Paths, Players, Places: Towards an Understanding of Mazes and Spaces in Videogames

This thesis contributes to the field of academic game studies by reworking and updating the established theories of Espen Aarseth, Janet Murray and Marie-Laure Ryan in understanding the path in videogames. It also draws upon the more recent theoretical discussions of figures such as Jesper Juul, Lev Manovich, Frans Mäyrä and James Newman in order to explore the player’s experience along these paths in the gameworld. By defining a vocabulary of routes through space, the thesis uses the maze in particular as a way of understanding the paths of videogames. The research starts by examining our cultural understanding of the maze within videogames. Various mazes around the UK were walked in order to understand their design and how this may translate into the virtual world of the videogame. The thesis examines the uses of real world mazes through the work of Penelope Doob, and Herman Kern to discuss how the videogame may rework our cultural understanding of the maze due to its increasingly ubiquitous nature. This enables a discussion of maze-paths found within many videogames that are not necessarily categorised by what is often discussed as the maze genre of games. A morphology of maze-paths is devised through comparing the mazes of the real world and the virtual mazes of the videogame. This is achieved by breaking down the maze into separate path types and shows how these paths may link to one another. The thesis argues that the paths of the videogame are generated by the player’s actions. Therefore the focus of this thesis is on the player’s experience along these paths and the objects found at points on them. In acknowledging how to overcome obstacles along the path it is also possible to understand the role of the path in the player’s learning and mastery of the gameworld. This leads to discussions of different types of play experienced by the player in the videogame. Play is separated into what I term purposeful play, being the activities intended by the designer, and appropriated play which is the play formed out of the player’s exploration of the game system. These two terms help to understand player’s incentives for playing along the ruled paths of the gameworld as well as exploring the game’s system further to find new types of play outside of the pre-determined rules. As this thesis is concerned with videogames involving the player’s avatar having a direct relationship with the path, the research also investigates what happens when certain devices break these paths. It was discovered that warp devices reconstruct both temporal and narrative elements within the gamespace, and cause the player’s avatar to temporarily move on tracks through the gameworld. In defining a vocabulary of movement through space on a fixed track, as opposed to a player-determined path, there is a further understanding of the player experience related to each type of route taken in the game. Through an understanding of the maze and defining a vocabulary of maze-paths, tracks and objects found along them, this thesis adds a new contribution to knowledge. It also acknowledges the importance of different types of play within videogames and how these can shape the player experience along the paths of the game

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed

Behavioural and neural insights into the recognition and motivational salience of familiar voice identities

The majority of voices encountered in everyday life belong to people we know, such as close friends, relatives, or romantic partners. However, research to date has overlooked this type of familiarity when investigating voice identity perception. This thesis aimed to address this gap in the literature, through a detailed investigation of voice perception across different types of familiarity: personally familiar voices, famous voices, and lab-trained voices. The experimental chapters of the thesis cover two broad research topics: 1) Measuring the recognition and representation of personally familiar voice identities in comparison with labtrained identities, and 2) Investigating motivation and reward in relation to hearing personally valued voices compared with unfamiliar voice identities. In the first of these, an exploration of the extent of human voice recognition capabilities was undertaken using personally familiar voices of romantic partners. The perceptual benefits of personal familiarity for voice and speech perception were examined, as well as an investigation into how voice identity representations are formed through exposure to new voice identities. Evidence for highly robust voice representations for personally familiar voices was found in the face of perceptual challenges, which greatly exceeded those found for lab-trained voices of varying levels of familiarity. Conclusions are drawn about the relevance of the amount and type of exposure on speaker recognition, the expertise we have with certain voices, and the framing of familiarity as a continuum rather than a binary categorisation. The second topic utilised voices of famous singers and their “super-fans” as listeners to probe reward and motivational responses to hearing these valued voices, using behavioural and neuroimaging experiments. Listeners were found to work harder, as evidenced by faster reaction times, to hear their musical idol compared to less valued voices in an effort-based decision-making task, and the neural correlates of these effects are reported and examined

5th International Conference on Time Perspective

Book of abstract

Remember the magic? How curiosity elicitation and the availability of extrinsic incentives shape memory formation and its neural mechanisms during encoding and early consolidation

While curiosity – the intrinsic desire to know – is a concept central to the human mind and knowledge acquisition, scientific research targeting the understanding of curiosity is still in its infancy and has only recently begun to unravel it. Studies on information-seeking, a popular way to manipulate and measure curiosity in the lab, found that information shows similar rewarding properties as other, extrinsic rewards/incentives like food or money. Indeed, both can motivate behaviour and elicit a response in the dopaminergic structures of the neural reward circuits. The dopaminergic response further enhances encoding of information that is presented around its release by influencing dopamine-dependent cellular mechanisms of learning in the hippocampus. As such, extrinsic rewards/incentives and curiosity motivate and facilitate learning, illustrating their importance in educational contexts and knowledge acquisition. Taken together, their large overlap in neural response and behavioural effects suggests that both may be supported by common neural processes. However, this implies that their combined use would be associated with sub-additive effects. On the other hand, if both were supported by differential neural effects, they could be used in an additive manner. Importantly, the question of how extrinsic rewards/incentives and curiosity interact in their effects on behaviour and cognition overall and memory in particular can only be answered if both effects are studied in conjunction rather than individually as often done in previous research. Another limitation stems from the way how studies thus far have investigated the effects of curiosity on memory, and in some cases, its interaction with extrinsic rewards/incentives, not only because they nearly exclusively all use the same paradigm, but more so because the paradigm itself has some inherent limitations that might affect how curiosity is conceptualised. The present work tries to address these gaps in the literature. In doing so, a new paradigm – the magic trick paradigm – was developed, in which curiosity and the availability of extrinsic incentives were manipulated to measure their effects on encoding. In the magic trick paradigm, curiosity was elicited using short videos of magic tricks. Participants engaged in an orientation task combined with ratings of the “subjective feelings of curiosity” and performance therein was incentivised using a between-subject design. Unbeknown to the participants, their memory for the magic tricks was tested a week later. Crucially, after behavioural pilots, the paradigm was adopted for usage with functional magnetic resonance imaging (fMRI) to be able to investigate the neural underpinnings of incentive- and/or curiosity-motivated incidental learning during encoding as well as early consolidation. To the best of our knowledge, the associated fMRI dataset – the Magic, Memory, and Curiosity (MMC) Dataset – is the first of its kind, making it highly valuable to the nascent field investigating the effects of curiosity on memory because (1) fMRI data was acquired during the magic trick paradigm, but also before and after, allowing to study neural mechanisms underlying encoding as well as early consolidation, and (2) videos of magic tricks as dynamic stimuli allow for a plethora of analysis approaches to answer myriads of research questions. Chapter 2 describes the methods and procedures used to generate the MMC Dataset (N = 50), presented in a way that allows independent researchers to re-use it according to their needs. Additionally, high data quality comparable to other openly available datasets in the field has been demonstrated by performing data quality assessments and basic validation analysis. This further lays the groundwork for Chapters 3 and 4 where the fMRI data acquired during encoding and consolidation, respectively, will be used. In Chapter 3, a meta-analytical approach was used to analyse the behavioural data from three studies (two behavioural studies and one fMRI study) using the magic trick paradigm to investigate the effects of curiosity, the availability of extrinsic incentives, and their interaction on memory. The main memory outcome was high-confidence recognition, a recollection-based memory measurement, but other indices were also examined to derive a more detailed picture. This revealed positive effects of curiosity and monetary incentives on encoding, in the absence of interaction effects. Exploratory analyses further showed that curiosity and monetary incentives might impact encoding differently, overall suggesting that the effects might be at least partially non-overlapping. Analysing the fMRI data acquired during the presentation of magic tricks using the intersubject synchronisation framework to account for the dynamic nature of the stimuli, we found that while the effects of curiosity on memory were located in the hippocampus and dopaminergic brain areas, neither the effects of curiosity nor incentives themselves were found in the often-implicated reward network, but instead were associated with regions involved in processing uncertainly and attention. Likewise, the effects of curiosity on memory spread further across broad cortical and subcortical networks. Overall, this suggests that the subjective feeling of curiosity and its effects on memory recruits broad brain networks when investigated with dynamic stimuli, caveating a too narrow focus on a small list of regions-of-interest while there is yet so much more to be learned about the effects of curiosity on memory. In Chapter 4, resting-state data acquired before and after learning was used to investigate changes in brain activity at rest following learning. The pre-learning rest data can be used as a baseline, allowing any changes from pre- to post-learning to be attributed to the learning experience itself. Because previous research has repeatedly pointed to similarities between extrinsic rewards/incentives and curiosity, our analysis focused on the change in resting-state functional connectivity between the dopaminergic midbrain and the anterior hippocampus, a dopaminergic consolidation mechanism previously reported in the context of extrinsically motivated learning. Contrary to our hypothesis, we did not find an overall change nor that individual differences therein predicted behavioural measures of learning. However, brain-behaviour correlations differed significantly depending on the availability of extrinsic incentives. In sum, this suggests that curiosity-motivated learning might be supported by different consolidation mechanisms compared to extrinsically motivated learning and that extrinsic motivation could re-configure resting-state networks supporting early consolidation. Overall, this work adds to the literature by replicating the effects of curiosity on encoding. More importantly, however, this work suggests that the systems supporting extrinsically and curiosity-motivated learning might differ more than previously assumed, especially when investigating activity across the whole brain rather than focusing on a priori candidate regions implicated in dopaminergic effects. Indeed, our results allow for the possibility that other neurotransmitter play a role as well in extrinsically and curiosity-motivated learning, further highlighting the need for more research in the area

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)