Social cognitive abilities are needed to process and understand social information in order to respond appropriately in everyday social interactions. While there are a number of tests that have been developed to measure social cognition in the literature, many have important limitations such as only assessing one ability, performance being predicted by measures of intelligence and exhibiting low ecological validity. To address some of these limitations, I developed a new test called the Edinburgh Social Cognition Test (ESCoT). The ESCoT is an animated test that assesses four domains of social cognition: cognitive Theory of Mind (ToM) (What is X thinking?); affective ToM (How does X feel at the end of the animation?); interpersonal understanding of social norms (Did X behave as other people should behave?); and intrapersonal understanding of social norms (Would you have acted the same as X in the animation?). The aims of this thesis were to examine the validity of the ESCoT as a test of social cognition and to further investigate social cognitive processes in healthy and neurological populations. The ESCoT was firstly administered to a healthy population of older, middle-aged and younger adults to examine the effects of ageing on social abilities. This study found that the ESCoT was sensitive to age; poorer performances on cognitive and affective ToM and also interpersonal but not intrapersonal understanding of social norms were predicted by older age. Furthermore unlike traditional tests used in the study, performance was not predicted by measures of intelligence. Instead, the sex of participants and autistic-like traits, in addition to age were found to be important for performance. The ESCoT was then validated in a sample of adults with Autism Spectrum Disorder (ASD), and performance was compared to performance on established social cognition tests. Convergent validity was demonstrated in the study and the ESCoT was sensitive to social cognitive difficulties found in ASD. This study also showed that the ESCoT was more effective than existing tests at differentiating ASD adults and neurotypical controls. The interplay of social anxiety and empathy on ESCoT performance in addition to further exploring sex and autistic-like traits were then examined in a younger adult population. Social anxiety and empathy were not significant predictors of performance on the ESCoT. Similar to the results of the ageing study, this study found that women were better than men on affective ToM. However, unlike the ageing study, better cognitive ToM performance was predicted by older age. Better performance on interpersonal understanding of social norms and ESCoT total scores were predicted by more years of education. The subsequent chapter then examined the clinical efficacy of the ESCoT in a patient population (Alzheimer's disease, behavioural-variant Frontotemporal dementia and amnestic mild cognitive impairment). Here performance on the ESCoT was compared between the patients and neurotypical controls. It was found that patients performed poorer than neurotypical controls on ESCoT total scores, affective ToM, inter-and intrapersonal understanding of social norms. The final chapter returned to healthy ageing to more closely investigate the consequences of healthy ageing on social cognitive processes, by examining the positivity bias (preference for positive over negative stimuli) found in older adults using an attention paradigm. There was no evidence of the positivity bias in older, middle-aged and younger adults in regards to reaction time or accuracy. However, older and middle-aged adults differed in accuracy across stimuli type compared to younger adults. This thesis offers novel insights into the social cognitive abilities of various populations. The ESCoT presents a new, informative and validated test of social cognition for researchers and clinicians to use, which has many advantages over established tests of social cognition.

This paper describes two experiments, each of which investigated the impact of stress on human episodic memory. All participants watched narrated slide shows containing emotional and neutral information. Experiment 1 demonstrated that pre-learning exposure to a psychological stressor (the Trier Social Stress Test or "TSST"; Kirschbaum, Pirke & Hellhammer, 1993) preserved or enhanced memory for emotional aspects of the slide show, but impaired memory for neutral aspects of the slide show. Moreover, stress exposure disrupted memory for information that was visually and thematically central to the slide show. Memory for peripheral information, on the other hand, was unaffected by stress. Experiment 2 replicated these results and extended them to a similar paradigm, where participants viewed separate emotional and neutral slide shows, and saliva was tested for the stress hormones cortisol and norepinephrine. Similar to the results of Experiment 1, stress disrupted memory for the neutral slide show, but enhanced memory for the emotional slide show. Salivary cortisol levels at retrieval were negatively correlated with memory for the neutral slide show. These results are consistent with theories invoking differential effects of stress on brain systems responsible for encoding and retrieving emotional memories (the amygdala) and non-emotional memories (e.g. the hippocampal formation, frontal cortex), and inconsistent with the view that memories formed under high levels of stress are qualitatively the same as those formed under ordinary emotional circumstances. These data, which are also consistent with results obtained in a number of studies using animals and humans, have implications for the traumatic memory debate and theories regarding human memory.

This study proposes a social and emotional disclosure model for understanding the mechanism that explains sharing intimate information on social media (Twitter). Previous research has indicated that some aspects of social, emotional, and attitudinal information processing are involved in disclosure of intimate information. However, these factors have been considered in isolation. This study proposes and tests a theoretically grounded model that brings all of these factors together by combining individual and group social media behaviors and online information processing in the realm of online social movements. The core explanatory model considers the impact of peer response, emotional evaluation, personal relevance, issue orientation, and motivation to post online on intimate information disclosure online. A path analysis building on four Poisson multiple regressions conducted on 28,629 #MeToo tweets evaluates the relationships proposed in the explanatory model. Results indicate that emotional evaluation and motivation to post online have direct, positive impacts on online disclosure. Other factors such as peer response, issue orientation, and personal relevance have negative direct relationships with online disclosure. Motivation to post online mediates the effects of emotional evaluation, issue orientation, and personal relevance on online disclosure while issue orientation mediates the effect of personal relevance on motivation to post online. This study offers findings that have use for practitioners interested in hashtag virality and to social media users interested in social influence and online information sharing.

Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans. Today, emotional systems such as software-only agents and embodied robots seem to improve every day at managing large volumes of information, and they remain emotionally incapable to read our feelings and react according to them. From a computational viewpoint, technology has made significant steps in determining how an emotional behavior model could be built; such a model is intended to be used for the purpose of intelligent assistance and support to humans. Human emotions are engines that allow people to generate useful responses to the current situation, taking into account the emotional states of others. Recovering the emotional cues emanating from the natural behavior of humans such as facial expressions and bodily kinetics could help to develop systems that allow recognition, interpretation, processing, simulation, and basing decisions on human emotions. Currently, there is a need to create emotional systems able to develop an emotional bond with users, reacting emotionally to encountered situations with the ability to help, assisting users to make their daily life easier. Handling emotions and their influence on decisions can improve the human-machine communication with a wider vision. The present thesis strives to provide an emotional architecture applicable for an agent, based on a group of decision-making models influenced by external emotional information provided by humans, acquired through a group of classification techniques from machine learning algorithms. The system can form positive bonds with the people it encounters when proceeding according to their emotional behavior. The agent embodied in the emotional architecture will interact with a user, facilitating their adoption in application areas such as caregiving to provide emotional support to the elderly. The agent's architecture uses an adversarial structure based on an Adversarial Risk Analysis framework with a decision analytic flavor that includes models forecasting a human's behavior and their impact on the surrounding environment. The agent perceives its environment and the actions performed by an individual, which constitute the resources needed to execute the agent's decision during the interaction. The agent's decision that is carried out from the adversarial structure is also affected by the information of emotional states provided by a classifiers-ensemble system, giving rise to a "decision with emotional connotation" included in the group of affective decisions. The performance of different well-known classifiers was compared in order to select the best result and build the ensemble system, based on feature selection methods that were introduced to predict the emotion. These methods are based on facial expression, bodily gestures, and speech, with satisfactory accuracy long before the final system. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.</p>