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An Examination of Print and Web Graphics’ Effect on Readers’ Recall Specific research regarding how information graphics, which are defined as charts, maps or diagrams, in print and online affect readers’ recall and understanding of information is scarce at best and at times, even contradictory. Previous print research suggests that information graphics does improve readers’ recall. Online graphics research notes that the freedom of the Web allows readers to learn more efficiently than traditional media. While, other research notes that the Web’s freedom causes users to spend more effort on orienting themselves with the Web site, and therefore, users don’t learn efficiently. This study examined whether the presentation of information graphics affects readers’ recall and found no statistical significance between readers’ recall scores who had read a print graphic and those who read an online graphic. / Department of Journalism

Thesis (M.A.)--Ball State University, 2008. / Title from PDF t.p. (viewed on Sept. 09, 2009). Includes bibliographical references (p. 31-34).

This study investigated the predictive value of time on-task and error scores on tests administered through Control Data Corporation PLATO interactive computer graphics simulation as predictors of errors and time on-task for inertial navigation system equipment operation. In addition, the correlation between simulated pass/fail error and time on-task scores, and subsequent pass/fail criteria using actual equipment was investigated.

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The goal of this thesis is to synthesize believable motions of a character interacting with its surroundings and manipulating objects through physical contacts and forces. Human-like autonomous avatars are in increasing demand in areas such as entertainment, education, and health care. Yet modeling the basic human motor skills of locomotion and manipulation remains a long-standing challenge in animation research. The seemingly simple tasks of navigating an uneven terrain or grasping cups of different shapes involve planning with complex kinematic and physical constraints as well as adaptation to unexpected perturbations. Moreover, natural movements exhibit unique personal characteristics that are complex to model. Although motion capture technologies allow virtual actors to use recorded human motions in many applications, the recorded motions are not directly applicable to tasks involving interactions for two reasons. First, the acquired data cannot be easily adapted to new environments or different tasks goals. Second, acquisition of accurate data is still a challenge for fine scale object manipulations. In this work, we utilize data to create natural looking animations, and mitigate data deficiency with physics-based simulations and numerical optimizations. We develop algorithms based on a single reference motion for three types of control problems. The first problem focuses on motions without contact constraints. We use joint torque patterns identified from the captured motion to simulate responses and recovery of the same style under unexpected pushes. The second problem focuses on locomotion with foot contacts. We use contact forces to control an abstract dynamic model of the center of mass, which sufficiently describes the locomotion task in the input motion. Simulation of the abstract model under unexpected pushes or anticipated changes of the environment results in responses consistent with both the laws of physics and the style of the input. The third problem focuses on fine scale object manipulation tasks, in which accurate finger motions and contact information are not available. We propose a sampling method to discover contact relations between the hand and the object from only the gross motion of the wrists and the object. We then use the abundant contact constraints to synthesize detailed finger motions. The algorithm creates finger motions of various styles for a diverse set of object shapes and tasks, including ones that are not present at capture time. The three algorithms together control an autonomous character with dexterous hands to interact naturally with a virtual world. Our methods are general and robust across character structures and motion contents when testing on a wide variety of motion capture sequences and environments. The work in this thesis brings closer the motor skills of a virtual character to its human counterpart. It provides computational tools for the analysis of human biomechanics, and can potentially inspire the design of novel control algorithms for humanoid robots.

One of the common research goals in disciplines such as computer graphics and robotics is to understand the subtleties of human motion and develop tools for recreating natural and meaningful motion. Physical simulation of virtual human characters is a promising approach since it provides a testbed for developing and testing control strategies required to execute various human behaviors. Designing generic control algorithms for simulating a wide range of human activities, which can robustly adapt to varying physical environments, has remained a primary challenge. This dissertation introduces methods for generic and robust control of virtual characters in an interactive physical environment. Our approach is to use the information of the physical contacts between the character and her environment in the control design. We leverage high-level knowledge of the kinematics goals and the interaction with the surroundings to develop active control strategies that robustly adapt to variations in the physical scene. For synthesizing intentional motion requiring long-term planning, we exploit properties of the physical model for creating efficient and robust controllers in an interactive framework. The control design leverages the reference motion capture data and the contact information with the environment for interactive long-term planning. Finally, we propose a compact soft contact model for handling contacts for rigid body virtual characters. This model aims at improving the robustness of existing control methods without adding any complexity to the control design and opens up possibilities for new control algorithms to synthesize agile human motion.