How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

Input and Intake in Language Acquisition

This dissertation presents an approach for a productive way forward in the study of language acquisition, sealing the rift between claims of an innate linguistic hypothesis space and powerful domain general statistical inference. This approach breaks language acquisition into its component parts, distinguishing the input in the environment from the intake encoded by the learner, and looking at how a statistical inference mechanism, coupled with a well defined linguistic hypothesis space could lead a learn to infer the native grammar of their native language. This work draws on experimental work, corpus analyses and computational models of Tsez, Norwegian and English children acquiring word meanings, word classes and syntax to highlight the need for an appropriate encoding of the linguistic input in order to solve any given problem in language acquisition

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Innocence and Guilt Detection in High-Stakes Television Appeals

The present thesis explored the cognitive and affective mechanisms underlying the cues used to make innocence-guilt decisions in the high-stakes situation of television appeals in which people appeal publicly for the return of a loved one. Two aspects of the processes involved in making judgements of veracity were studied. Part 2 examined the interaction between explicit and implicit judgments. The studies in Part 2 experimentally manipulated aspects of 12 appeals to test hypotheses about the heuristics judges used to determine veracity. The hypotheses in these studies were initially based on the central assumption that in the absence of unambiguous information judges will draw on heuristics to make veracity judgments. These cognitive shortcuts were hypothesised to lead to biases in innocence-guilt judgments. The innocence bias was introduced as a potential predisposition that stood to be tested. Results did not reveal a consistent innocence or guilt bias. Rather, while all four experiments in Part 2 indicated the presence of different underlying cognitive processes across all experimental conditions, the results from these studies would appear to challenge the existence of any intrinsic tendency towards biases. In Part 3, the context of the appeals was taken as the basis for the assumption that truthful people would give clearer indications of grief than ones who were lying. Multivariate cues were analysed simultaneously using 39 appeals, with a theoretical basis drawn from the grief literature. Eight previously unidentified aspects consisting of verbal cues drawn from grief literature are found to distinguish honest and deceptive appeals with high accuracy and reliability. The work thus contributed to the initial understanding of the interaction between explicit and implicit decisions in making innocence-guilt judgments. Standing models of cognitive processing and their implications for the present thesis were also discussed. Contingent upon further clarification of cognitive processes involved during innocence and guilt verdict decision-making, the findings are particularly germane to the area of televised press conferences and have implications for police and practice

An investigation into vocal expressions of emotions: the roles of valence, culture, and acoustic factors.

This PhD is an investigation of vocal expressions of emotions, mainly focusing on non-verbal sounds such as laughter, cries and sighs. The research examines the roles of categorical and dimensional factors, the contributions of a number of acoustic cues, and the influence of culture. A series of studies established that naive listeners can reliably identify non-verbal vocalisations of positive and negative emotions in forced-choice and rating tasks. Some evidence for underlying dimensions of arousal and valence is found, although each emotion had a discrete expression. The role of acoustic characteristics of the sounds is investigated experimentally and analytically. This work shows that the cues used to identify different emotions vary, although pitch and pitch variation play a central role. The cues used to identify emotions in non-verbal vocalisations differ from the cues used when comprehending speech. An additional set of studies using stimuli consisting of emotional speech demonstrates that these sounds can also be reliably identified, and rely on similar acoustic cues. A series of studies with a pre-literate Namibian tribe shows that non-verbal vocalisations can be recognized across cultures. An fMRI study carried out to investigate the neural processing of non-verbal vocalisations of emotions is presented. The results show activation in pre-motor regions arising from passive listening to non-verbal emotional vocalisations, suggesting neural auditory-motor interactions in the perception of these sounds. In sum, this thesis demonstrates that non-verbal vocalisations of emotions are reliably identifiable tokens of information that belong to discrete categories. These vocalisations are recognisable across vastly different cultures and thus seem to, like facial expressions of emotions, comprise human universals. Listeners rely mainly on pitch and pitch variation to identify emotions in non verbal vocalisations, which differs with the cues used to comprehend speech. When listening to others' emotional vocalisations, a neural system of preparatory motor activation is engaged

Tailoring Interaction. Sensing Social Signals with Textiles.

Nonverbal behaviour is an important part of conversation and can reveal much about the nature of an interaction. It includes phenomena ranging from large-scale posture shifts to small scale nods. Capturing these often spontaneous phenomena requires unobtrusive sensing techniques that do not interfere with the interaction. We propose an underexploited sensing modality for sensing nonverbal behaviours: textiles. As a material in close contact with the body, they provide ubiquitous, large surfaces that make them a suitable soft interface. Although the literature on nonverbal communication focuses on upper body movements such as gestures, observations of multi-party, seated conversations suggest that sitting postures, leg and foot movements are also systematically related to patterns of social interaction. This thesis addressees the following questions: Can the textiles surrounding us measure social engagement? Can they tell who is speaking, and who, if anyone, is listening? Furthermore, how should wearable textile sensing systems be designed and what behavioural signals could textiles reveal? To address these questions, we have designed and manufactured bespoke chairs and trousers with integrated textile pressure sensors, that are introduced here. The designs are evaluated in three user studies that produce multi-modal datasets for the exploration of fine-grained interactional signals. Two approaches to using these bespoke textile sensors are explored. First, hand crafted sensor patches in chair covers serve to distinguish speakers and listeners. Second, a pressure sensitive matrix in custom-made smart trousers is developed to detect static sitting postures, dynamic bodily movement, as well as basic conversational states. Statistical analyses, machine learning approaches, and ethnographic methods show that by moni- toring patterns of pressure change alone it is possible to not only classify postures with high accuracy, but also to identify a wide range of behaviours reliably in individuals and groups. These findings es- tablish textiles as a novel, wearable sensing system for applications in social sciences, and contribute towards a better understanding of nonverbal communication, especially the significance of posture shifts when seated. If chairs know who is speaking, if our trousers can capture our social engagement, what role can smart textiles have in the future of human interaction? How can we build new ways to map social ecologies and tailor interactions