Two recent studies testing navigation of rats in swimming pools have posed problems for any account of the use of purely geometric properties of space in navigation (M. Graham, M. A. Good, A. McGregor, & J. M. Pearce, 2006; J. M. Pearce, M. A. Good, P. M. Jones, & A. McGregor, 2004). The authors simulated 1 experiment from each study in a virtual reality environment to test whether experimental results could be explained by view-based navigation. The authors recorded a reference image at the target location and then determined global panoramic image differences between this image and images taken at regularly spaced locations throughout the arena. A formal model, in which an agent attempts to minimize image differences between the reference image and current views, generated trajectories that could be compared with the search performance of rats. For both experiments, this model mimics many aspects of rat behavior. View-based navigation provides a sufficient and parsimonious explanation for a range of navigational behaviors of rats under these experimental conditions.
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http://dx.doi.org/10.1037/0097-7403.34.1.15 | DOI Listing |
J Comp Physiol A Neuroethol Sens Neural Behav Physiol
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Brain Research Institute, University of Zurich, Zurich, Switzerland.
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School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia.
Arch Bone Jt Surg
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Department of Orthopedics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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March 2024
Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs.
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School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia.
Solitarily foraging ant species differ in their reliance on their two primary navigational systems- path integration and visual learning. Despite many species of Australian bull ants spending most of their foraging time on their foraging tree, little is known about the use of these systems while climbing. "Rewinding" displacements are commonly used to understand navigational system usage, and work by introducing a mismatch between these navigational systems, by displacing foragers after they have run-down their path integration vector.
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