Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

The modularity of aesthetic processing and perception in the human brain. Functional neuroimaging studies of neuroaesthetics.

By taking advantage of the advent of functional Magnetic Resonance Imaging (fMRI) this thesis argues that aesthetics belongs in the domain of neurobiology by investigating the different brain processes that are implicated in aesthetic perception from two perspectives. The first experiment explores a specific artistic style that has stressed the problem in the relationship between objects and context. This study investigates the neural responses associated with changes in visual perception, as when objects are placed in their normal context versus when the object-context relationship is violated. Indeed, an aim of this study was to cast a new light on this specific artistic style from a neuroscientific perspective. In contrast to basic rewards, which relate to the reproduction of the species, the evolution of abstract, cognitive representations facilitates the use of a different class of rewards related to hedonics. The second part investigates the hedonic processes involved in aesthetic judgments in order to explore if such higher order cognitive rewards use the same neural reward mechanism as basic rewards. In the first of these experiments we modulate the extent to which the neural correlates of aesthetic preference vary as a function of expertise in architecture. In the second experiment we aim to measure the more general effects of labelling works of art with cognitive semantic information in order to explore the neural modulation of aesthetic preference relative to this information. The main finding of this thesis is that stimulus affective value is represented separately in OFC, with positive reward (increasing aesthetic judgments) being represented in medial OFC and negative reward value is being represented in lateral OFC. Furthermore ventral striatum encode reward expectancy and the predictive value of a stimulus. These findings suggest a dissociation of reward processing with separate neural substrates in reward expectancy and stimulus affective value

Modeling Visual Rhetoric and Semantics in Multimedia

Recent advances in machine learning have enabled computer vision algorithms to model complicated visual phenomena with accuracies unthinkable a mere decade ago. Their high-performance on a plethora of vision-related tasks has enabled computer vision researchers to begin to move beyond traditional visual recognition problems to tasks requiring higher-level image understanding. However, most computer vision research still focuses on describing what images, text, or other media literally portrays. In contrast, in this dissertation we focus on learning how and why such content is portrayed. Rather than viewing media for its content, we recast the problem as understanding visual communication and visual rhetoric. For example, the same content may be portrayed in different ways in order to present the story the author wishes to convey. We thus seek to model not only the content of the media, but its authorial intent and latent messaging. Understanding how and why visual content is portrayed a certain way requires understanding higher level abstract semantic concepts which are themselves latent within visual media. By latent, we mean the concept is not readily visually accessible within a single image (e.g. right vs left political bias), in contrast to explicit visual semantic concepts such as objects. Specifically, we study the problems of modeling photographic style (how professional photographers portray their subjects), understanding visual persuasion in image advertisements, modeling political bias in multimedia (image and text) news articles, and learning cross-modal semantic representations. While most past research in vision and natural language processing studies the case where visual content and paired text are highly aligned (as in the case of image captions), we target the case where each modality conveys complementary information to tell a larger story. We particularly focus on the problem of learning cross-modal representations from multimedia exhibiting weak alignment between the image and text modalities. A variety of techniques are presented which improve modeling of multimedia rhetoric in real-world data and enable more robust artificially intelligent systems