Figure 7) ridges/valleys and suggestive contours. Overlaid are representative "gauges" (discs revealing the surface normal) oriented on the images by people in the study, colored by how far they deviate from the ground truth. AbstractThis paper investigates the ability of sparse line drawings to depict 3D shape. We perform a study in which people are shown an image of one of twelve 3D objects depicted with one of six styles and asked to orient a gauge to coincide with the surface normal at many positions on the object's surface. The normal estimates are compared with each other and with ground truth data provided by a registered 3D surface model to analyze accuracy and precision. The paper describes the design decisions made in collecting a large data set (275,000 gauge measurements) and provides analysis to answer questions about how well people interpret shapes from drawings. Our findings suggest that people interpret certain shapes almost as well from a line drawing as from a shaded image, that current computer graphics line drawing techniques can effectively depict shape and even match the effectiveness of artist's drawings, and that errors in depiction are often localized and can be traced to particular properties of the lines used. The data collected for this study will become a publicly available resource for further studies of this type.

Figure 7) ridges/valleys and suggestive contours. Overlaid are representative "gauges" (discs revealing the surface normal) oriented on the images by people in the study, colored by how far they deviate from the ground truth. AbstractThis paper investigates the ability of sparse line drawings to depict 3D shape. We perform a study in which people are shown an image of one of twelve 3D objects depicted with one of six styles and asked to orient a gauge to coincide with the surface normal at many positions on the object's surface. The normal estimates are compared with each other and with ground truth data provided by a registered 3D surface model to analyze accuracy and precision. The paper describes the design decisions made in collecting a large data set (275,000 gauge measurements) and provides analysis to answer questions about how well people interpret shapes from drawings. Our findings suggest that people interpret certain shapes almost as well from a line drawing as from a shaded image, that current computer graphics line drawing techniques can effectively depict shape and even match the effectiveness of artist's drawings, and that errors in depiction are often localized and can be traced to particular properties of the lines used. The data collected for this study will become a publicly available resource for further studies of this type.

No abstract

The interpretation of other agents as intentional actors equipped with mental states has been connected to the attribution of rationality to their behavior. But a workable definition of "rationality" is difficult to formulate in complex situations, where standard normative definitions are difficult to apply. In this study, we explore a notion of rationality based on the idea of evolutionary fitness. We ask whether agents that are more adapted to their environment are, consequently, perceived as more rational and intentional. We created a 2-D virtual environment populated with autonomous virtual agents, each of which behaves according to a built-in program equipped with simulated perception, memory, and decision making. We then introduced a process of simulated evolution that pressured the agents' programs toward behavior more adapted to the simulated environment. We showed these agents to human subjects in 2 experiments, in which we respectively asked them to judge their intelligence and to dynamically estimate their "mental states." The results confirm that subjects construed evolved agents as more intelligent, and judged evolved agents' mental states more accurately, relative to nonevolved agents. These results corroborate a view that the interpretation of agent behavior is connected to a concept of rationality based on the apparent fit between an agent's actions and its environment.

Comprehension of goal-directed, intentional motion is an important but understudied visual function. To study it, we created a two-dimensional virtual environment populated by independently-programmed autonomous virtual agents, which navigate the environment, collecting food and competing with one another. Their behavior is modulated by a small number of distinct "mental states": exploring, gathering food, attacking, and fleeing. In two experiments, we studied subjects' ability to detect and classify the agents' continually changing mental states on the basis of their motions and interactions. Our analyses compared subjects' classifications to the ground truth state occupied by the observed agent's autonomous program. Although the true mental state is inherently hidden and must be inferred, subjects showed both high validity (correlation with ground truth) and high reliability (correlation with one another). The data provide intriguing evidence about the factors that influence estimates of mental state-a key step towards a true "psychophysics of intention."