Moral sentimentalism is a crucial field for building moral competence into artificial intelligence (AI) artefacts through emotion recognition and modeling techniques. Although the relation between emotions and the construction of morally relevant thoughts is not straightforward, various business applications are implied to be capable of detecting morally relevant mental content of their users through automated emotion recognition (AER). These applications rely on problematic ideas; while they involve effective technical solutions (e.g., image recognition, natural language processing), the foundations of their operationalization processes (i.e., selection of data collection and analytical methods for connecting emotions and moral thinking) are invalid. Following calls for a multidisciplinary methodological discourse on affective computing and emotionally laden human-technology interaction design, we propose a discussion of the use of automated emotion recognition in AI ethics. We start the discussion by considering two questions: (1) Are the attempts to design moral competence in AI artefacts by means of automated emotion recognition techniques relevant? (2) If so, what kinds of limits should be acknowledged? By clarifying the methodological landscape, we reveal that, at best, automated emotion recognition can provide only hints about a person’s emotional information processing. Thus, the most urgent question to address is, what is the nature of these hints and their connection to the person’s moral reasoning and actions? We coin the design concept of inherent areas of reasoning to guide proper operationalization processes for both technically and scientifically valid treatment of the question. Additionally, we elaborate how inherent areas of reasoning is related to another pivotal design concept: emotion task. A major threat is that an increasing amount of scientifically invalid applications will be adopted. The worry is urgent given that scientifically biased applications of emotion recognition systems have grown into large businesses.

Modern information technology makes it possible to redesign the ways people work. In the future, machines can carry out intelligence-requiring tasks, which previously were done by people. It is thus good to develop methodologies for designing intelligent systems. An example of such methods is cognitive mimetics, i.e. imitating human information processing. Today, machines cannot by themselves navigate in archipelagos. However, the fact that people can take care of ship steering and navigation means that there is an information process, which makes it possible to navigate ships. This information process takes place inside the minds of navigating people. If we are able to explicate the information processing in the navigator’s mind, the knowledge of it can be used in designing intelligent machines. Replicating physical objects and industrial processes by means of digital computers is called digital twinning. Digital twins (DTs), which are digital replicas of physical systems and processes, have recently become tools for working with complex industrial processes. A crucial question for DTs is should human actions be added to them? As the answer is positive, such models of human information processing can be called human digital twins (HDTs). The knowledge of human tacit and explicit information processes can be represented by human digital twins. Models can be used in the search for a deeper understanding of human intelligent information processes. Human digital twins can thus be used as methodological tools in cognitive mimetics. In our present study, we modeled paper machine operators’ thinking. Specifically, we developed an ideal-exception-correction (IEC) model for paper operators’ control logic. The model illustrates how research and HDT-modeling can be used for explicating the subconscious or tacit information processing of people for the design of intelligent systems. In this article a model for design processes using cognitive modelling will be suggested. The concepts of cognitive mimetics and human digital twins enable us to outline a model for using the long tradition of simulating human thinking as a tool in designing intelligent systems.

In this paper, we illustrate a concrete case to apply the Life-Based Design approach to identifying human goals for technology to achieve. We focus on a form-of-life method of design, which seeks to reach a mental state of “digital wellbeing.” Digital well-being aims to facilitate digitalization and changes in the digital environment, and to maximize the availability and accessibility of services. We evaluate the target group of senior citizens, who are facing an accelerating pace of digitalization of the services in their daily lives. This increases their sense of anxiety and undermines their well-being.

The core idea of experience-driven design is to define the intended experience before functionality and technology. This is a radical idea for companies that have built their competences around specific technologies. Although many technology companies are willing to shift their focus towards experience-driven design, reports on real-life cases about the utilization of this design approach are rare. As part of an industry-led research program, we introduced experience-driven design to metal industry companies with experience goals as the key technique. Four design cases in three companies showed that the goals are useful in keeping the focus on user experience, but several challenges are still left for future research to tackle. This exploratory research lays ground for future research by providing initial criteria for assessing experience design tools. The results shed light on utilizing experience goals in industrial design projects and help practitioners in planning and managing the product design process with user experience in mind.

A method for primed product comparisons was developed, based on the methodological considerations of emotional appraisal process and affective mental contents. The method was implemented as a computer tool, which was utilised in two experiments (N = 18 for both). Ten adjectives served as primes, and five drinking glass pictures as stimuli. Participants' task was to choose a preference between two glasses, given the priming adjective. The results validate the method by providing test-retest reliability measures and showing convergence with questionnaires. Further, different evaluation times between the primes and the stimuli reveal the existence of different mental processes associated with various aspects of product experience, as predicted by appraisal theory. The results have various implications for experience research and development in HCI, as they demonstrate how the method can be used for product evaluation and the analysis of the mental processes, which users use to evaluate the products.

The key prerequisite for experience-driven design is to define what experience to design for. User experience (UX) goals concretise the intended experience. Based on our own case studies from industrial environments and a literature study, we propose five different approaches to acquiring insight and inspiration for UX goal setting: Brand, Theory, Empathy, Technology, and Vision. Each approach brings in a different viewpoint, thus supporting the multidisciplinary character of UX. The Brand approach ensures that the UX goals are in line with the company's brand promise. The Theory approach utilises the available scientific knowledge of human behaviour. The Empathy approach focuses on knowing the actual users and stepping into their shoes. The Technology approach considers the new technologies that are being introduced and their positive or negative influence on UX. Finally, the Vision approach focuses on renewal, introducing new kinds of UXs. In the design of industrial systems, several stakeholders are involved and they should share common design goals. Using the different UX goal-setting approaches together brings in the viewpoints of different stakeholders, thus committing them to UX goal setting and emphasising UX as a strategic design decision.

User psychology is a human–technology interaction research approach that uses psychological concepts, theories, and findings to structure problems of human–technology interaction. As the notion of user experience has become central in human–technology interaction research and in product development, it is necessary to investigate the user psychology of user experience. This analysis of emotional human–technology interaction is based on the psychological theory of basic emotions. Three studies, two laboratory experiments, and one field study are used to investigate the basic emotions and the emotional mind involved in user experience. The first and second experiments study the measurement of subjective emotional experiences during novel human–technology interaction scenarios in a laboratory setting. The third study explores these aspects in a real-world environment. As a result of these experiments, a bipolar competence–frustration model is proposed, which can be used to understand the emotional aspects of user experience.

The purpose of this paper is to clarify the differences between approaches in the research field of human-technology interaction. We are especially interested in individuating user psychology from the more traditional paradigms. Therefore, we suggest a preliminary theoretical framework of criteria for distinguishing and individuating the different interaction research paradigms. The framework consists of five dimensions in which the paradigms may vary from each other. In this paper, we also discuss how ubiquitous computing is related to some of the dimensions. In addition, we focus on defining the new elements user psychology can bring to the discussion and analysis of human-technology interaction. To demonstrate the usage of the framework, we apply it to differentiate user psychology from traditional HCI research.

Understanding information graphics rely on thought process of graphics' reader, and thinking has the most important role in interpretation and exploitation. We are interested in the cognitive mechanisms underlying interaction with information graphics. In this study, we studied those cognitive processes which are employed when interacting with information graphics. Based on these experiments, we have separated four different types of thought processes which occur during the interactions with information graphics. We propose that these thought processes are apperception, restructuring, reflection and construction.

Wiki-technology is one of the new internet collaborative service platform and allows, among other things, building innovation networks for industries and research groups. To make these wiki tools really effective it is essential to understand human ICT-service interaction problems on scientific psychological grounds instead of resorting to folk psychological intuitions. The problem with wikis is that their inbuilt interaction problems may substantially interfere with the development of open innovation communities. This is why it is essential to investigate psychological factors in the interactions with open innovation systems.

The extent to which memory for information content is reliable, trustworthy, and accurate is crucial in the information age. Being forced to divert attention to interrupting messages is common, however, and can cause memory loss. The memory effects of interrupting messages were investigated in three experiments. In Experiment 1, attending to an interrupting message decreased memory accuracy. Experiment 2, where four interrupting messages were used, replicated this result. In Experiment 3, an interrupting message was shown to be most disturbing when it was semantically very close to the main message. Drawing from a theory of long-term working memory it is argued that interrupting messages can both disrupt the active semantic elaboration of content during encoding and cause semantic interference upon retrieval. Properties of the interrupting message affect the extent and type of errors in remembering. Design implications are discussed.

The spatial arrangement of elements such as icons in a computer interface may influence learning the interface. However, the effects of layout organization on users' information processing is relatively little studied so far. The three experiments of this paper examined two attributes of layouts: spatial grouping by proximity and semantic coherence. Learning was assessed by tasks in which 30 participants recalled icon-like items' labels, locations, or both as a series of study-recall trials. The results show that layout organization interacts with task demands. Semantic organization improves recall of labels, and spatial grouping supports recall of locations. When both labels and locations are learned concurrently, the best recall performance is associated with a simultaneously grouped and semantically coherent layout. However, semantic and spatial organization may interact unexpectedly on learning. The findings are discussed from the viewpoint of information chunking in memory processes and interface design.