆和情感调节密切相关。
它由海马齿状回、海马旁回和子盖组成，其中海马齿状回被认为是最重要的结构之一。
海马体扮演着连接大脑皮层和下丘脑的重要角色。
它与下丘脑的通往杏仁核和纹状体的传导通路共同组成了情绪调节的神经网络。
海马体还参与了多巴胺、去甲肾上腺素和5-羟色胺等神经递质的调节，进而影响情感的表达和调节。
二、1. 情感记忆的形成与调节海马体与情感记忆的形成密切相关。
情感记忆是特定情境下的记忆，常常伴随着强烈的情感体验。
海马体通过与皮质区域的联系，参与了情绪记忆的编码和检索过程。
它可以将情感体验与环境上下文相结合，形成更为丰富和稳定的情感记忆。
2. 情绪表达与调节海马体与情感的表达和调节紧密相连。
海马体与扁桃体、杏仁核等情感中枢结构相互连接，共同组成情感调节的神经网络。
当人们面临压力、焦虑或恐惧等负面情绪时，海马体参与了情感调节的过程，有助于缓解负面情绪并保持情绪的稳定。
3. 自我意识与情感识别海马体还与自我意识和情感识别有关。
海马体的活动与个体对自我以及他人情感状态的认知密切相关。
一些研究表明，海马体在情感识别任务中的激活与个体对情感表情的敏感性和辨别能力相关。
4. 应激反应的调节海马体与应激反应的调节紧密相连。
在面对压力和应激时，海马体通过与下丘脑的传导通路，调节了应激反应的强度和持续时间。
它能够抑制下丘脑-杏仁核-纹状体等神经回路的活动，从而减轻情绪的负面反应。
总结：综上所述，海马体在情感调节中发挥着重要作用。
它参与了情绪记忆的形成和调节，情感的表达和调节，自我意识和情感识别，以及应激反应的调节。
海马体的功能障碍与情感障碍、焦虑症和抑郁症等情绪障碍有关。
进一步研究海马体的功能和调控机制，对于理解和治疗情感障碍具有重要的临床意义。
第1篇一、实验背景情绪智力（Emotional Intelligence，简称EI）是指个体识别、理解、管理和调节自己及他人情绪的能力。
近年来，情绪智力在心理学、管理学和教育学等领域得到了广泛关注。
研究表明，情绪智力对个体的人际关系、职业发展和心理健康等方面具有重要影响。
本实验旨在探讨情绪智力对人际关系的影响，为提高个体情绪智力水平提供理论依据。
二、实验目的1. 探讨情绪智力与人际关系之间的相关性。
2. 分析情绪智力在人际关系中的具体作用机制。
3. 为提高个体情绪智力水平，促进人际关系和谐提供实证依据。
三、实验方法1. 被试：选取60名大学生作为实验对象，其中男生30名，女生30名，年龄在18-22岁之间。
2. 实验工具：（1）情绪智力量表：采用王登峰编制的情绪智力量表，包括自我情绪识别、情绪理解、情绪调节和情绪管理四个维度。
（2）人际关系量表：采用社会关系量表，包括亲密关系、友谊关系和职业关系三个维度。
3. 实验步骤：（1）对被试进行情绪智力量表和人际关系量表的施测，收集数据。
（2）对收集到的数据进行统计分析，探讨情绪智力与人际关系之间的相关性。
（3）根据相关性分析结果，进一步探讨情绪智力在人际关系中的具体作用机制。
四、实验结果1. 情绪智力与人际关系的相关性分析：（1）情绪智力总分与人际关系总分呈显著正相关（r=0.532，p<0.01）。
（2）情绪智力的四个维度与人际关系三个维度均呈显著正相关。
2. 情绪智力在人际关系中的作用机制分析：（1）自我情绪识别与亲密关系呈显著正相关（r=0.415，p<0.01），说明个体对自身情绪的识别能力越高，其亲密关系越好。
（2）情绪理解与友谊关系呈显著正相关（r=0.382，p<0.01），说明个体对他人情绪的理解能力越高，其友谊关系越好。
（3）情绪调节与职业关系呈显著正相关（r=0.456，p<0.01），说明个体对情绪的调节能力越高，其职业关系越好。
大脑与情感的神经生物学机制人类情感的复杂性一直是心理学和神经科学研究的热点。
随着技术的进步，我们对大脑与情感之间的神经生物学机制有了更深入的了解。
本文将探讨大脑与情感之间的紧密联系及其神经生物学机制。
一、情感的定义与类型情感是指人类对外界刺激所产生的主观体验，包括喜、怒、哀、乐等。
根据情感的性质和表现形式，可以将情感分为基本情感和复杂情感。
基本情感是一种原始的，与生俱来的情感，如恐惧、愉悦等；而复杂情感则是人们在日常生活中所表现出的更为综合和复杂的情感，如爱、憎、嫉妒等。
二、情感的神经解剖基础大脑是人类情感生成和调控的中心。
情感的产生和调控涉及多个脑区的协同作用。
主要的脑区包括杏仁核、扣带回、海马体等。
杏仁核是情感加工的枢纽，它对外界刺激进行初步的评估和筛选，并参与情感记忆的形成。
扣带回是情感的调节中心，它通过调控杏仁核的活动来影响情感的表达和体验。
海马体则负责情感记忆的存储与检索。
三、情绪的神经机制情绪是情感的一种表现形式，是短暂的、有主观体验的情感状态。
情绪与大脑中多个神经递质的分泌紧密相关。
例如，多巴胺和血清素等神经递质参与了愉悦和快乐情绪的产生，而儿茶酚胺则与愤怒和厌恶等负性情绪的产生有关。
四、情感与记忆的相互作用情感和记忆之间存在着紧密的相互作用关系。
情感能够加强记忆的形成和存储，并通过情感记忆的检索来影响人们的情绪体验。
这种相互作用是通过海马体和杏仁核等脑区的协同活动实现的。
五、情感引发的心理生理反应情感的激发会导致一系列心理生理反应的产生，例如心跳加快、呼吸急促、面部表情的改变等。
这些心理生理反应是通过自主神经系统的调控实现的。
六、情感的个体差异每个人在情感体验和表达上都存在着一定的个体差异。
这些差异可能与个体的遗传背景、环境经验等因素有关。
未来的研究还需进一步探究这些个体差异的神经基础。
七、情感障碍的神经生物学基础情感障碍是情感调节失调所导致的精神健康问题，如抑郁症、焦虑症等。
这些情感障碍的发生可能与大脑神经回路的功能异常有关，如杏仁核-扣带回回路的失调。
基于脑电信号的情绪识别李贤哲;暴伟;谢能刚【期刊名称】《北京生物医学工程》【年(卷),期】2022(41)1【摘要】目的对多种情绪进行快速准确的识别,是目前脑机接口和情感计算领域的研究热点。
本文针对多情绪分类问题及个体差异的影响因素,设计一种电影片段诱发实验,利用机器学习算法对9位被试不同情绪的脑电信号进行分析,以期能够快速准确识别不同被试的情绪状态。
方法首先采用非侵入式脑电设备收集被试在恐惧、愤怒、悲伤和快乐4种情绪下的脑电信号,通过对信号进行降噪处理,使用时域、频域和非线性动力学的特征提取方法,共提取出15种不同的有效特征,并根据均方根特征和三维时域特征的散点图来验证4种情绪之间的区分性;最后以平均准确率作为分类识别的评价指标,应用K近邻算法对9位被试整体的脑电特征进行训练和分类。
结果3种时域特征的识别率差异较大,一阶差分绝对值的均值特征平均准确率达到95%,其余时域特征分类效果一般;频域特征中使用Welch法得到Gamma频带特征识别效果最好,平均准确率超过95%;非线性动力学特征识别率较好,平均分类准确率都超过90%。
结论利用Welch法得到的Gamma频带特征和一阶差分绝对值的均值作为最优特征,能够快速准确识别不同被试的情绪状态。
【总页数】10页(P8-16)【作者】李贤哲;暴伟;谢能刚【作者单位】安徽工业大学管理科学与工程学院【正文语种】中文【中图分类】R318.04【相关文献】1.基于脑电信号的情绪状态识别算法研究2.基于脑电信号的情绪识别概述3.基于栈式自编码神经网络的脑电信号情绪识别4.基于多通道脑电信号的因果网络情绪识别5.基于时空自适应图卷积神经网络的脑电信号情绪识别因版权原因，仅展示原文概要，查看原文内容请购买。
海马体的神经调控与情感记忆海马体是大脑中重要的神经结构之一，对于情感记忆的形成和调控起着关键作用。
海马体通过参与神经回路的特定调节，能够对情绪和记忆进行有效地整合和加工。
下面将从海马体与情感记忆的关系、海马体的神经调控机制以及相关的研究进展三个方面进行论述。
一、海马体与情感记忆的关系海马体作为大脑皮层和边缘系统之间的重要连接部位，与情感记忆的形成和调控息息相关。
情感记忆是指在特定情绪体验下形成的记忆，其中包含了个体对于情感信息的加工和存储。
海马体通过与边缘神经系统的相互作用，能够对情感刺激进行感知和评估。
研究发现，海马体在情感记忆中的功能主要表现在两个方面：一是海马体对情感体验的加工和调节，二是海马体对情感记忆的存储和检索。
在情感体验方面，海马体通过与边缘系统的连接，参与到情感信息的加工和评估中，从而影响个体对情感刺激的感知和反应。
在情感记忆的存储和检索方面，海马体具有将情感记忆与相关的空间和时间信息进行整合的能力，同时也是情感记忆的重要存储和检索场所。
二、海马体的神经调控机制海马体的神经调控机制十分复杂，涉及到多个神经递质和神经活动的调节。
以下是海马体神经调控机制的主要内容：1. 神经递质：多种神经递质参与到海马体的神经调控中。
例如，谷氨酸作为兴奋性神经递质，能够增强神经元之间的连接和信息传递，从而参与到情感记忆的形成和调控中。
而γ-氨基丁酸（GABA）则是一种抑制性神经递质，能够抑制神经元的兴奋性，起到平衡和调控神经活动的作用。
2. 神经环路：海马体与边缘系统、杏仁核等结构之间形成了庞大的神经回路。
这些神经回路通过神经纤维的传导，使得情感信息能够被传入到海马体，并在海马体中得到整合和调控。
同时，海马体也通过神经回路将情感记忆的结果输出到其他脑区，从而影响个体的行为和情绪反应。
3. 突触可塑性：突触可塑性是指神经元通过长期的活动和经验改变突触连接的能力。
海马体作为一个重要的突触可塑性场所，通过突触的形成和重塑，能够对情感记忆进行加工和调控。
I.J. Image, Graphics and Signal Processing, 2012, 8, 50-56Published Online August 2012 in MECS (/)DOI: 10.5815/ijigsp.2012.08.07Human Emotion Recognition SystemDilbag SinghComputer Science and Engineering Dept.Guru Nanak Dev University Amritsar (Punjab) India.e-mail: dggill2@Abstract—This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc..). Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 97 percent accurate results and it is easy and simplest way than Emotion recognition using brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.Index Terms—Emotions, Visible color difference, Mood, Brain activityI.I NTRODUCTIONWe know that emotions play a major role in a Human life. At different kind of moments or time Human face reflects that how he/she feels or in which mood he/she is. Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. Emotion or intention is often communicated by subtle changes in one or several discrete features.The addition or absence of one or more facial actions may alter its interpretation. In addition, some facial expressions may have a similar gross morphology but indicate varied meaning for different expression intensities. In order to capture the subtlety of facial expression in non-verbal communication,I will use an existing simulator which will be able to capture human emotions by reading or comparing facial expressions. This algorithm automatically extracts features and their motion information, discriminate subtly different facial expressions, and estimate expression intensity.Fig1 is showing how Emotion recognition using brain activity performs its task.Fig. 1: Emotion recognition using brain activityII.P ROBLEM DEFINITIONAs described in the overview, the purpose of this paper is to analyze the limitations with existing system Emotion recognition using brain activity [1]. In Emotion recognition using brain activity the developer Robert Horlings has used brain activities which is toughest task to do as it become expensive, complex and also time consuming when we try to measure human brain with Electroencephalography (eeg).They have used existing data and the result of their analysis were 31 to 81 percentage correct and from which even by using Fuzzy logic 72 to 81 percentage only for two classes of emotions. Apparently the division between different emotions is not (only) based on EEG signals it depend on some another.In this paper I am going to purpose a system (by using an existing simulator) which is capable for achieving up to 97 percentage results and easy than Emotion recognition using brain activity system. My purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper I have also try to use neural network for better results by using a existing simulator.III.H UMAN E MOTIONSEmotion is an important aspect in the interaction and communication between people. Even though emotions are intuitively known to everybody, it is hard to define emotion. The Greek philosopher Aristotle thought of emotion as a stimulus that evaluates experiences based on the potential for gain or pleasure. Years later, in the seventeenth century, Descartes considered emotion to mediate between stimulus and response [2]. Nowadays there is still little consensus about the definition of emotion. Kleinginna and Kleinginna gathered and analyzed 92 definitions of emotion from literaturepresent that day [3]. They conclude that there is little consistency between different definitions and suggested the following comprehensive definition:Emotion is a complex set of interactions among subjective and objective factors, mediated by neural/hormonal systems, which can:1. Give rise to affective experiences such as feelings of arousal, pleasure/displeasure;2. Generate cognitive processes such as emotionally relevant perceptual effects, appraisals, labelling processes;3. Activate widespread physiological adjustments to the arousing conditions; and4. Lead to behaviour that is often, but not always, expressive, goal directed, and adaptive.This definition shows the different sides of emotion. On the one hand emotion generates specific feelings and infl uences someone‘s behavior. This part of emotion is well known and is in many cases visible to a person himself or to the outside world. On the other hand emotion also adjusts the state of the human brain, and directly or indirectly influences several processes [2]. Fig2 is showing different Human emotions which are use able in order to achieve our goal.Fig. 2: Different Human emotionsIn spite of the difficulty of precisely defining it, emotion is omnipresent and an important factor in human life. People‘s moods heavily influence their way of communicating, but also their acting and productivity. Imagine two car drivers, one being happy and the other being very mad. They will be driving totally different. Emotion also plays a crucial role in all-day communication. One can say a word like `OK' in a happy way, but also with disappointment or sarcasm. In most communication this meaning is interpreted from the tone of the voice or from non-verbal communication. Other emotions are in general only expressed by body language, like boredom [1].A large part of communication is done nowadays by computer or other electronic devices. But this interaction is a lot different from the way human beings interact. Most of the communication between human beings involves non-verbal signs, and the social aspect of this communication is important. Humans also tend to include this social aspect when communicating with computers [3].IV.R ELATED W ORKResearch efforts in human–computer interaction are focused on the means to empower computers (robots and other machines) to understand human intention, e.g. speech recognition and gesture recognition systems [1]. In spite of considerable achievements in this area during the past several decades, there are still a lot of problems, and many researchers are trying to solve them. Besides, there is another important but ignored mode of communication that may be important for more natural interaction: emotion plays an important role in contextual understanding of messages from others in speech or visual forms.There are numerous areas in human–computer interaction that could effectively use the capability to understand emotion [2], [3]. For example, it is accepted that emotional ability is an essential factor for the next-generation personal robot, such as the Sony AIBO [4]. It can also play a significant role in ‗intelligent room‘ [5] and ‗affective computer tutor‘ [6]. Although limited in number compared with the efforts being made towards intention-translation means, some researchers are trying to realise man–machine interfaces with an emotion understanding capability.Most of them are focused on facial expression recognition and speech signal analysis [7]. Another possible approach for emotion recognition is physiological signal analysis. We believe that this is a more natural means of emotion recognition, in that the influence of emotion on facial expression or speech can be suppressed relatively easily, and emotional status is inherently reflected in the activity of the nervous system. In the field of psychophysiology, traditional tools for the investigation of human emotional status are based on the recording and statistical analysis of physiological signals from both the central and autonomic nervous systems [8].Researchers at IBM recently reported an emotion recognition device based on mouse-type hardware [9]. Picard and colleagues at the MIT Media Laboratory have been exerting their efforts to implement an ‗affective computer‘ since the late 1990s [10]. Although they demonstrated the feasibility of a physiological signal-based emotion recognition system, several aspects of its performance need to be improved before it can be utilized as a practical system. First, their algorithm development and performance tests were carried out with data that reflect intentionally expressed emotion.Moreover, their data were acquired from only one subject, and, hence, their emotion recognition algorithmis user-dependent and must be tuned to a specific person. It seems natural to start from the development of a user-dependent system, as the speech recognition system began with a speaker-dependent system. Nevertheless, a user-independent system is essential for practical application, so that the users do not have to be bothered with training of the system. To our knowledge, there is no previous study that has demonstrated a physiological signal-based emotion recognition system that is applicable to multiple users.Another problem with current systems is the required length of signals. At present, at least 2–5 min of signal monitoring is required for a decision [11]. For practical purposes, the required monitoring time should be reduced further. In this paper, a novel emotion recognition system based on the processing of physiological signals is presented. This system shows a recognition ratio much higher than chance probability, when applied to physiological signal databases obtained from tens to hundreds of subjects.The system consists of characteristic waveform detection, feature extraction and pattern classification stages. Although the waveform detection and feature extraction stages were designed carefully, there was a large amount of within-class variation of features and overlap among classes. This problem could not be solved by simple classifiers, such as linear and quadratic classifiers, that were adopted for previous studies with similar purposes.V.T HE SCOPE OF THIS RESEARCH WORKi.This research work deals with measuring the facialexpressions of human beings.ii.It does not deal with rest of body of the humans.iii.As there exist some methods which do the same job they are also considered in this research work.iv.Since it is not feasible to run these algorithms in real environment, therefore a simulator is developed which will simulate the proposed work.v.Different type of tests will be implemented using proposed strategy.vi.Visualization of the experimental results and drawing appropriate performance analysis.vii.Appropriate conclusion will be made based upon performance analysis.viii.For future work suitable future directions will be drawn considering limitations of existing work.Throughout the research work emphasis has been on the use of either open source tools technologies or licensed software.VI.R ESEARCH M ETHODOLOGYThe step-by-step methodology to be followed for Human emotion recognition system using neural networks:1. Study and analyze various old techniques for human emotion recognition.2. Based upon above analysis a simulator is developed for Human emotion recognition by using MATLAB 7.5 version (I have only used the simulator it is developed by a team of Luigi Rosa 'ITALY').3. Results achieved after the execution of program are compared with the earlier outputs.Requirements:Matlab, Matlab Image Processing Toolbox, Matlab Neural Network Toolbox.VII.H OW E MOTION RECOGNITION SYSTEM WORKS Consider the Fig8 in which required steps are givenfor emotion recognition.Fig. 8: How Emotion recognition system works.A. Knowledge BaseIt contains certified images which we will use for comparisons for the sake of emotion recognition. These images are highly qualified and these are stored in given database. Whenever any input is given to system, system will find the relevant picture from knowledge base by comparing input to certified images and gives a output (emotion).B. Preprocessing and ResizeThe main goal of this step is to enhance input image and also remove various type of noises. After applying these techniques we need to resize the image that is consider only human face this is done by using eye selection method.C. Difference MeasurementsIt will find the difference between the input image and the certified images (stored in knowledge base) and give result to emotion recognition step.D. Emotion RecognitionIt will get output from Difference measurement and compare the result and give output depend on minimum difference.VIII. E IGEN E XPRESSIONS FOR F ACIAL E XPRESSIONR ECOGNITION Luigi Rosa has proposed an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier.This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. This code has been tested using the JAFFE Database. Using 150 images randomly selected for training and 63 images for testing, without any overlapping, we obtain an excellent recognition rate greater than 83 percent. Major features:1. Facial expression recognition based on Neural Networks.2. PCA-based dimensionality reduction.3. High recognition rate.4. Intuitive GUI(Graphical User Interface).5. Easy C/C++ implementationsIX. E XPERIMENTAL R ESULTSI have used Luigi Rosa's EigenExpressions for Facial Expression Recognition simulator in which by giving many inputs and get many outputs which are correct to great extent some of the results are shown in Fig. 9 andFig. 10:-Fig. 9:Eyedetection in EigenExpressions for Facial ExpressionRecognition simulatorFig.10: Surprised recognition in EigenExpressions for FacialExpression Recognition simulatorX. P ROPOSED A LGORITHMAs I have shown in Fig. 8 Here I am going to explain that how my Purposed system going to achieve the work and also what will the Design of my system is. As I have mentioned so far my system will have capability to measure different type of emotions at a moment (like a man is laughing but actually he is sad). The algorithm I have developed is as:- Step 1: Input a image f(x,y).Step 2: Apply Enhancement and Restoration process to detect face accurately and get g(x,y).Step 3: % compare image with emotion categories and each category have some different sort of images like happy, very happy or a sad showing that he is happy and store them a array emotion[i][j] where I reflects section of emotion type and j reflecting section which actually have image related to i.For(i=1;i=section size;i++) For(j=1;j=total images in i;j++) if (g(x,y) like emotion[i][j]) Result[i]= %age of results;%Result[i] gives the relative measurements of g(x,y) like emotion[i][j]; Break;Step 4: For(i=1;i=section size;i++)Print section "have" Result[i]; Step 5: ExitXI. D EVELOPED S IMULATORIn this section I am going to define the design of my system that is how it looks like or what will happen if you give this kind of input. The design has been developed in visual basic and it has been implement it using Matlab. Fig. 11 is showing my purposed system when it run first time.In this system I have added three buttonsa. Input Image: This button will allow user to browse and find their image of interest.b. Eye Detection: This button is actually need to focus on human face by comparing their eyes.c. Results: Here my developed algorithm will actually work and give results like shown in Fig12. Fig.12 is showing the output of my purposed system when it willactually implemented.Fig. 11: How Human Emotion Sensing System looks like.XII. P ERFORMANCE R ESULTSAfter implementing Luigi Rosa's EigenExpressions for Facial Expression Recognition simulator I have used the performance or output of these results to compare with other method's results. An artificial neural network trained using Levenberg Marquardt algorithm was developed for emotion recognition through facial expression [2]. Fig13 illustrates the comparative performance with other approaches [3-5]. As evident the proposed approach has given very given performance for the datasets considered. We plan to extend this work for intelligent surveillance systems to detect anomaly behaviours (example: predict potential terrorist activities) in public places. We also target to develop adaptive selection strategies to detect important features etc. Results are as shown:Table Ⅰ Comparison of different approaches for Human SenseRecognitionFig12: Results of Human Emotion Sensing SystemA.Correspondence between facial expressions andelements of the Hidden Markov Model. Correspondence between facial expressions and elements of the Hidden Markov Model is shown in Table II.Table II Correspondence between facial expressions and elements ofthe Hidden Markov Model.parison of modeling facial expressions withmodeling speech using HMMs.Comparison of modeling facial expressions with modeling speech using HMMs has been shown in Table III. Table III. Comparison of modelling facial expressions with modellingspeech using HMMs.XIII.C ONCLUSIONIn this paper we have analyzed the limitations of existing system Emotion recognition using brain activity. In 'Emotion recognition using brain activity' brain activities using EEG signals has been used which is toughest task to do as it become expensive, complex and also time consuming when we try to measure human brain with Electroencephalography (eeg).Even when they have used existing data their result of analysis were 31 to 81 percentages correct and from which even by using Fuzzy logic 72 to 81 percentages only for two classes of emotions were achieved. This paper also gives us idea that we can sense human emotions also by reading and comparing the faces with images or data which is stored in knowledge base. In this paper by using a system which is trained by neural networks we have achieved up to 97 percent accurate results.XIV.F UTURE W ORKDue to lack of experience in this research field I have spent my most of time to understand basic concepts and existing techniques than building my own technique. However we have seen that we have got good results by using a simulator which uses neural network. But still this simulator has some limitations like at a time it will give results in one answer like Yes/No so for future work I will try to add Fuzzy logic membership function in it as one input may belong to different areas than only to a single one.In future work I will also try to add the application of Gabor filter based feature extraction in combination with neural network for the recognition of different facial emotions with more accuracy and better performance.XV.R EFERENCES[1]L. Franco, Alessandro Treves ―A Neural NetworkFacial Expression Recognition System using Unsupervised‖ Local Processing, Cognitive Neuroscience Sector – SISSA.[2] C. L. Lisetti, D. E. Rumelhart ―Facial ExpressionRecognition Using a Neural Network‖ In Proceedings of the 11 th International FLAIRS Conference, pp. 328—332, 2004.[3] C. Busso, Z. Deng , S. Yildirim , M. Bulut , C.M.Lee, A. Kazemzadeh , S.B. Lee, U. Neumann , S.Narayanan Analysis of Emotion Recognition using Facial Expression , Speech and Multimodal Information, ICMI‘04, PY, USA, 2004[4]Claude C. Chibelushi, Fabrice Bourel ―FacialExpression Recognition: A Brief Tutorial Overview‖.[5] C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I.Fotiadis, Toward Emotion Recognition in Car-Racing Drivers:A Biosignal Processing Approach, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol.38, No. 3, 2008.[6]ANDREASSI, J. L. (2000): ―Psychophysiology:human behavior and physiological response‖(Lawrence Erlbaum Associates, New Jersey, 2000) [7]ARK, W., DRYER, D. C., and LU, D. J. (1999):―The emotion mouse‖. 8th Int. Conf. Human-computer Interaction, pp. 818–823.[8]ARKIN, R. C., FUJITA, M., TAKAGI, T., andHASEGAWA, R. (2001): ―Ethological modeling and architec ture for an entertainment robot‖. IEEE Int. Conf. Robotics & Automation, pp. 453–458 [9]BERGER, R. D., AKSELROD, S., GORDON, D.,and COHEN, R. J. (1986): ―An efficient algorithm for spectral analysis of heart rate variability‖, IEEE Trans. Biomed. Eng., 33, pp. 900–904[10]BING-HWANG, J., and FURUI, S. (2000):‗Automatic recognition and understanding of spoken language—a first step toward natural human-machine communication‘, Proc. IEEE, 88, pp. 1142–1165.[11]BOUCSEIN, W. (1992): ―Electrodermal activity‘(Plenum Press, New York, 1992) BROERSEN, P.M. T. (2000a): ‗Facts and fiction in spectral analysis‖, IEEE Trans. Instrum. Meas., 49, pp.766–772.Dilbag Singh is a student ofGuru Nanak Dev University,Amritsar Punjab India inDepartment of ComputerScience and Engineering. Hehas completed his masterdegrees in computer science in2010 at Guru Nanak DevUniversity, Amritsar Punjab.Now he is going to complete hisM. tech in June 2012. His research interests include Parallel computing, software structure, embedded system, object detection and identification, and location sensing and tracking.。
多层次分类的情感状态分析与预测方法研究随着互联网技术的高速发展，大量的数据被产生和积累。
这些数据包含了人们的语言信息，其中蕴含各种情感信息。
对于个别人或特定群体，在各种社交、商业或科技平台上，表达言辞的方式也各不相同。
因此，如何对这些数据进行情感状态分析和预测便成为了亟待解决的问题。
本文将探讨多层次分类的情感状态分析与预测方法，介绍相关研究成果，并展望其未来的发展趋势。
1. 情感状态分析的基本概念情感状态分析（sentiment analysis），又称观点挖掘（opinion mining）或情感识别（emotion recognition），是指通过自然语言处理和机器学习等方法来捕捉和分析人类的情感状态，包括喜怒哀乐等情感。
而情感状态的判定，通常是以情感词及其相关上下文语句为基础，进而针对情感的语义特征进行深入分析。
情感状态分析这一领域的研究核心在于如何解析各种自然语言数据中的情感语义，即如何利用计算机自动化地分析和判断情感状态。
现代情感分析技术根据可获取的数据和分析的目的，可划分为基于规则的情感分析、基于情感词典的情感分析、基于机器学习的情感分析和基于深度学习的情感分析等几大类别。
其中，基于深度学习的情感分析发展迅猛，以其高准确率和强泛化能力风靡于学术界和工业界。
2. 多层次分类的情感状态分析方法传统情感分析方法多为二分类，目的是判断某个语句属于正面情感或负面情感。
但是在现实中，语句的情感状态是多样且复杂的，仅把情感分为正负两类，无法更全面地分析文本情感的内涵。
为此，研究者建立了多层次分类的情感状态分析模型来解决语句多样性带来的挑战。
多层次分类的情感状态分析方法主要分为两种：自底向上的针对子情感分类方法和自顶向下的针对总体情感分类方法。
自底向上的方法逐层分析句子中的情感，将每个句子的情感按照其级别从低到高分成不同层次。
而自顶向下的方法则将情感状态分为几个大类别，然后逐层细分，形成多层级的分类体系。
心 理 科 学 进 展 第30卷附录：图A1 问题解决能力测试例题(Novak, 1961)Ad vanc esi nPsych o logi c alScien ce. All Rights Reserved.第3期 刘耀辉 等: 基于过程数据的问题解决能力测量及数据分析方法图A2 植物大战僵尸游戏截屏(Zhao et al., 2015)图A3 能力模型和一些行为指标之间的联系(Zhao et al., 2015)Ad vanc esi nPsych o logi c alScien ce. All Rights Reserved.心 理 科 学 进 展 第30卷图A4 PISA 2012问题解决测试例题Ad vanc esi nPsych o logi c alScien ce. All Rights Reserved.心理科学进展 2022, Vol. 30, No. 3, 536–555 © 2022 中国科学院心理研究所 Advances in Psychological Science https:///10.3724/SP.J.1042.2022.00536536 ·元分析(Meta-Analysis)·不同情绪载体的神经活动及其异同——脑成像研究的ALE 元分析*刘俊材1 冉光明1 张 琪2(1西华师范大学教育学院心理系; 2西华师范大学学前与初等教育学院, 四川 南充 637002)摘 要 情绪识别一直是学界关注的热点。
虽然已有研究探讨了动态面孔表情、动态身体表情和声音情绪的脑机制, 但对每种情绪载体的整体认识相对不完善, 同时对不同情绪载体之间神经机制的共性和区别知之甚少。
因此, 本研究首先通过三项独立的激活似然估计元分析来识别每种情绪模式的大脑激活区域, 然后进行对比分析以评估三种情绪载体之间共同的和独特的神经活动。
结果显示, 动态面孔表情的大脑活动包括广泛的额叶、枕叶、颞叶和部分顶叶皮层以及海马、小脑、丘脑、杏仁核等皮层下区域; 动态身体表情的激活集中于颞/枕叶相关脑区以及小脑和海马; 声音情绪则引起了颞叶、额叶、杏仁核、尾状核和脑岛的激活。
人脑认知与情感的研究与分析人类是万物之灵，拥有独特的思维和情感能力。
这种能力来源于人脑对世界的认知和理解。
人脑的认知和情感机制是复杂而微妙的，它们之间存在着密切的关联和互动。
因此，研究人脑认知与情感的相互作用，对于更好地理解人类行为和思维过程具有重要意义。
一、人脑的认知机制人脑的认知机制是指对周围环境的感知、处理、理解和记忆等过程。
这些过程通过神经元之间的复杂相互作用来完成。
人类的感官系统（例如视觉、听觉、触觉、味觉等）负责将外界信息传达给大脑。
一旦信息被感知，它就会在大脑中被处理和理解。
我们的认知过程在很大程度上受到与周围环境的交互影响，这一过程涉及记忆，学习和意识。
人脑认知机制的研究，一直是神经科学的重要领域。
科学家通过在大脑的特定区域使用功能性核磁共振成像技术，对认知过程的神经机制进行了深入研究。
他们发现，大脑的不同区域在处理感知信息时起不同的作用。
例如，视觉皮层（視覺皮層）负责处理视觉信息，听觉皮层（聽覺皮層）则负责处理听觉信息。
此外，还有一些核心区域，如顶叶的颞、额区域，它们在记忆、意识和情感加工等方面起着重要作用。
大脑有许多神经元和神经递质，这些神经元形成复杂的网络，支持快速而高效的信息处理。
神经递质如多巴胺、血清素、去甲肾上腺素和γ-氨基丁酸（GABA）是重要的神经调节者，在情感加工中也起着至关重要的作用。
二、情感对人脑层次的影响情感是指人类在面临刺激时所体验的感受和反应。
情感与认知密不可分，二者互相作用。
情感可以影响我们的决策和行为，并且往往会对我们的认知产生深刻影响。
例如，当我们感到愉悦时，便会更愿意与他人沟通，并更容易记住他人说的话。
相反，负面情绪，如压力、恐惧和抑郁，会损害我们的决策和记忆能力。
情感的表达和处理受到大脑的多个区域的影响。
这些区域包括扣带回皮层、垂体、下丘脑、杏仁核和海马等。
研究发现，杏仁核是情感加工的最初阶段，它是记忆加工中情感标记的重要中继站。
而前额叶皮层（前額葉皮層）则负责情感调节。
