Points are widely used design elements in gamified systems. Yet how they motivate is still unclear: what motivational meaning or functional significance do users ascribe to points and when? To answer this question, we conducted a semi-structured interview study with 27 users of two popular gamified platforms, Duolingo and Habitica. Through reflexive thematic analysis, we constructed six different types of functionalisation variously proposed in prior gamification and personal informatics work but often not empirically supported. We highlight the importance of functional design detail (such as points should proportionally reward effort) and derive design guidelines.

Virtual Reality (VR) applications based on real-world contexts, such as training, healthcare, and entertainment, rely on varying degrees of replication of real settings. However, there is a lack of systematic design support for determining which elements of a setting to retain or modify. We propose Behavior Setting Map (BSM), a contextual analysis tool to help VR designers understand and transform real-world settings into virtual ones. In a two-stage study, we piloted and iterated BSM with 30 student designers and 24 individuals in the early stages of content design for real-world VR applications. The evaluation results provide empirical insights into participants’ perceptions of BSM, suggesting that the tool was perceived as easy to use and useful in supporting early-stage VR content design through a structured, guided, and visualized approach, accommodating designers with varying levels of experience. Additionally, we discuss the design opportunities and potentials of BSM in a wide range of Extended Reality applications.

Self-determination theory (SDT) has been widely successful in human-computer interaction (HCI). It offers ready concepts, measures, and theoretical propositions for third wave HCI topics such as user experience, fun, wellbeing, motivation, or user autonomy. Still, HCI applications of SDT have been partial, at times superficial, and disconnecting-leaving great unfulfilled potential which motivated the present special issue. In this introduction, we present SDT to interested scholars, chart its use across HCI to date, and outline six advances to move HCI toward more intentional applications of SDT. As the articles from this issue illustrate, future growth areas of SDT in HCI are in extending domain-specific models and applications, harnessing underused parts of theory, computational formalization, extending levels of analysis, facilitating design translation, and engaging in a cross-disciplinary dialogue on autonomy.

Computational models offer powerful tools for formalising psychological theories, making them both testable and applicable in digital contexts. However, they remain little used in the study of motivation within psychology. We focus on the "need for competence", postulated as a key basic human need within Self-Determination Theory (SDT) – arguably the most influential psychological framework for studying intrinsic motivation (IM). The need for competence is treated as a single construct across SDT texts. Yet, recent research has identified multiple, ambiguously defined facets of competence in SDT. We propose that these inconsistencies may be alleviated by drawing on computational models from the field of artificial intelligence, specifically from the domain of reinforcement learning (RL). By aligning the aforementioned facets of competence – effectance, skill use, task performance, and capacity growth – with existing RL formalisms, we provide a foundation for advancing competence-related theory in SDT and motivational psychology more broadly. The formalisms reveal underlying preconditions that SDT fails to make explicit, demonstrating how computational models can improve our understanding of IM. Additionally, our work can support a cycle of theory development by inspiring new computational models formalising aspects of the theory, which can then be tested empirically to refine the theory. While our research lays a promising foundation, empirical studies of these models in both humans and machines are needed, inviting collaboration across disciplines.

As video games have moved to the mainstream of entertainment and popular culture, they also have given rise to new media fears. These span concerns for player welfare such as gaming addiction, negative effects of ‘screen time,’ gambling-like mechanics, dark patterns and questionable business practices, online toxicity, and extremism. Questions of game worker welfare are similarly making headlines, such as harassment, discrimination, or precarious and unhealthy working conditions. To sort warranted concerns from unwarranted moral panics, the first Ethical Games Conference, held in 2024, gathered the state of the art of research on ethical issues in games to inform evidence-based guidelines for industry and regulators. The selected full papers and opinion pieces of the conference, collected in this special issue, showcase a wide range of issues and barriers, and aspirations to move from avoiding harm to using games for positive social impact.

Computational modelling is a powerful tool to specify psychological theories, render them severely testable, and implement them into digital applications. Yet it has seen little uptake in Self-Determination Theory (SDT). Here, we demonstrate that SDT’s verbal definitions of competence and optimal challenge are underspecified in theoretically and practically relevant ways. Conceptual analyses of key texts identifies four verbal facets of competence definitions that need not co-occur and are inconsistently reflected in common self-report measures and operationalisations. Optimal challenge is insufficiently specified to be severely tested or implemented in practice, and entails a logical and empirical incoherence. Finally, SDT lacks a cognitive model of competence. We outline how computational modelling, inspired by the AI field of computational intrinsic motivation,can help resolve resulting issues.

People are spending more and more time interacting with virtual objects and environments. We argue that Roger Barker’s concept of a ‘behaviour setting’ can be usefully applied to such experiences with relatively little modification if we recognize subjective aspects of such experiences such as presence and immersion. We define virtual behaviour settings as virtual environments where the partly or fully digital milieu is synomorphic with and circumjacent to embodied behaviour, as opposed to the fragmented behaviour settings of much-mediated interaction. We present two tools that can help explain and predict the outcomes of virtual experiences—the behaviour setting canvas (BSC) and model—and demonstrate their utility through examples. We conclude that the behaviour setting concept is helpful in both designing virtual environments and understanding their impact, while virtual environments offer a powerful new methodological paradigm for studying behaviour settings. This article is part of the theme issue ‘People, places, things, and communities: expanding behaviour settings theory in the twenty-first century’.

KEYWORDS: Gamesinteractive mediaserious gamesattentionattentional selectionactive samplinglearninggame-based learning

‘Juicy’ or immediate abundant action feedback is widely held to make video games enjoyable and intrinsically motivating. Yet we do not know why it works: Which motives are mediating it? Which features afford it? In a pre-registered (n=1,699) online experiment, we tested three motives mapping prior practitioner discourse—effectance, competence, and curiosity—and connected design features. Using a dedicated action RPG and a 2x2+control design, we varied feedback amplification, success-dependence, and variability and recorded self-reported effectance, competence, curiosity, and enjoyment as well as free-choice playtime. Structural equation models show curiosity as the strongest enjoyment and only playtime predictor and support theorised competence pathways. Success dependence enhanced all motives, while amplification unexpectedly reduced them, possibly because the tested condition unintentionally impeded players’ sense of agency. Our study evidences uncertain success affording curiosity as an underappreciated moment-to-moment engagement driver, directly supports competence-related theory, and suggests that prior juicy game feel guidance ties to legible action-outcome bindings and graded success as preconditions of positive ‘low-level’ user experience.

Recent years have seen intense research, media, and policy debate on whether amount of time spent playing video games ("playtime") affects players' well-being. Existing research has used cross-sectional designs with easy-to-obtain but unreliable self-report measures of playtime or, in rare instances, obtained industry data on objectively tracked playtime but only for individual games, not a player's total playtime across games. Further, researchers have raised concerns that publication bias and a lack of differentiation between exploratory and confirmatory research have undermined the credibility of the evidence base. As a result, we still do not know whether well-being affects playtime, playtime affects well-being, both, or neither. To track people's playtime across multiple games, we developed a method to log playtime on the Xbox platform. In a 12-week, six-wave panel study of adult U.S./U.K. Xbox-predominant players (414 players, 2036 completed surveys), we investigated within-person temporal relations between objectively measured playtime and well-being. Across multiple preregistered model specifications, we found that the within-person prospective relationships between playtime and well-being, or vice versa, were not practically significant—even the largest associations were unlikely to register a perceptible impact on a player's well-being. These results support the growing body of evidence that playtime is not the primary factor in the relationship between gaming and mental health for the majority of players and that research focus should be on the context and quality of gameplay instead.

Existing theories of how game use relates to mental health have important limitations: few account for both quantity and quality of use, differentiate components of mental health (hedonic wellbe- ing, eudaimonic wellbeing, and illbeing), provide an explanation for both positive and negative outcomes, or readily explain the well-evidenced absence of playtime effects on mental health. Many also lack the specificity to be readily falsifiable. In response, we present the Basic Needs in Games (BANG) model. Grounded in self- determination theory, BANG proposes that mental health outcomes of game use are in large part mediated by the motivational quality of play and the extent to which play quantity and quality lead to need satisfaction or frustration. We show how BANG addresses the limitations of current theories and aligns with emerging evidence on the etiologies of disordered play. Thus, BANG advances HCI theory on the impact of games and other interactive technologies on mental health.

Language models are widely used for different Natural Language Processing tasks while suffering from a lack of personalization. Personalization can be achieved by, e.g., fine-tuning the model on training data that is created by the user (e.g., social media posts). Previous work shows that the acquisition of such data can be challenging. Instead of adapting the model's parameters, we thus suggest selecting a model that matches the user's mental model of different thematic concepts in language. In this article, we attempt to capture such individual language understanding of users. In this process, two challenges have to be considered. First, we need to counteract disengagement since the task of communicating one's language understanding typically encompasses repetitive and time-consuming actions. Second, we need to enable users to externalize their mental models in different contexts, considering that language use changes depending on the environment. In this article, we integrate methods of gamification into a visual analytics (VA) workflow to engage users in sharing their knowledge within various contexts. In particular, we contribute the design of a gameful VA playground called Concept Universe. During the four-phased game, the users build personalized concept descriptions by explaining given concept names through representative keywords. Based on their performance, the system reacts with constant visual, verbal, and auditory feedback. We evaluate the system in a user study with six participants, showing that users are engaged and provide more specific input when facing a virtual opponent. We use the generated concepts to make personalized language model suggestions.

Computational models are powerful tools to formalise psychological theories, render them severely testable, and embed them into digital applications. Yet they have seen little uptake in Self-Determination Theory (SDT). Here we demonstrate how SDT's conception of competence and optimal challenge can benefit from computational modelling informed by AI research on computational intrinsic motivation (IM). We surface underspecification in present verbal definitions, challenging the construct validity of common operationalisations and impeding computational implementations. We identify four separate verbal facets of competence and match them to distinct computational IM formalisms. These leverage formal accounts of novelty, diversity, or progress to drive skill acquisition, yielding optimal challenge-seeking behaviour and other competence dynamics postulated by SDT. We argue that these accounts specifically and computational IM more widely highlight inconsistencies within, and can concretise SDT's verbal articulations. We gently introduce motivation researchers to computational modelling and IM, complement model intuition with formal detail, and provide practical pointers for their use in psychological research and application.

Players’ basic psychological needs for autonomy, competence, and relatedness are among the most commonly used constructs used in research on what makes video games so engaging, and how they might support or undermine user wellbeing. However, existing measures of basic psychological needs in games have important limitations—they either do not measure need frustration, or measure it in a way that may not be appropriate for the video games domain, they struggle to capture feelings of relatedness in both single- and multiplayer contexts, and they often lack validity evidence for certain contexts (e.g., playtesting vs experience with games as a whole). In this paper, we report on the design and validation of a new measure, the Basic Needs in Games Scale (BANGS), whose 6 subscales cover satisfaction and frustration of each basic psychological need in gaming contexts. The scale was validated and evaluated over five studies with a total of 1246 unique participants. Results supported the theorized structure of the scale and provided evidence for discriminant, convergent and criterion validity. Results also show that the scale performs well over different contexts (including evaluating experiences in a single game session or across various sessions) and over time, supporting measurement invariance. Further improvements to the scale are warranted, as results indicated lower reliability in the autonomy frustration subscale, and a surprising non-significant correlation between relatedness satisfaction and frustration. Despite these minor limitations, BANGS is a reliable and theoretically sound tool for researchers to measure basic needs satisfaction and frustration with a degree of domain validity not previously available.

The study of roleplaying games has remained a small and somewhat separate tradition within game studies writ large. Yet roleplaying games arguably defined and influenced many design elements of today’s digital games, and constitute a formational experience for many influential digital game designers. This makes roleplaying games (and their theory) interesting from a media historical perspective alone (King & Borland 2003, Gilsdorf 2009, Peterson 2012). More importantly, the peculiarities of non-digital roleplaying games as a socially shared effort of imagination, performance, storytelling, simulation, and gaming have generated insights that have much to offer to the wider field of game studies. The particular theoretical sensibilities and concepts borne from the study of roleplaying games have, for instance, drawn attention to: • different “aesthetic agendas” afforded by game mechanics and jointly produced by players, e.g. in the “Threefold Model” and “Forge Theory” (Kim 2008, Mason 2004); • the necessary framing work performed by players to produce and navigate various levels of meaning in game play, as well as to the (re)production and negotiation of relations and boundaries between “in-game” and “real life” events, e.g. in Fine’s “frame levels” (Fine 1983);

Flow and self-determination theory predict that game difficulty in balance with player skill maximises enjoyment and engagement, mediated by attentive absorption or competence. Yet recent evidence and methodological concerns are challenging this view, and key theoretical predictions have remained untested, importantly which objective difficulty-skill ratio is perceived as most balanced. To test these, we ran a preregistered study (n=309) using a Go-like 2-player game with an AI opponent, randomly assigning players to one of three objective difficulty-skill ratios (AI plays to win, draw, or lose) over five matches. The AI successfully manipulated objective balance, with the draw condition perceived as most balanced. However, balance did not impact play behaviour, nor did we find the predicted uniform 'inverted-U' between balance and positive play experiences. Importantly, we found both theories too underspecified to severely test. We conclude that balance and competence likely matter less for behavioural engagement than commonly held. We propose alternative factors such as player appraisals, novelty, and progress, and debate the value and challenges of theory-testing work in games HCI.

In games and playable media, almost nothing is as it was at the turn of the millennium. Digital and analog games have exploded in reach, diversity, and relevance. Digital platforms and globalisation have shifted and fragmented their centres of gravity and how they are made and played. Games are converging with other media, technologies, and arts into a wide field of playable media. Games research has similarly exploded in volume and fragmented into disciplinary specialisms. All this can be deeply disorienting. The journal Games: Research and Practice wants to offer a lighthouse that helps readers orient themselves in this new, ever-shifting reality of games industry and games research.

With the rise of microtransactions, particularly in the mobile games industry, there has been ongoing concern that games reliant on these obtain substantial revenue from a small proportion of heavily involved individuals, to an extent that may be financially burdensome to these individuals. Yet despite substantive grey literature and speculation on this topic, there is little robust data available. We explore the revenue distribution in microtransaction-based mobile games using a transactional dataset of $4.7B in in-game spending drawn from 69,144,363 players of 2,873 mobile games over the course of 624 days. We find diverse revenue distributions in mobile games, ranging from a “uniform” cluster, in which all spenders invest approximately similar amounts, to “hyper-Pareto” games, in which a large proportion of revenue (approximately 38%) stems from 1% of spenders alone. Specific kinds of games are typified by higher spending: The more a game relies on its top 1% for revenue generation, the more these individuals tend to spend, with simulated gambling products (“social casinos”) at the top. We find a small subset of games across all genres, clusters, and age ratings in which the top 1% of gamers are highly financially involved—spending an average of $66,285 each in the 624 days under evaluation in the most extreme case. We discuss implications for future studies on links between gaming and wellbeing.

How does the difficulty of a task affect people's enjoyment and engagement? Intrinsic motivation and flow theories posit a 'goldilocks' optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a two-player tactical game test suite with an artificial intelligence (AI)-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n = 311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.

Video games are increasingly designed to provoke reflection and challenge players’ perspectives. Yet we know little about how such perspective-challenging experiences come about in gameplay. In response, we used systematic self-observation diaries and micro-phenomenological interviews to capture players’ (n=15) lived experience of perspective challenges in purposely sampled games including Hatoful Boyfriend, The Stanley Parable, or Papers, Please. We found a sequence of trigger, reflection, and transformation constituting perspective-challenging experiences, matching Mezirow’s model of transformative learning. Most of these were game-related or ‘endo-game’, suggesting that medium self-reflection could be an overlooked part of everyday game reflection and appreciation. Reflections were accompanied by a wide range of emotions, including frequent epistemic emotions, and emotions could change drastically even during short gameplay experiences. Actual perspective change or transformation was rare. We construct a model of granular types of triggers, reflections, and transformations that can aid reflective game design.