Older adults are worse than young adults at perceiving emotions in others. However, it is unclear why these age-related differences in emotion perception exist. The studies presented in this thesis investigated the cognitive, emotional and motivational factors influencing age differences in emotion perception. Study 1 revealed no age differences in mood congruence effects: sad faces were rated as more sad when participants experienced negative mood. In contrast, Study 2 demonstrated that sad mood impaired recognition accuracy for sad faces. Together, findings suggested that different methods of assessing emotion perception engage the use of discrete processing strategies. These mood influences on emotion perception are similar in young and older adults. Studies 3 and 4 investigated age differences in emotion perception tasks which are more realistic and contextualised than still photographs of facial expressions. Older adults were worse than young at recognising emotions from silent dynamic displays; however, older adults outperformed young in a film task that displayed emotional information in multiple modalities (Study 3). Study 4 suggested that the provision of vocal information was particularly beneficial to older adults. Furthermore, vocabulary mediated the relationship between age and performance on the contextual film task. However, age-related deficits in decoding basic emotions were established in a separate multi-modal video-based task. In addition, age differences in the perception of neutral expressions were also examined. Neutral expressions were interpreted as displaying positive emotions by older adults. Using a dual-task paradigm, Study 5 suggested that working memory processes are involved in decoding emotions. However, age-related declines in working memory were not driving age effects in emotion perception. Neuropsychological, motivational and cognitive explanations for these results are evaluated. Implications of these findings for older adults' social functioning are discussed.

This study investigated the associations between emotion recognition ability and autistic traits in a sample of non-clinical young adults. Two hundred and forty nine individuals took part in an emotion recognition test, which assessed recognition of 12 emotions portrayed by actors. Emotion portrayals were presented as short video clips, both with and without sound, and as sound only. Autistic traits were assessed using the Autism Spectrum Quotient (ASQ) questionnaire. Results showed that men had higher ASQ scores than women, and some sex differences in emotion recognition were also observed. The main finding was that autistic traits were correlated with several measures of emotion recognition. More specifically, ASQ-scores were negatively correlated with recognition of fear and with recognition of ambiguous stimuli.

Emotion states greatly influence many areas in our daily lives, such as: learning, decision making and interaction with others. Therefore, the ability to detect and recognize one’s emotional states is essential in intelligence Human Machine Interaction (HMI). In this thesis, a pattern classification framework was developed to sense and communicate emo- tion changes expressed by the Central Nervous System (CNS) through the use of EEG signals. More specifically, an EEG-based subject-dependent affect recognition system was developed to quantitatively measure and categorize three affect states: Positively excited, neutral and negatively excited. Several existing feature extraction algorithms and classifiers were researched, analyzed and evaluated through a series of classification simulations using a publicly available emotion-based EEG database. Simulation results were presented followed by an interpretation discussion. The findings in this thesis can be useful for the design of affect sensitive applications such as augmented means of communication for severely disabled people that cannot directly express their emotions. Furthermore, we have shown that with significantly reduced number of channels, classification rates maintained a level that is feasible for emotion recognition. Thus current HMI paradigms to integrate consumer electronics such as smart hand-held device with commercially available EEG headsets is promising and will significantly broaden the application cases.

Emotion states greatly influence many areas in our daily lives, such as: learning, decision making and interaction with others. Therefore, the ability to detect and recognize one’s emotional states is essential in intelligence Human Machine Interaction (HMI). In this thesis, a pattern classification framework was developed to sense and communicate emo- tion changes expressed by the Central Nervous System (CNS) through the use of EEG signals. More specifically, an EEG-based subject-dependent affect recognition system was developed to quantitatively measure and categorize three affect states: Positively excited, neutral and negatively excited. Several existing feature extraction algorithms and classifiers were researched, analyzed and evaluated through a series of classification simulations using a publicly available emotion-based EEG database. Simulation results were presented followed by an interpretation discussion. The findings in this thesis can be useful for the design of affect sensitive applications such as augmented means of communication for severely disabled people that cannot directly express their emotions. Furthermore, we have shown that with significantly reduced number of channels, classification rates maintained a level that is feasible for emotion recognition. Thus current HMI paradigms to integrate consumer electronics such as smart hand-held device with commercially available EEG headsets is promising and will significantly broaden the application cases.

Emotion conveys the psychological state of a person. It is expressed by a variety of physiological changes, such as changes in blood pressure, heart beat rate, degree of sweating, and can be manifested in shaking, changes in skin coloration, facial expression, and the acoustics of speech. This research focuses on the recognition of emotion conveyed in speech. There were three main objectives of this study. One was to examine the role played by the glottal source signal in the expression of emotional speech. The second was to investigate whether it can provide improved robustness in real-world situations and in noisy environments. This was achieved through testing in clear and various noisy conditions. Finally, the performance of glottal features was compared to diverse existing and newly introduced emotional feature domains. A novel glottal symmetry feature is proposed and automatically extracted from speech. The effectiveness of several inverse filtering methods in extracting the glottal signal from speech has been examined. Other than the glottal symmetry, two additional feature classes were tested for emotion recognition domains. They are the: Tonal and Break Indices (ToBI) of American English intonation, and Mel Frequency Cepstral Coefficients (MFCC) of the glottal signal. Three corpora were specifically designed for the task. The first two investigated the four emotions: Happy, Angry, Sad, and Neutral, and the third added Fear and Surprise in a six emotions recognition task. This work shows that the glottal signal carries valuable emotional information and using it for emotion recognition has many advantages over other conventional methods. For clean speech, in a four emotion recognition task using classical prosodic features achieved 89.67% recognition, ToBI combined with classical features, reached 84.75% recognition, while using glottal symmetry alone achieved 98.74%. For a six emotions task these three methods achieved 79.62%, 90.39% and 85.37% recognition rates, respectively. Using the glottal signal also provided greater classifier robustness under noisy conditions and distortion caused by low pass filtering. Specifically, for additive white Gaussian noise at SNR = 10 dB in the six emotion task the classical features and the classical with ToBI both failed to provide successful results; speech MFCC's achieved a recognition rate of 41.43% and glottal symmetry reached 59.29%. This work has shown that the glottal signal, and the glottal symmetry in particular, provides high class separation for both the four and six emotion cases. It is confidently surpassing the performance of all other features included in this investigation in noisy speech conditions and in most clean signal conditions.

The effective encoding and interpretation of facial expressions is critical to inferring the intentions, motivation, and emotional state of others. Asperger syndrome (AS) is a pervasive, neurodevelopmental condition characterized by significant deficits in social interaction, impaired use of language, and stereotyped interests and activities. Deficient encoding and interpretation of facial expressions is likely related to the social difficulties experienced by those with AS. A video-based intervention administered via Internet is proposed for young children with AS. This research hopes to clarify the questions (1) are young children with AS able to interpret simple emotions and (2) can they learn the skills necessary to interpret complex emotions. Data will be analyzed using multivariate analysis of covariance. / text

Studies examining impaired emotion recognition and perceptual processing in autism spectrum disorders (ASD) show inconsistent results (Harms, Martin, & Wallace, 2010; Jemel, Mottron, & Dawson, 2006), and many of these studies include eye tracking data. The current study utilizes a novel task, emotion recognition of a dynamic talking face with sound, to compare children with ASD (n=8; aged 6-10, 7 male) with mental age (MA) and gender matched controls (n=8; aged 4-10, 7 male) on an emotion identification and eye tracking task. Children were asked to watch several short video clips (2.5-5 seconds) portraying the emotions of happy, sad, excited, scared, and angry and identify the emotion portrayed in the video. A mixed factorial ANOVA analysis was conducted to examine group differences in attention when viewing the stimuli. Differences in emotion identification ability were examined using a t-test and Fisher's exact tests of independence. Findings indicated that children with ASD spent less time looking at faces and the mouth region than controls. Additionally, the amount of time children with ASD spent looking at the mouth region predicted better performance on the emotion identification task. The study was underpowered; however, so these results were preliminary and require replication. Results are discussed in relation to natural processing of emotion and social stimuli. <i>[revised ETD per Dean DePauw 10/25/12 GMc]</i> / Master of Science

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When people interact with each other, they not only listen to what the other says, they react to facial expressions, gaze direction, and head movement. Human-computer interaction would be enhanced in a friendly and non-intrusive way if computers could understand and respond to users’ body language in the same way. This thesis aims to investigate new methods for human computer interaction by combining information from the body language of the head to recognize the emotional and cognitive states. We concentrated on the integration of facial expression, eye gaze and head movement using soft computing techniques. The whole procedure is done in two-stage. The first stage focuses on the extraction of explicit information from the modalities of facial expression, head movement, and eye gaze. In the second stage, all these information are fused by soft computing techniques to infer the implicit emotional states. In this thesis, the frequency of head movement (high frequency movement or low frequency movement) is taken into consideration as well as head nods and head shakes. A very high frequency head movement may show much more arousal and active property than the low frequency head movement which differs on the emotion dimensional space. The head movement frequency is acquired by analyzing the tracking results of the coordinates from the detected nostril points. Eye gaze also plays an important role in emotion detection. An eye gaze detector was proposed to analyze whether the subject's gaze direction was direct or averted. We proposed a geometrical relationship of human organs between nostrils and two pupils to achieve this task. Four parameters are defined according to the changes in angles and the changes in the proportion of length of the four feature points to distinguish avert gaze from direct gaze. The sum of these parameters is considered as an evaluation parameter that can be analyzed to quantify gaze level. The multimodal fusion is done by hybridizing the decision level fusion and the soft computing techniques for classification. This could avoid the disadvantages of the decision level fusion technique, while retaining its advantages of adaptation and flexibility. We introduced fuzzification strategies which can successfully quantify the extracted parameters of each modality into a fuzzified value between 0 and 1. These fuzzified values are the inputs for the fuzzy inference systems which map the fuzzy values into emotional states.

Individuals from Aberdeen/Aberdeenshire and Glasgow/Greater Glasgow have anecdotally been thought to differ in their expression of emotion with the former group being thought to be less emotionally expressive that the latter. The current thesis carried out three studies to empirically examine this. A systematic review of measures of emotion experience, regulation, expression and alexithymia was carried out to establish their psychometric properties. The results of the review lead to recommendations for which scales to use within future studies of the thesis. The second study used measures of emotion experience (Positive Affect Negative Affect Schedule), emotion regulation (Emotion Regulation Questionnaire) and alexithymia (Toronto Alexithymia Scale-20), identified within the review, in samples of adults from Aberdeen/Aberdeenshire and Glasgow/Greater Glasgow. A multiple indicators multiple causes model was used to examine group differences in response to these measures, this method allowed examination of differences on factor means and individual indicator items on the scales. It was found that Aberdeen/Aberdeenshire participants demonstrated a higher factor mean on the Negative Affect (NA) factor of the PANAS; the Aberdeen/Aberdeenshire participants also endorsed an individual item on the ERQ (Item 5) and the TAS-20 (Item 1) more than the Glasgow/Greater Glasgow participants. Finally, a qualitative study was carried out in which participants from each group recalled events related to six emotions. In describing events related to fear, anger and sadness, Aberdeen/Aberdeenshire participants tended to use positive statements that downplayed events related to these emotions, while the Glasgow/Greater Glasgow participants tended to use 'catastrophic' statements when describing events related to the same emotions. This may indicate differing cultural models between these populations.