Spatial scaling in human peripheral vision

The observation that performance in many visual tasks can be made independent of eccentricity by increasing the size of peripheral stimuli according to the cortical magnification factor has dominated studies of peripheral vision for many years. However, it has become evident that the cortical magnification factor cannot be successfully applied to all tasks. To find out why, several tasks were studied using spatial scaling, a method which requires no pre-determined scaling factors (such as those predicted from cortical magnification) to magnify the stimulus at any eccentricity. Instead, thresholds are measured at the fovea and in the periphery using a series of stimuli, all of which are simply magnified versions of one another. Analysis of the data obtained in this way reveals the value of the parameter E2, the eccentricity at which foveal stimulus size must double in order to maintain performance equivalent to that at the fovea. The tasks investigated include hyperacuities (vernier acuity, bisection acuity, spatial interval discrimination, referenced displacement detection, and orientation discrimination), unreferenced instantaneous and gradual movement, flicker sensitivity, and face discrimination. In all cases tasks obeyed the principle of spatial scaling since performance in the periphery could be equated to that at the fovea by appropriate magnification. However, E2 values found for different spatial tasks varied over a 200-fold range. In spatial tasks (e.g. bisection acuity and spatial interval discrimination) E2 values were low, reaching about 0.075 deg, whereas in movement tasks the values could be as high as 16 deg. Using a method of spatial scaling it has been possible to equate foveal and peripheral perfonnance in many diverse visual tasks. The rate at which peripheral stimulus size had to be increased as a function of eccentricity was dependent upon the stimulus conditions and the task itself. Possible reasons for these findings are discussed.

Scaling up question-answering to linked data

Linked Data semantic sources, in particular DBpedia, can be used to answer many user queries. PowerAqua is an open multi-ontology Question Answering (QA) system for the Semantic Web (SW). However, the emergence of Linked Data, characterized by its openness, heterogeneity and scale, introduces a new dimension to the Semantic Web scenario, in which exploiting the relevant information to extract answers for Natural Language (NL) user queries is a major challenge. In this paper we discuss the issues and lessons learned from our experience of integrating PowerAqua as a front-end for DBpedia and a subset of Linked Data sources. As such, we go one step beyond the state of the art on end-users interfaces for Linked Data by introducing mapping and fusion techniques needed to translate a user query by means of multiple sources. Our first informal experiments probe whether, in fact, it is feasible to obtain answers to user queries by composing information across semantic sources and Linked Data, even in its current form, where the strength of Linked Data is more a by-product of its size than its quality. We believe our experiences can be extrapolated to a variety of end-user applications that wish to scale, open up, exploit and re-use what possibly is the greatest wealth of data about everything in the history of Artificial Intelligence. © 2010 Springer-Verlag.