Weighted Integration of Landmarks in a One-Dimensional Spatial Search Task

2014 ◽  
Author(s):  
Yu Du ◽  
Neil McMillan ◽  
Christopher Madan ◽  
Marcia Spetch
2014 ◽  
Author(s):  
Neil McMillan ◽  
Pierre Nadeau-Marchand ◽  
Yu Du ◽  
Christopher R. Madan ◽  
Marcia L. Spetch

2010 ◽  
Vol 36 (1) ◽  
pp. 92-109 ◽  
Author(s):  
Patrizia Potì ◽  
Patricia Kanngiesser ◽  
Martina Saporiti ◽  
Alessandra Amiconi ◽  
Bettina Bläsing ◽  
...  

2014 ◽  
Vol 42 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Chad M. Ruprecht ◽  
Joshua E. Wolf ◽  
Nina I. Quintana ◽  
Kenneth J. Leising

2013 ◽  
Author(s):  
Kenneth J. Leising ◽  
Chad M. Ruprecht ◽  
Josh E. Wolf ◽  
Nina Quintana

Author(s):  
S. Borhaninejad ◽  
F. Hakimpour ◽  
E. Hamzei

Nowadays the selective access to information on the Web is provided by search engines, but in the cases which the data includes spatial information the search task becomes more complex and search engines require special capabilities. The purpose of this study is to extract the information which lies in spatial documents. To that end, we implement and evaluate information extraction from GML documents and a retrieval method in an integrated approach. Our proposed system consists of three components: crawler, database and user interface. In crawler component, GML documents are discovered and their text is parsed for information extraction; storage. The database component is responsible for indexing of information which is collected by crawlers. Finally the user interface component provides the interaction between system and user. We have implemented this system as a pilot system on an Application Server as a simulation of Web. Our system as a spatial search engine provided searching capability throughout the GML documents and thus an important step to improve the efficiency of search engines has been taken.


2020 ◽  
Vol 30 (6) ◽  
pp. 3632-3643
Author(s):  
Gabriel Pelletier ◽  
Lesley K Fellows

Abstract Whether you are a gazelle bounding to the richest tract of grassland or a return customer heading to the freshest farm stand at a crowded market, the ability to learn the value of spatial locations is important in adaptive behavior. The ventromedial frontal lobe (VMF) is implicated in value-based decisions between objects and in flexibly learning to choose between objects based on feedback. However, it is unclear if this region plays a material-general role in reward learning. Here, we tested whether VMF is necessary for learning the value of spatial locations. People with VMF damage were compared with healthy participants and a control group with frontal damage sparing VMF in an incentivized spatial search task. Participants chose among spatial targets distributed among distractors, rewarded with an expected value that varied along the right-left axis of the screen. People with VMF damage showed a weaker tendency to reap reward in contralesional hemispace. In some individuals, this impairment could be dissociated from the ability to make value-based decisions between objects, assessed separately. This is the first evidence that the VMF is critically involved in reward-guided spatial search and offers a novel perspective on the relationships between value, spatial attention, and decision-making.


Sign in / Sign up

Export Citation Format

Share Document