The effects of graphical fidelity on player experience

Graphical assets in video games have become increasingly complex over the years, but little is known about their effect on player experience (PX). In this paper, we present results of a controlled study with 48 participants comparing how abstract and stylized graphics influence player experience in casual games. Our results show that high-fidelity graphics result in a more positive impression of the game. However, we also show that many effects are only present in the game with a more challenging mechanic. This shows that casual games can be compelling and enjoyable to play despite simplistic graphics, suggesting that small game developers and researchers need not focus on elaborate visuals to engage players. Copyright © 2013 ACM

Lagrangian Coherent Structures: A Climatological Look

A relatively new area at the crossroads of fluid and nonlinear dynamics are objects known as Lagrangian Coherent Structures (LCSs). LCSs are mathematically classified to differentiate parts of fluid flows. They, themselves, are the most influential parts of fluids. These objects have the most influence on the fluids around them and they allow for a sense of hierarchy in an otherwise busy environment of endless variables and trajectories. While all particles of fluids have the same dynamics on an individual basis, areas of fluid are not created equal and to be able to detect which parts will be the most important to look at allows for easier, but just as accurate, prediction of fluid movement. Recent applications include cleanup operations during the BP Deepwater Horizon oil spill, pollutant transfer in oceanic basins, and the analysis of polar storm activity. This thesis explores LCSs from the discrete mathematics to the future climatological impacts using virtual fluid simulations and LCS detection tools to facilitate analysis as well as diving into a case study with real and unapproximated oceanic data in the days following the Fukushima Daiichi nuclear power plant disaster

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

A psychology literature study on modality related issues for multimodal presentation in crisis management

The motivation of this psychology literature study is to obtain modality related guidelines for real-time information presentation in crisis management environment. The crisis management task is usually companied by time urgency, risk, uncertainty, and high information density. Decision makers (crisis managers) might undergo cognitive overload and tend to show biases in their performances. Therefore, the on-going crisis event needs to be presented in a manner that enhances perception, assists diagnosis, and prevents cognitive overload. To this end, this study looked into the modality effects on perception, cognitive load, working memory, learning, and attention. Selected topics include working memory, dual-coding theory, cognitive load theory, multimedia learning, and attention. The findings are several modality usage guidelines which may lead to more efficient use of the user’s cognitive capacity and enhance the information perception

Investigating the effectiveness of animations in exploring learning a case study in a Chemical Engineering course

Includes abstract.Includes bibliographical references.Using technology in the classroom environment has become increasingly popular among educators. One way of employing technology is using instructional animations to teach concepts, favoured owing to their ability to depict changes in object over time. Animations are commonly believed to increase motivation and foster learning, but there is little empirical evidence for this belief. Some researchers have found that animations can be effective; others, however, show that animations have the same effect as a combination of static pictures and text. Some have even showed that animations could actually have negative effects on student learning. A lecturer at the University of Cape Town had planned to use animations in his third year undergraduate Chemical Engineering Course in Reactor Design. This became the context for the present study which investigated the effectiveness of these animations for promoting conceptual understanding as well as exploring students' perspective on learning from animations as well as students' enjoyment level. A quasi-experimental case study was conducted over four topics in Reactor Design and one topic was repeated. Each investigation was on one topic, and in each investigation, the Reactor Design class was split so that the student either attended a traditional lecture or an animation lecture. The two groups of students were used to compare the impact of animations on student learning

Learning STEM Through Integrative Visual Representation

Previous cognitive models of memory have not comprehensively taken into account the internal cognitive load of chunking isolated information and have emphasized the external cognitive load of visual presentation only. Under the Virk Long Term Working Memory Multimedia Model of cognitive load, drawing from the Cowan model, students presented with integrated animations of the key neural signal transmission subcomponents where the interrelationships between subcomponents are visually and verbally explicit, were hypothesized to perform significantly better on free response and diagram labeling questions, than students presented with isolated animations of these subcomponents. This is because the internal attentional cognitive load of chunking these concepts is greatly reduced and hence the overall cognitive load is less for the integrated visuals group than the isolated group, despite the higher external load for the integrated group of having the interrelationships between subcomponents presented explicitly. Experiment 1 demonstrated that integrating the subcomponents of the neuron significantly enhanced comprehension of the interconnections between cellular subcomponents and approached significance for enhancing recall of the layered molecular correlates of the cellular structures and their interconnections. Experiment 2 corrected time on task confounds from Experiment 1 and focused on the cellular subcomponents of the neuron only. Results from the free response essay subcomponent subscores did demonstrate significant differences in favor of the integrated group as well as some evidence from the diagram labeling section. Results from free response, short answer and What-If (problem solving), and diagram labeling detailed interrelationship subscores demonstrated the integrated group did indeed learn the extra material they were presented with. This data demonstrating the integrated group learned the extra material they were presented with provides some initial support for the assertion that chunking mediated the greater gains in learning for the neural subcomponent concepts over the control