EEG Based Emotion Identification Using Unsupervised Deep Feature Learning

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition

Affect-based information retrieval

One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap

Affective Signals as Implicit Indicators of Information Relevancy and Information Processing Strategies

Search engines have become better in providing information to users, however, they still face major challenges such as determining how searchers process information, how they make relevance judgments, and how their cognitive or emotional state affect their search progress. We address these challenges by exploring searchers' affective dimension. In particular, we investigate how feelings, facial expressions, and electrodermal activity (EDA) could help to understand information relevancy, search progress, and information processing strategies (IPSs). To meet this goal, we designed an experiment with 45 participants exposed to affective stimuli prior solving a precision-oriented search task. Results indicate that initial affective states are linked to IPSs. In addition, we found that smiles act as implicit indicators of information relevancy and IPSs. Moreover, results convey that both smiles and EDA may serve as implicit indicators of progress and completion of search tasks. Findings from this work have practical implications in areas such as personalization and relevance feedback.ye

Brief mindfulness training enhances cognitive control in socioemotional contexts: Behavioral and neural evidence.

In social contexts, the dynamic nature of others' emotions places unique demands on attention and emotion regulation. Mindfulness, characterized by heightened and receptive moment-to-moment attending, may be well-suited to meet these demands. In particular, mindfulness may support more effective cognitive control in social situations via efficient deployment of top-down attention. To test this, a randomized controlled study examined effects of mindfulness training (MT) on behavioral and neural (event-related potentials [ERPs]) responses during an emotional go/no-go task that tested cognitive control in the context of emotional facial expressions that tend to elicit approach or avoidance behavior. Participants (N = 66) were randomly assigned to four brief (20 min) MT sessions or to structurally equivalent book learning control sessions. Relative to the control group, MT led to improved discrimination of facial expressions, as indexed by d-prime, as well as more efficient cognitive control, as indexed by response time and accuracy, and particularly for those evidencing poorer discrimination and cognitive control at baseline. MT also produced better conflict monitoring of behavioral goal-prepotent response tendencies, as indexed by larger No-Go N200 ERP amplitudes, and particularly so for those with smaller No-Go amplitude at baseline. Overall, findings are consistent with MT's potential to enhance deployment of early top-down attention to better meet the unique cognitive and emotional demands of socioemotional contexts, particularly for those with greater opportunity for change. Findings also suggest that early top-down attention deployment could be a cognitive mechanism correspondent to the present-oriented attention commonly used to explain regulatory benefits of mindfulness more broadly

What is Strategic Competence and Does it Matter? Exposition of the Concept and a Research Agenda

Drawing on a range of theoretical and empirical insights from strategic management and the cognitive and organizational sciences, we argue that strategic competence constitutes the ability of organizations and the individuals who operate within them to work within their cognitive limitations in such a way that they are able to maintain an appropriate level of responsiveness to the contingencies confronting them. Using the language of the resource based view of the firm, we argue that this meta-level competence represents a confluence of individual and organizational characteristics, suitably configured to enable the detection of those weak signals indicative of the need for change and to act accordingly, thereby minimising the dangers of cognitive bias and cognitive inertia. In an era of unprecedented informational burdens and instability, we argue that this competence is central to the longer-term survival and well being of the organization. We conclude with a consideration of the major scientific challenges that lie ahead, if the ideas contained within this paper are to be validated

Social interactions, emotion and sleep: a systematic review and research agenda

Sleep and emotion are closely linked, however the effects of sleep on socio-emotional task performance have only recently been investigated. Sleep loss and insomnia have been found to affect emotional reactivity and social functioning, although results, taken together, are somewhat contradictory. Here we review this advancing literature, aiming to 1) systematically review the relevant literature on sleep and socio-emotional functioning, with reference to the extant literature on emotion and social interactions, 2) summarize results and outline ways in which emotion, social interactions, and sleep may interact, and 3) suggest key limitations and future directions for this field. From the reviewed literature, sleep deprivation is associated with diminished emotional expressivity and impaired emotion recognition, and this has particular relevance for social interactions. Sleep deprivation also increases emotional reactivity; results which are most apparent with neuro-imaging studies investigating amygdala activity and its prefrontal regulation. Evidence of emotional dysregulation in insomnia and poor sleep has also been reported. In general, limitations of this literature include how performance measures are linked to self-reports, and how results are linked to socio-emotional functioning. We conclude by suggesting some possible future directions for this field