Demystifying the Black Box - Exploring How Users Make Sense of Fully Automated Vehicles

Vehicles are becoming increasingly automated. Already today vehicles are able to take over and assist the human driver in certain driving tasks, and the scope of technical possibilities is rapidly expanding. However, for the proliferation of AVs to occur, there are several challenges that must first be overcome. These challenges are not only structural, including regulations and technological development, but also user-related, such as the adoption of and willingness of users to use the system. Previous research has identified the importance of user understanding of Automated Vehicles (AVs), as this affects usage directly as well as indirectly by impacting acceptance. In this thesis, a design approach has been chosen that uses a product semantic framework as the basis for addressing the issue of user understanding with the aim of exploring how users make sense of the AV. The research presented is based on data from a quasi-experimental study, conducted using a seemingly fully automated (WOz) vehicle on a test course where participants’ understanding was investigated both during and after the test runs.The findings show that use of the AV gave rise to several levels of meaning, based on two different processes. The main one was an external process, where integration of the participants’ conceptual models of human drivers and AVs, artefactual signifiers, and situational signifiers in a context developed meaning. However, an internal process was also evident, where meanings themselves developed new meanings. This thesis presents a tentative model based on the findings, describing three important components: the user’s conceptual model, the signifiers, and the meanings that arise during usage of the AV. The model illustrates the complex interplay between these three components and can be used to better understand and investigate how users make sense of AVs to aid the design and development of AVs. The thesis also contributes to the field of product semantics through the practical application of product semantic theories, in addition to providing further insight into how users develop meaning and make sense of artefacts, by describing the processes and components which seem to be the foundation when making sense of artefacts.Having said that, further studies need to explore in greater detail the dynamics of the process of making sense, the process of making sense in partially automated vehicles, and how meaning changes during a prolonged usage

A Connotative Space for Supporting Movie Affective Recommendation

The problem of relating media content to users’affective responses is here addressed. Previous work suggests that a direct mapping of audio-visual properties into emotion categories elicited by films is rather difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of video features and the subjective sphere of emotions, we propose to shift the representation towards the connotative properties of movies, in a space inter-subjectively shared among users. Consequently, the connotative space allows to define, relate and compare affective descriptions of film videos on equal footing. An extensive test involving a significant number of users watching famous movie scenes, suggests that the connotative space can be related to affective categories of a single user. We apply this finding to reach high performance in meeting user’s emotional preferences

The ins and outs of pleasure: roles and importance of hedonic value

Pagination differs from hard bound copy. Index missing from e-thesis.The focus of this thesis was the hedonic value of stimuli, which is more commonly known as pleasure or positive affect. First, the scientific meaning of hedonic value was dissected. Second, a classification identifying core causes of positive affect was created. The classification was derived from specific positive moments reported by individuals throughout a day (collected through experience sampling methodology). Seventeen triggers of positive affect were identified, which were extracted from the data rather than originating from theory. Third, affective influences on reflexive-like motor responses were investigated using an approach-avoidance task. Contrary to previous studies, approach reaction times were not speeded by highly affective stimuli. Instead, a novel non-emotional effect was found on reaction times, which could directly explain the current results, and those of previous studies, in non-affective terms. Fourth, the propagation of hedonic reactivity from pleasurable to neutral stimuli was investigated. Contrary to expectations, the evaluative conditioning procedure utilised did not exhibit a phenomenon called blocking. Instead, 'liking' spread non-selectively to all stimuli co-occurring with the source hedonic stimulus. Fifth, the positive effect of pleasure on goal-directed motivation was established: participants were found to press a food trigger harder for highly palatable snacks compared to bland snacks, even though participants were not informed about the hidden measurement of forces. Additionally, the impact of hedonic value on actual food intake was quantified with best-fit equations that predicted consumption at both the group and individual level. In the last study, hedonic habituation, or the inhibitory effect of pleasure on itself, was demonstrated: eating pleasant snacks, as compared to bland ones, reduced the hedonic ratings of test foods that were consumed afterwards. Finally, these inputs and outputs of hedonics were integrated into a model specifying principal roles of pleasure in human behaviour. This pleasure-incentive model explains the effects of pleasure on incentive motivation, and makes important predictions about the mechanisms of pathological conditions such as over-eating and drug addiction

Who is the director of this movie? Automatic style recognition based on shot features

We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on the full filmographies of six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second, confirms that these shot-related features do not appear as random patterns in movies from the same director. For feature extraction we adopt methods based on both conventional and deep learning techniques. Our findings suggest that feature sequential patterns, i.e. how features evolve in time, are at least as important as the related feature distributions. To the best of our knowledge this is the first study dealing with automatic attribution of movie authorship, which opens up interesting lines of cross-disciplinary research on the impact of style on the aesthetic and emotional effects on the viewers

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Political communications in the form of unstructured text convey rich connotative meaning that can reveal underlying group social processes. Previous research has focused on sentiment analysis at the document level, but we extend this analysis to sub-document levels through a detailed analysis of affective relationships between entities extracted from a document. Instead of pure sentiment analysis, which is just positive or negative, we explore nuances of affective meaning in 22 affect categories. Our affect propagation algorithm automatically calculates and displays extracted affective relationships among entities in graphical form in our prototype (TEAMSTER), starting with seed lists of affect terms. Several useful metrics are defined to infer underlying group processes by aggregating affective relationships discovered in a text. Our approach has been validated with annotated documents from the MPQA corpus, achieving a performance gain of 74% over comparable random guessers