Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Searching for design energy: re-visiting my generative process using selection, evaluation, and morphing to generate new ideas

My interests lie primarily with the energy behind the design of products. In pursuing the question of that energy and its potential, it has become clear to me that prior to any claims regarding transferability it is vital to first document and analyze the components that comprise the whole of design energy. Design Energy, in this case, is the creativity and intellect behind the process of design, from idea generation to production. I have focused on the creation of a method for documenting the design process that incorporates scans and images and other process data by utilizing AutoCAD, 3DMax, Photoshop, Morpheus, and various animation creating software. Through this method I re-visited my generative process in three phases; one by searching and selecting snapshots of my creative work, two by manipulating these found images using morphing software, and three by comparing and mimicking aspects of other design processes as explained by Nigel Cross. By looking back at my process I was able to reevaluate my steps through comparing them to other processes and through my analysis method of using morphing software which led to new idea generation

Does Information on Automated Driving Functions and the Way of Presenting It before Activation Influence Users’ Behavior and Perception of the System?

Information on automated driving functions when automation is not activated but is available have not been investigated thus far. As the possibility of conducting non-driving related activities (NDRAs) is one of the most important aspects when it comes to perceived usefulness of automated cars and many NDRAs are time-dependent, users should know the period for which automation is available, even when not activated. This article presents a study (N = 33) investigating the effects of displaying the availability duration before&mdash versus after&mdash activation of the automation on users&rsquo activation behavior and on how the system is rated. Furthermore, the way of addressing users regarding the availability on a more personal level to establish &ldquo sympathy&rdquo with the system was examined with regard to acceptance, usability, and workload. Results show that displaying the availability duration before activating the automation reduces the frequency of activations when no NDRA is executable within the automated drive. Moreover, acceptance and usability were higher and workload was reduced as a result of this information being provided. No effects were found with regard to how the user was addressed. Document type: Articl

Connecting people through physiosocial technology

Social connectedness is one of the most important predictors of health and well-being. The goal of this dissertation is to investigate technologies that can support social connectedness. Such technologies can build upon the notion that disclosing emotional information has a strong positive influence on social connectedness. As physiological signals are strongly related to emotions, they might provide a solid base for emotion communication technologies. Moreover, physiological signals are largely lacking in unmediated communication, have been used successfully by machines to recognize emotions, and can be measured relatively unobtrusively with wearable sensors. Therefore, this doctoral dissertation examines the following research question: How can we use physiological signals in affective technology to improve social connectedness? First, a series of experiments was conducted to investigate if computer interpretations of physiological signals can be used to automatically communicate emotions and improve social connectedness (Chapters 2 and 3). The results of these experiments showed that computers can be more accurate at recognizing emotions than humans are. Physiological signals turned out to be the most effective information source for machine emotion recognition. One advantage of machine based emotion recognition for communication technology may be the increase in the rate at which emotions can be communicated. As expected, experiments showed that increases in the number of communicated emotions increased feelings of closeness between interacting people. Nonetheless, these effects on feelings of closeness are limited if users attribute the cause of the increases in communicated emotions to the technology and not to their interaction partner. Therefore, I discuss several possibilities to incorporate emotion recognition technologies in applications in such a way that users attribute the communication to their interaction partner. Instead of using machines to interpret physiological signals, the signals can also be represented to a user directly. This way, the interpretation of the signal is left to be done by the user. To explore this, I conducted several studies that employed heartbeat representations as a direct physiological communication signal. These studies showed that people can interpret such signals in terms of emotions (Chapter 4) and that perceiving someone's heartbeat increases feelings of closeness between the perceiver and sender of the signal (Chapter 5). Finally, we used a field study (Chapter 6) to investigate the potential of heartbeat communication mechanisms in practice. This again confirmed that heartbeat can provide an intimate connection to another person, showing the potential for communicating physiological signals directly to improve connectedness. The last part of the dissertation builds upon the notion that empathy has positive influences on social connectedness. Therefore, I developed a framework for empathic computing that employed automated empathy measurement based on physiological signals (Chapter 7). This framework was applied in a system that can train empathy (Chapter 8). The results showed that providing users frequent feedback about their physiological synchronization with others can help them to improve empathy as measured through self-report and physiological synchronization. In turn, this improves understanding of the other and helps people to signal validation and caring, which are types of communication that improve social connectedness. Taking the results presented in this dissertation together, I argue that physiological signals form a promising modality to apply in communication technology (Chapter 9). This dissertation provides a basis for future communication applications that aim to improve social connectedness

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Operationalising luxury in the premium automotive industry

This thesis presents an Action Research project investigating the use of customers’ perceptions of premium and luxury cars within the premium automotive industry. The research was sponsored by Jaguar Land Rover (JLR), a UK-based automotive manufacturer. An inductive, phenomenological approach was adopted in which JLR’s Premiumness Research Programme (PRP) was used as a case study to build an understanding of the consumer’s perception of luxury, to discover how to communicate this understanding within the business, and to determine how it could be integrated into the NPD process. A passive exploratory study was conducted to understand JLR’s PRP work, to seek new insights about the nature of customer’s reactions when evaluating luxury and premium cars, and to assess JLR’s approach in conducting the PRP. An interventionist descriptive study was conducted to probe for deeper insights into how successful JLR’s research and dissemination process had been within the company, to establish how the wider NPD community interacted with the data, and to develop and test new ideas and tools that enhanced the utility and accessibility of the PRP data. The research generated 58 Research Observations and 36 individual insights that challenged conventional wisdom about how the voice of the customer (VoC) can be captured and used in the NPD process. JLR’s PRP methodology was revealed as a powerful multi-method technique for acquiring data about consumers’ expectations of luxury automotive brands and products, their reactions when evaluating luxury and premium vehicles, and their emotional satisfaction with features and attributes of luxury and premium vehicles. Limitations in JLR’s ability to process and operationalise such data lead to the development of a Premiumness Verbatims Database tool which enabled the wider NPD community to access the PRP knowledge in a safe and meaningful way by considering the translation and utility of subjective VoC data

From video to hybrid simulator: Exploring affective responses toward non-verbal pedestrian crossing actions using camera and physiological sensors

Capturing drivers’ affective responses given driving context and driver-pedestrian interactions remains a challenge for designing in-vehicle, empathic interfaces. To address this, we conducted two lab-based studies using camera and physiological sensors. Our first study collected participants’ (N = 21) emotion self-reports and physiological signals (including facial temperatures) toward non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving dataset. Our second study increased realism by employing a hybrid driving simulator setup to capture participants’ affective responses (N = 24) toward enacted, non-verbal pedestrian crossing actions. Key findings showed: (a) non-positive actions in videos elicited higher arousal ratings, whereas different in-video pedestrian crossing actions significantly influenced participants’ physiological signals. (b) Non-verbal pedestrian interactions in the hybrid simulator setup significantly influenced participants’ facial expressions, but not their physiological signals. We contribute to the development of in-vehicle empathic interfaces that draw on behavioral and physiological sensing to in-situ infer driver affective responses during non-verbal pedestrian interactions