Neutral Emotions Research Articles

Facial emotion-recognition deficits in patients with schizophrenia and unaffected first-degree relatives.

This study aimed to determine trait- and state-dependent markers of schizophrenia by investigating facial emotion-recognition (FER) deficits in remitted patients with schizophrenia and their first-degree relatives (FR). Three groups were included: the Schizophrenia group (n=66), their unaffected FR group (n=40), and healthy controls (n=50) who were matched for age, sex, and years of education. A facial-labeling task was used to examine FER deficits using the following eight standardized expressions: happy, fearful, disgusted, angry, sad, contemptuous, surprised, and neutral. There was a poorer accuracy in the recognition of sadness and anger in the Schizophrenia group as well as in contempt in both the Schizophrenia and FR groups compared with healthy controls. The response times for the recognition of contempt, sadness, and neutral emotion were delayed in the Schizophrenia group and those for fear were delayed in the Schizophrenia and FR groups compared with healthy controls. Concerning the accuracy in FER, sadness and anger can be considered state-dependent markers of remitted schizophrenia, and contempt is a trait-dependent marker of schizophrenia. Similarly, for response times in FER, contempt, sadness, and neutral emotion can be considered state-dependent markers of remitted schizophrenia, while fear is considered a trait-dependent marker of schizophrenia. These findings may contribute to the early diagnosis of schizophrenia and the development of relevant therapeutic interventions.

Independence Threat or Interdependence Threat? The Focusing Effect on Social or Physical Threat Modulates Brain Activity.

The neural basis of threat perception has mostly been examined separately for social or physical threats. However, most of the threats encountered in everyday life are complex. The features of interactions between social and physiological threats under different attentional conditions are unclear. The present study explores this issue using an attention-guided paradigm based on ERP techniques. The screen displays social threats (face threats) and physical threats (action threats), instructing participants to concentrate on only one type of threat, thereby exploring brain activation characteristics. It was found that action threats did not affect the processing of face threats in the face-attention condition, and electrophysiological evidence from the brain suggests a comparable situation to that when processing face threats alone, with higher amplitudes of the N170 and EPN (Early Posterior Negativity) components of anger than neutral emotions. However, when focusing on the action-attention condition, the brain was affected by face threats, as evidenced by a greater N190 elicited by stimuli containing threatening emotions, regardless of whether the action was threatening or not. This trend was also reflected in EPN. The current study reveals important similarities and differences between physical and social threats, suggesting that the brain has a greater processing advantage for social threats.

Do incidental positive emotions induce more optimistic expectations of decision outcomes? An empirical study from the perspective of event-related potential.

Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision-making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision-making through a money gambling task using a one-way (emotion type: positive, negative, neutral emotions) between-subjects experimental design. Individuals with positive emotions had significantly greater risk-seeking rates than those with negative emotions during the decision selection phase (p<.01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event-related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback-related negativity component). Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations.

Эмотивность и ее проявление в речи русских и американских тревел-видеоблогеров

The relevance of the work is due to the interest of the scientific linguistic community in the problem of the manifestation of emotivity among representatives of different nationalities during the creation of travel video blogs. The main purpose of this work is to diagnose emotional characteristics and to make a comparative analysis of the speech manifestation of emotions in Russian and American travel video bloggers. This article presents an experiment that is carried out within the framework of the modified content analysis method in the aspect of hidden pragmalinguistics. During the experiment, some categories of emotions of travel video bloggers were discovered, which were identified based on the frequency of actualization in their texts of some plans of speech strategies of hidden influence. Positive and negative emotions were found among the emotions of Russian travel video bloggers, such as joy, enthusiasm, enthusiasm, indignation, resentment, anxiety. American travel video bloggers expressed positive and neutral emotions in their videos. The following emotions were revealed: satisfaction, inspiration, delight. At the same time, there was a manifestation of confidence. Based on the data obtained, a comparative analysis was conducted, during which it turned out that Russian video bloggers had a gender division in the process of analyzing emotive features, which manifested itself in the fact that men are prone to negative emotivity, whereas women are usually positive. American travel video bloggers did not show any negative manifestations of emotions.

Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots

Emotions, long deemed a distinctly human characteristic, guide a repertoire of behaviors, e.g., promoting risk-aversion under negative emotional states or generosity under positive ones. The question of whether Artificial Intelligence (AI) can possess emotions remains elusive, chiefly due to the absence of an operationalized consensus on what constitutes 'emotion' within AI. Adopting a pragmatic approach, this study investigated the response patterns of AI chatbots—specifically, large language models (LLMs)—to various emotional primes. We engaged AI chatbots as one would human participants, presenting scenarios designed to elicit positive, negative, or neutral emotional states. Multiple accounts of OpenAI's ChatGPT Plus were then tasked with responding to inquiries concerning investment decisions and prosocial behaviors. Our analysis revealed that ChatGPT-4 bots, when primed with positive, negative, or neutral emotions, exhibited distinct response patterns in both risk-taking and prosocial decisions, a phenomenon less evident in the ChatGPT-3.5 iterations. This observation suggests an enhanced capacity for modulating responses based on emotional cues in more advanced LLMs. While these findings do not suggest the presence of emotions in AI, they underline the feasibility of swaying AI responses by leveraging emotional indicators.

Abnormal ambiguous facial expression recognition in Chinese patients with schizophrenia

BackgroundPatients with schizophrenia (SCZ) exhibit difficulties deficits in recognizing facial expressions with unambiguous valence. However, only a limited number of studies have examined how these patients fare in interpreting facial expressions with ambiguous valence (for example, surprise). Thus, we aimed to explore the influence of emotional background information on the recognition of ambiguous facial expressions in SCZ.MethodsA 3 (emotion: negative, neutral, and positive) × 2 (group: healthy controls and SCZ) experimental design was adopted in the present study. The experimental materials consisted of 36 images of negative emotions, 36 images of neutral emotions, 36 images of positive emotions, and 36 images of surprised facial expressions. In each trial, a briefly presented surprised face was preceded by an affective image. Participants (36 SCZ and 36 healthy controls (HC)) were required to rate their emotional experience induced by the surprised facial expressions. Participants’ emotional experience was measured using the 9-point rating scale. The experimental data have been analyzed by conducting analyses of variances (ANOVAs) and correlation analysis.ResultsFirst, the SCZ group reported a more positive emotional experience under the positive cued condition compared to the negative cued condition. Meanwhile, the HC group reported the strongest positive emotional experience in the positive cued condition, a moderate experience in the neutral cued condition, and the weakest in the negative cue condition. Second, the SCZ (vs. HC) group showed longer reaction times (RTs) for recognizing surprised facial expressions. The severity of schizophrenia symptoms in the SCZ group was negatively correlated with their rating scores for emotional experience under neutral and positive cued condition.ConclusionsRecognition of surprised facial expressions was influenced by background information in both SCZ and HC, and the negative symptoms in SCZ. The present study indicates that the role of background information should be fully considered when examining the ability of SCZ to recognize ambiguous facial expressions.

Insightsphere: Social Analytics Command Center

The Social Media Command Center is a complex program designed to monitor and analyze user activity on various social media platforms. The innovative tool includes sentiment analysis to measure user sentiment and sentiment. One of its main features is the ability to disable unwanted features, allowing you to access content and communications. The system uses sentiment analysis to identify and correct harmful or counterproductive user behavior to create a better and more manageable online environment. This allows the system to distinguish between positive, negative, and neutral emotions. The plug-in feature is a special feature of the Command Center that allows administrators to disable or block user actions based on arbitrary diagnostic results. For example, if the analysis finds that there is no offensive or harmful content, the system can automatically take steps to reduce the impact by removing or reducing the scope of that content. Command Center also offers dashboards and reports that provide deep insights into social media trends, user behavior, and sentiment patterns. This helps organizations make decisions, improve community management, and maintain a better online environment.

Public emotions and visual perception of the East Coast Park in Singapore: A deep learning method using social media data

Social media data mining has become a prevailing approach to understanding people’s emotional response to, and perception of, public spaces. Drawing on Google Maps Reviews’ text reviews and shared images, this study develops a deep learning framework that combines Transformer BERT and CNN-VGG models to explore public emotions and visual perception of the East Coast Park (ECP) in Singapore. The results show that (1) public emotions of the ECP are predominantly joy, with minor emotional fluctuations in 2020 due to the pandemic lockdown; (2) CNN-VGG image classification is an effective tool to capture people’s visual preferences of public spaces, (3) public emotions across 12 specific public spaces are predominantly joy and neutral, but the results of people’s visual preference exhibited considerable diversity, and (4) image data is mostly associated with joy or neutral emotions, generally indicating users’ satisfaction of ECP public space, and for this reason, there is a lack of sound base to identify environmental factors associated with people’s negative emotions. This study shows that the proposed deep learning framework is effective in using social media data to understand the public's emotional response to and visual preference of urban public spaces.

Sentiment analysis model for cryptocurrency tweets using different deep learning techniques

Abstract Bitcoin (BTC) is one of the most important cryptocurrencies widely used in various financial and commercial transactions due to the fluctuations in the price of this currency. Recent research in large data analytics and natural language processing has resulted in the development of automated techniques for assessing the sentiment in online communities, which has emerged as a crucial platform for users to express their thoughts and comments. Twitter, one of the most well-known social media platforms, provides many tweets about the BTC cryptocurrency. With this knowledge, we can apply deep learning (DL) to use these data to predict BTC price variations. The researchers are interested in studying and analyzing the reasons contributing to the BTC price’s erratic movement by analyzing Twitter sentiment. The main problem in this article is that no standard model with high accuracy can be relied upon in analyzing textual emotions, as it represents one of the factors affecting the rise and fall in the price of cryptocurrencies. This article aims to classify the sentiments of an expression into positive, negative, or neutral emotions. The methods that have been used are word embedding FastText model in addition to different DL methods that deal with time series, one-dimensional convolutional neural networks (CONV1D), long-short-term memory networks (LSTMs), recurrent neural networks, gated recurrent units, and a Bi-LSTM + CONV1D The main results revealed that the LSTM method, based on the DL technique, achieved the best results. The performance accuracy of the methods was 95.01, 95.95, 80.59, 95.82, and 95.67%, respectively. Thus, we conclude that the LSTM method achieved better results than other methods in analyzing the textual sentiment of BTC.

Picture me in person: Personalization and emotionalization as political campaign strategies on social media in the German federal election period 2021

Due to the possibilities of direct communication with voters, politicians successfully use social media for personalization and emotionalization in election campaigns. However, since much of the research is based on text-centered analyses of individual platforms, we examine multimodal strategies of personalization and emotionalization of political candidates across platforms. Through a qualitative content and picture type analysis ( n = 401) of Facebook and Instagram posts, we identify seven multimodal personalization strategies in Study 1. We find that politicians use the two platforms differently; on Instagram, politicians present themselves more privately, whereas on Facebook, a more formal personalization dominates. In Study 2 ( n = 159), we use automated content analytical methods to examine the emotional expressions of candidates within their personalized posts. While positive or neutral emotions dominate the candidates’ self-representation on social media, differences between male and female candidates become apparent: Female candidates show significantly more happy faces than their male counterparts.

A multimodal emotion recognition system using deep convolution neural networks

Despite the progress in computer-technology, with regard to Human-Computer Interaction (HCI), emotion recognition is still a challenging problem. In this paper, we present a novel multimodal emotion recognition system capable of recognizing emotions from audio, video, and text data using deep convolution neural networks. The system is able to recognize happy, angry, sad, afraid, disgust, surprise and neutral emotions. We used three datasets to train and test the system, one set for each of the three input formats. The results show a recognition accuracy rate of 100% for audio, 69% for video, and 64% for text. When applying the decision-level fusion, the recorded accuracy rate is 80%. These results confirm that the system is effective in recognizing human emotions.

The Effects of Stress and Chatbot Services Usage on Customer Intention for Purchase on E-commerce Sites

In the rapidly evolving digital marketplace, customer service has become a critical factor influencing consumer behaviour. With the advent of Artificial Intelligence (AI), particularly chatbots, customer service companies are increasingly leveraging technology to enhance user experience. This study explores the relationship between customer emotions, detected during interactions with e-commerce chatbots, and their subsequent purchase intentions. Emotion detection within Human-Computer Interaction (HCI) is a vital area of research, as specific emotions, such as joy or frustration, can significantly impact marketing effectiveness and consumer decision-making. This research aims to understand how emotional responses to chatbot interactions can predict customer&amp;apos;s intention to purchase, thereby offering insights for businesses to optimize their AI-driven customer service strategies. The study analyzes four diverse datasets – EmotionLines, CARER, GoEmotion, and EmotionPush – to identify emotion-labelled sentences indicative of purchase intention. Our findings reveal that Neutral and Joyful emotions are predominant in influencing customers&amp;apos; purchase intentions, highlighting the importance of understanding these emotional states in e-commerce settings. While Neutral emotion is most influential, Joy consistently plays a significant role in positive customer engagement. This research underscores the need for e-commerce businesses to focus on emotional intelligence in chatbots, enhancing customer experience and potentially driving sales. Future research directions include examining real chatbot-customer interactions to further understand the impact of AI-driven customer service on consumer emotions and behaviours.

Hierarchical-attention-based neural network for gait emotion recognition

Human gait is an emerging biometric feature and contains important information for long-distance emotion recognition. However, sadness and neutral emotions are easily misjudged during the recognition process, because the body posture of the two emotions is quite similar. Existing methods have difficulty in distinguishing these two emotions satisfactorily since they treat all action features equally without differentiating their contribution to recognition, or they ignore the motion information in gait that can express emotion. In this paper, we propose a novel hierarchical attention neural network, which can automatically learn the affective features contained in human motion and action, and effectively distinguish between sad and neutral emotions. The network consists of three modules: motion sentiment module (MSM), action sentiment module (ASM) and emotion classifier. Specifically, MSM is composed of a position encoder and a velocity encoder. It extracts affective features from motion information and helps to distinguish the sad and neutral emotions based on gait velocity. ASM consists of an action encoder that compresses the discriminative human actions into a latent space. Emotion classifier recognizes the emotion based on the outputs from MSM and ASM. Moreover, we present a feature preprocessing method to deal with the problem of imbalanced data categories. Experiments demonstrate that our approach enhances the discriminability of sad and neutral emotions and performs better than many state-of-the-art methods. In addition, ablation experiments further verify that both velocity and action features are important for the gait-based emotion recognition task.

An assessment of dynamic facial emotion recognition and theory of mind in children with ADHD: An eye-tracking study.

Deficits in social cognition in attention deficit hyperactivity disorder (ADHD) have been associated with difficulties in functioning. Since recognizing emotional facial expressions is essential for developing the perceptual components of the theory of mind (ToM), it is important to assess this relationship in children with ADHD. This study therefore compared the recognition of emotional stimuli and gaze patterns between children with ADHD and healthy children using eye-tracking with dynamic facial images. It also examined the relationship between facial emotion recognition accuracy, gaze patterns, ToM scores, and ADHD symptoms. Children with ADHD aged 8-13 (n = 47) and a control group (n = 38) completed a facial emotion recognition test, ToM tests, and the Conners' Parent Rating Scale. Participants' gaze patterns in response to dynamic facial emotion expressions were recorded using eye-tracking technology. Children with ADHD exhibited significantly lower accuracy in the recognition of the facial expressions of disgust and anger. The percentage fixation in the eye region was also significantly lower for happy, angry, sad, disgusted, and neutral emotions in the children with ADHD compared to the control group. No relationship was determined between the percentage of fixations on facial areas of interests and ADHD symptoms or ToM tests. This study provides evidence that children with ADHD experience deficits in visual attention to emotional cues. In addition, it suggests that facial emotion recognition deficits in children with ADHD represent a separate domain of social cognition that develops independently of ToM skills and core symptoms. Understanding and treating the social difficulties of individuals with ADHD may help improve their social functioning.

The effects of awe on interpersonal forgiveness: the mediating role of small-self.

Awe could increase prosocial behavior, but little is known about its effects on interpersonal forgiveness. This study aims to explore the potential impact of awe on interpersonal forgiveness and the underlying mechanism of this process, using a combination of questionnaires, economic game and computational modeling. In Study 1, we utilized Trait Awe Scale (TAS) and Forgiveness Trait Scale (FTS) to examine the association between trait awe and trait forgiveness. In Study 2, we employed pre-screened video to induce awe, happy and neutral emotions, then evaluated the effects of induced awe on small-self and interpersonal forgiveness in hypothetical interpersonal offensive situations (Study 2a) and two economic interaction situations (Study 2b). Results from Study 1 indicate that there is a positive correlation between trait awe and trait forgiveness. Study 2 reveal that awe can enhance interpersonal forgiveness in both interpersonal conflict situations and economic interaction situations, and this effect is mediated by the sense of small-self elicited by awe. Overall, these findings contribute to our understanding of the potential impact of awe on interpersonal forgiveness and provide valuable insights into the mechanisms through which awe may influence forgiveness. Further research in this area could help to elucidate the potential applications of awe-based interventions in promoting forgiveness and positive social interactions.

The Role of Coherent Robot Behavior and Embodiment in Emotion Perception and Recognition During Human-Robot Interaction: Experimental Study.

Social robots are becoming increasingly important as companions in our daily lives. Consequently, humans expect to interact with them using the same mental models applied to human-human interactions, including the use of cospeech gestures. Research efforts have been devoted to understanding users' needs and developing robot's behavioral models that can perceive the user state and properly plan a reaction. Despite the efforts made, some challenges regarding the effect of robot embodiment and behavior in the perception of emotions remain open. The aim of this study is dual. First, it aims to assess the role of the robot's cospeech gestures and embodiment in the user's perceived emotions in terms of valence (stimulus pleasantness), arousal (intensity of evoked emotion), and dominance (degree of control exerted by the stimulus). Second, it aims to evaluate the robot's accuracy in identifying positive, negative, and neutral emotions displayed by interacting humans using 3 supervised machine learning algorithms: support vector machine, random forest, and K-nearest neighbor. Pepper robot was used to elicit the 3 emotions in humans using a set of 60 images retrieved from a standardized database. In particular, 2 experimental conditions for emotion elicitation were performed with Pepper robot: with a static behavior or with a robot that expresses coherent (COH) cospeech behavior. Furthermore, to evaluate the role of the robot embodiment, the third elicitation was performed by asking the participant to interact with a PC, where a graphical interface showed the same images. Each participant was requested to undergo only 1 of the 3 experimental conditions. A total of 60 participants were recruited for this study, 20 for each experimental condition for a total of 3600 interactions. The results showed significant differences (P<.05) in valence, arousal, and dominance when stimulated with the Pepper robot behaving COH with respect to the PC condition, thus underlying the importance of the robot's nonverbal communication and embodiment. A higher valence score was obtained for the elicitation of the robot (COH and robot with static behavior) with respect to the PC. For emotion recognition, the K-nearest neighbor classifiers achieved the best accuracy results. In particular, the COH modality achieved the highest level of accuracy (0.97) when compared with the static behavior and PC elicitations (0.88 and 0.94, respectively). The results suggest that the use of multimodal communication channels, such as cospeech and visual channels, as in the COH modality, may improve the recognition accuracy of the user's emotional state and can reinforce the perceived emotion. Future studies should investigate the effect of age, culture, and cognitive profile on the emotion perception and recognition going beyond the limitation of this work.

Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis.

Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the dynamic features of different emotions, and four characteristic microstates were analyzed. In the alpha band, microstate A exhibited a higher occurrence in both negative and positive emotions than in neutral emotions. Microstate C exhibited a prolonged duration of negative emotions compared to positive emotions, and a higher occurrence was observed in both microstates C and D during positive emotions. Notably, a unique transition pair was observed between microstates B and C during positive emotions, whereas a unique transition pair was observed between microstates A and D during negative emotions. This study emphasizes the potential of integrating virtual reality (VR) and EEG to facilitate experimental design. Furthermore, this study enhances our comprehension of neural activities during various emotional states.

The control patterns of affective processing and cognitive reappraisal: insights from brain controllability analysis.

Perceiving and modulating emotions is vital for cognitive function and is often impaired in neuropsychiatric conditions. Current tools for evaluating emotional dysregulation suffer from subjectivity and lack of precision, especially when it comes to understanding emotion from a regulatory or control-based perspective. To address these limitations, this study leverages an advanced methodology known as functional brain controllability analysis. We simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data from 17 healthy subjects engaged in emotion processing and regulation tasks. We then employed a novel EEG/fMRI integration technique to reconstruct cortical activity in a high spatiotemporal resolution manner. Subsequently, we conducted functional brain controllability analysis to explore the neural network control patterns underlying different emotion conditions. Our findings demonstrated that the dorsolateral and ventrolateral prefrontal cortex exhibited increased controllability during the processing and regulation of negative emotions compared to processing of neutral emotion. Besides, the anterior cingulate cortex was notably more active in managing negative emotion than in either controlling neutral emotion or regulating negative emotion. Finally, the posterior parietal cortex emerged as a central network controller for the regulation of negative emotion. This study offers valuable insights into the cortical control mechanisms that support emotion perception and regulation.

Consumers' perceptions and acceptance of genome editing in agriculture: Insights from the United States of America and Switzerland

The terms “New Genomic Techniques” (NGTs) or “Genome Editing” refer to various methods that allow finding, cleaving, and repairing specific sequences in the genome. These techniques could contribute to managing various challenges in plant breeding and agriculture. Aside from regulatory uncertainties, the lack of consumer acceptance has frequently been cited as a significant barrier to the widespread use of NGTs in plant breeding and agriculture across the planet. This study was based on an anonymous online survey (N = 1202). It investigated what consumers from two countries that differ in gene technology regulation, namely the United States of America and Switzerland, thought about three specific applications of NGTs in plant breeding (i.e., blight-resistant potato, gluten-free wheat, cold-resistant soybean). The study highlights the importance of the affect heuristic for acceptance, as half of the participants in both countries expressed positive feelings regarding the three applications, a quarter of the participants expressed negative, and the remaining participants expressed torn or neutral emotions. Some evidence was provided that the regulatory context might have acted as a risk cue, as participants in Switzerland expressed more negative feelings, perceptions, and lower acceptance than participants from the United States of America. Lastly, our findings underscore the importance of a collaboration between the life sciences and social sciences in balancing technological innovations and public perceptions and acceptance, which have been shown in this study to be impacted by affect, values, and context.

Visual Event-Related Potentials under External Emotional Stimuli as Early Signs for Mild Cognitive Impairment.

Alzheimer's disease (AD) is a neurodegenerative disorder featured by progressive cognitive decline, which manifests in severe impairment of memory, attention, emotional processing and daily activities, leading to significant disability and social burden. Investigation on Mild Cognitive Impairment (MCI), the prodromal and transitional stage between normal aging and AD, serves as a key in diagnosing and slowing down the progression of AD. Numerous effects have been made up to date, however, the attentional mechanisms under different external emotion stimuli in MCI and AD are still unexplored in deep. To further explore the attentional mechanisms under different external emotion stimuli in both MCI and AD patients. In 51 healthy volunteers (Controls, 24 males and 27 females), 52 MCI (19 males and 33 females), and 47 AD (15 males and 32 females) patients, we administered the visual oddball event-related potentials (ERPs) under three types of external emotional stimuli: Neutral, Happiness and Sadness, in which the components N1, P2, N2 and P3 as well as the abnormal cortical activations corresponding to the significant ERP differences in the three groups were observed. Under all three external emotions, in AD patients, N2 and P3 latencies were significantly prolonged compared to both Controls and MCI. In addition, under Happiness, in MCI, P3 latencies were significantly delayed compared to Controls. Meanwhile, under both Happiness and Sadness, in AD patients, P3 amplitudes were significantly decreased compared to Controls and MCI, respectively. During N2 time window, under Neutral emotion, significant hypoactivation in the right superior temporal gyrus was found in AD patients compared to Controls, and under Happiness, the activation of the right inferior frontal gyrus was significantly attenuated in MCI compared to Controls. Under Sadness, in AD patients, the activation of the right superior frontal gyrus was significantly decreased compared to MCI. During P3 time window, under both Happiness and Sadness, when AD patients compared to MCI, the significantly attenuated activations were located in the right fusiform gyrus and the right middle occipital gyrus, respectively. Our results demonstrated visual attentional deficits under external emotional stimuli in both MCI and AD patients, highlighting the function of Happiness for early detecting MCI, in which the P3 latency and the hypoactivation of right inferior frontal gyrus during N2 time window can be early signs. The current study sheds further light of attentional mechanisms in MCI and AD patients, and indicates the value of emotional processing in the early detection of cognitive dysfunction.