It’s not the model that doesn’t fit, it’s the controller! The role of cognitive skills in understanding the links between natural mapping, performance, and enjoyment of console video games

This study examines differences in performance, frustration, and game ratings of individuals playing first person shooter video games using two different controllers (motion controller and a traditional, pushbutton controller) in a within-subjects, randomized order design. Structural equation modeling was used to demonstrate that cognitive skills such as mental rotation ability and eye/hand coordination predicted performance for both controllers, but the motion control was significantly more frustrating. Moreover, increased performance was only related to game ratings for the traditional controller input. We interpret these data as evidence that, contrary to the assumption that motion controlled interfaces are more naturally mapped than traditional push-button controllers, the traditional controller was more naturally mapped as an interface for gameplay

Experiencing Poverty in an Online Simulation: Effects on Players’ Beliefs, Attitudes and Behaviors about Poverty

Digital simulations are increasingly used to educate about the causes and effects of poverty, and inspire action to alleviate it. Drawing on research about attributions of poverty, subjective well-being, and relative income, this experimental study assesses the effects of an online poverty simulation (entitled Spent) on participants’ beliefs, attitudes, and actions. Results show that, compared with a control group, Spent players donated marginally more money to a charity serving the poor and expressed higher support for policies benefitting the poor, but were less likely to take immediate political action by signing an online petition to support a higher minimum wage. Spent players also expressed greater subjective well-being than the control group, but this was not associated with increased policy support or donations. Spent players who experienced greater presence (perceived realism of the simulation) had higher levels of empathy, which contributed to attributing poverty to structural causes and support for anti-poverty policies. We draw conclusions for theory about the psychological experience of playing online poverty simulations, and for how they could be designed to stimulate charity and support for anti-poverty policies

Video games as meaningful entertainment experiences

We conducted an experiment to examine individuals’ perceptions of enjoyable and meaningful video games and the game characteristics and dimensions of need satisfaction associated with enjoyment and appreciation. Participants (N = 512) were randomly assigned to 1 of 2 groups that asked them to recall a game that they found either particularly fun or particularly meaningful, and to then rate their perceptions of the game that they recalled. Enjoyment was high for both groups, though appreciation was higher in the meaningful- than fun-game condition. Further, enjoyment was most strongly associated with gameplay characteristics and satisfaction of needs related to competency and autonomy, whereas appreciation was most strongly associated with story characteristics and satisfaction of needs related to insight and relatedness

The motivational pull of video game feedback, rules, and social interaction: Another self-determination theory approach

This paper argues that most video game enjoyment can be understood in terms of the type of feedback used, the rules set out by the game and the social elements of the game - concepts that have been identified as critical to video games. Self-determination theory (SDT) is used as a lens for understanding the mechanism by which these traits might lead to enjoyment. Specifically, the argument is that feedback, rules, and social elements of games will fulfill the dimensions of SDT - competence autonomy, and relatedness. Then, the dimensions of SDT will predict enjoyment. Participants were presented with a game that emphasized feedback, rules, or social elements. Games that emphasized flexible rules led to feelings of competence while games that emphasized social elements led to feelings of relatedness. Competence and elatedness then led to feelings of enjoyment. In doing so, this study identifies key elements of video games while illuminating ways to understand video game enjoyment

Malicious User Experience Design Research for Cybersecurity

This paper explores the factors and theory behind the user-centered research that is necessary to create a successful game-like prototype, and user experience, for malicious users in a cybersecurity context. We explore what is known about successful addictive design in the fields of video games and gambling to understand the allure of breaking into a system, and the joy of thwarting the security to reach a goal or a reward of data. Based on the malicious user research, game user research, and using the GameFlow framework, we propose a novel malicious user experience design approac

The Racing-Game Effect: Why Do Video Racing Games Increase Risk-Taking Inclinations?

The present studies investigated why video racing games increase players’ risk-taking inclinations. Four studies reveal that playing video racing games increases risk taking in a subsequent simulated road traffic situation, as well as risk-promoting cognitions and emotions, blood pressure,sensation seeking, and attitudes toward reckless driving. Study 1 ruled out the role of experimental demand in creating such effects. Studies 2 and 3 showed that the effect of playing video racing games on risk taking was partially mediated by changes in selfperceptions as a reckless driver. These effects were evident only when the individual played racing games that reward traffic violations rather than racing games that do not reward traffic violations (Study 3) and when the individual was an active player of such games rather than a passive observer (Study 4). In sum, the results underline the potential negative impact of racing games on traffic safety

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

‘In the game’? Embodied subjectivity in gaming environments

Human-computer interactions are increasingly using more (or all) of the body as a control device. We identify a convergence between everyday bodily actions and activity within digital environments, and a trend towards incorporating natural or mimetic form of movement into gaming devices. We go on to reflect on the nature of player ‘embodiment’ in digital gaming environments by applying insights from the phenomenology of Maurice Merleau-Ponty. Three conditions for digital embodiment are proposed, with implications for Calleja’s (2011) Player Involvement Model (PIM) of gaming discussed

Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours