Statistical graphs are becoming common in many fields. Modern authoring tools offer easy ways to create complex graphs. Despite multiple sources of advice, graph designers have little objective evidence on which to base design decisions to alleviate complexity. We aimed to fill a gap in the evidence of the difficulty created by two design features: adding one data series to a simple graph or adding dependent axes to a graph. We found main effects of adding a data series, of a greater number of data points, and an additional dependent axis, but primarily on the time readers studied the graph. Via deeper analysis, we explained the difficulty graph readers experienced when answering questions. That explanation focuses on the selection of points from a source graph to be part of the query. Based on our results, we recommend avenues for research on graph complexity and graph comprehension measurement.

In theory, visual saliency in a graph should be used to draw attention to its most important component(s). Thus salience is commonly viewed both as a basis for predicting where graph readers are likely to look, and as a core design technique for emphasizing what a reader is intended to see among competing elements in a given chart or plot. We briefly review models, metrics, and applicable theories as they pertain to graphs. We then introduce new saliency models based on perceptual and cognitive theories that, to our knowledge, have not been previously applied to models for viewing statistical graphics. The resulting frameworks can be broadly classified as bottom-up perceptual models or top-down cognitive models. We report the results of evaluating these new theory-informed approaches on gaze data collected for statistical graphs and for more general information visualizations. Interestingly, the new models fare no better than previous ones. We review the experience, noting why we expected these hypotheses to be effective, and discuss how and why their performance did not match our aspirations. We suggest directions for future research that may help to clarify some of the issues raised.

Effective communication with a mobile robot using speech is a difficult problem even when you can control the auditory scene. Robot self-noise or ego noise, echoes and reverberation, and human interference are all common sources of decreased intelligibility. Moreover, in real-world settings, these problems are routinely aggravated by a variety of sources of background noise. Military scenarios can be punctuated by high decibel noise from materiel and weaponry that would easily overwhelm a robot's normal speaking volume. Moreover, in nonmilitary settings, fans, computers, alarms, and transportation noise can cause enough interference to make a traditional speech interface unusable. This work presents and evaluates a prototype robotic interface that uses perspective taking to estimate the effectiveness of its own speech presentation and takes steps to improve intelligibility for human listeners.

Effective communication with a mobile robot using speech is a difficult problem even when you can control the auditory scene. Robot ego-noise, echoes, and human interference are all common sources of decreased intelligibility. In real-world environments, however, these common problems are supplemented with many different types of background noise sources. For instance, military scenarios might be punctuated by high decibel plane noise and bursts from weaponry that mask parts of the speech output from the robot. Even in non-military settings, however, fans, computers, alarms, and transportation noise can cause enough interference that they might render a traditional speech interface unintelligible. In this work, we seek to overcome these problems by applying robotic advantages of sensing and mobility to a text-to-speech interface. Using perspective taking skills to predict how the human user is being affected by new sound sources, a robot can adjust its speaking patterns and/or reposition itself within the environment to limit the negative impact on intelligibility, making a speech interface easier to use.

Auditory perspective taking is the process of imagining the auditory scene from another's place and inferring what that person can (and cannot) hear, as well as how this affects his or her auditory comprehension. With this inferred knowledge, a conversational partner can then adapt his or her vocal presentation to overcome or cope with competing sounds and other auditory challenges to ensure that what is being said can be understood. In this poster, we explore several aspects of auditory perspective taking in the context of a robot speech and listening interface.

How do children learn how to play hide and seek? At age 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. We show through a case study that a 3 1/2 year old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. We propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. We model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. We also take the "hiding" model and use it as the basis for a "seeking" model. We suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person.

In conversation, people often use spatial relationships to describe their environment, e.g., "There is a desk in front of me and a doorway behind it," and to issue directives, e.g., "go around the desk and through the doorway." In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multimodal robot interface. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog. Examples using spatial language are included for both robot-to-human feedback and also human-to-robot commands. We also discuss some linguistic consequences in the semantic representations of spatial and locative information based on this work.

In our research on intuitive means for humans and intelligent, mobile robots to collaborate, we use a multimodal interface that supports speech and gestural inputs. As a preliminary step to evaluate our approach and to identify practical areas for future work, we conducted a wizard-of-Oz pilot study with five participants who each collaborated with a robot on a search task in a separate room. The goal was to find a sign in the robot's environment with the word "FOO" printed on it. Using a subset of our multimodal interface, participants were told to direct the collaboration. As their subordinate, the robot would understand their utterances and gestures, and recognize objects and structures in the search space. Participants conversed with the robot through a wireless microphone and headphone and, for gestural input, used a touch screen displaying alternative views of the robot's environment to indicate locations and objects.

Users of low data rate voice coders now demand good speaker recognition in addition to quality and intelligibility. Speaker recognizability was one of the selection criteria for the DoD Digital Voice Processor Consortium in selecting a new standard 2400 bps algorithm. We have developed a speaker recognition test based on same-different judgments for pairs of sentences spoken by 10 male and 10 female speakers. Preliminary experiments showed that the sensitivity of the test is similar to that of currently used acceptability and intelligibility tests. Male and female talkers produced somewhat different rank orderings, but averaging over males/females gave results that "make sense" when compared with intelligibility and acceptability scores for the same coders. The results of these experiments determined the speaker recognition test procedures that were used in the 2400 bps coder final selection process.