Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Data- and Value-Driven Software Engineering with Deep Customer Insight : Proceedings of the Seminar No. 58314308

There is a need in many software-based companies to evolve their software development practices towards continuous integration and continuous deployment. This allows a company to frequently and rapidly integrate and deploy their work and in consequence also opens opportunities for getting feedback from customers on a regular basis. Ideally, this feedback is used to support design decisions early in the development process, e.g., to determine which features should be maintained over time and which features should be skipped. In more general terms, the entire R&D system of an organization should be in a state where it is able to respond and act quickly based in instant customer feedback and where actual deployment of software functionality is seen as a way of fast experimenting and testing what the customer needs. Experimentation refers here to fast validation of a business model or more specifically validating a value hypothesis. Reaching such a state of continuous experimentation implies a lot of challenges for organizations. Selected challenges are how to develop the "right" software while developing software "right", how to have an appropriate tool infrastructure in place, how to measure and evaluate customer value, what are appropriate feedback systems, how to improve the velocity of software development, how to increase the business hit rate with new products and features, how to integrate such experiments into the development process, how to link knowledge about value for users or customers to higher-level goals of an organization. These challenges are quite new for many software-based organizations and not sufficiently understood from a software engineering perspective. These proceedings contain selected seminar papers of the student seminar Data- and Value-Driven Software Engineering with Deep Customer Insight that was held at the Department of Computer Science of the University of Helsinki. The seminar was held during the fall semester of 2014 from September 1st to December 8th. Papers in the seminar cover a wide range of topics related to the creation of value in software engineering. An interview of startups shows that emerging companies face a number of key decision points that shape their future. Value has a different meaning in different contexts. Embedded devices can be used to gather data and provide more value to the users through analysis and adaptation to circumstances. In entertainment, metrics can provide content creators the chance to react to user behavior and provide a more meaningful user experience. Value creation needs an active approach to software development from the companies: software engineering processes need to be incorporated with proper mechanisms to find the correct stakeholders, elicit requirements that provide the highest value and successfully implement the necessary changes with short development cycles. When the right building blocks are in place, companies are able to quickly deliver new software and leverage data from their products and services to continuously improve the perceived value of software

Computer-inspired Quantum Experiments

The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and \textit{practical} approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highly-efficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -- demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research.Comment: Comments and suggestions for additional references are welcome

THE DEVELOPMENT OF A MULTIMODAL NEUROADAPTIVE GAMING TECHNOLOGY TO DISTRACT FROM PAINFUL EXPERIENCES.

Painful experiences can be mitigated by distraction techniques such as video game distraction, due to limited available attentional resources. There are many benefits to using video games as a non-pharmacological intervention, including their cost-effectiveness and absence of side effects or withdrawal symptoms. However, video games cannot provide a distraction which is sufficient for pain management if they are not engaging. This work aims to discuss how and why video games capture attention and explore how modulating game factors can affect the response to pain. The aim of this work in its entirety is to develop a neuroadaptive game which is tailored to reorient attention away from a painful experience, and towards the distraction technique. The neuroadaptive element of this technology will enable a balance of challenge and skill which make a unique and playable game for each participant. The development of the neuroadaptive game was supported by two studies. Study One focused on the determination of optimal game difficulty level for pain distraction, and Study Two furthered this research, alongside determining optimal neurological sites for the monitoring of attention and attentional reorientation. Study 3 explored the use of a neuroadaptive gaming technology to distract from pain – a bespoke, real-time data processing pipeline was developed for this purpose. The limitations of the neuroadaptive game are discussed in detail with considerations for future work and development. The results of the three studies carried out during the course of this work indicate that real-time pre-processing and classification of fNIRS data to a good standard is possible. The studies also revealed that the montage for data collection and features used for data collection are crucial considerations for classification accuracy. This thesis also has implications for further work into neuroadaptive technologies and how these systems can be tested and verified. Statistical significance between a non-neuroadaptive game and a neuroadaptive game was not found throughout the course of this work, although the potential explanations and future considerations are discussed in detail. Overall, we were able to confirm that pain tolerance can be improved with the use of a distraction task, but that the balance of task difficulty and skill level is delicate and requires further exploration

14th Annual Symposium of the School of Science, Engineering and Health

Welcome to the 14th Annual Symposium of the School of Science, Engineering and Health. This event continues a strong tradition showcasing student and faculty innovation, creativity and productivity in academic departments largely from within the School of Science, Engineering and Health