In search of the unknown: Artificial intelligence and foraging
Nathan Wispinski & Paulo Bruno Serafim · University of Alberta & Gran Sasso Science Institute
Tue, Jul 11, 2023 · 05:00 UTC
Nathan Wispinski & Paulo Bruno Serafim · University of Alberta & Gran Sasso Science Institute
Tue, Jul 11, 2023 · 05:00 UTC
Anna Stöckl & Michael Harrap · University of Konstanz & University of Oxford
Tue, May 23, 2023 · 05:00 UTC
Matthew Apps & Aaron Bornstein · University of Birmingham & University of California, Irvine
Tue, May 9, 2023 · 05:00 UTC
Eleanor Caves & Vivienne Foroughirad · University of California, Santa Barbara & Georgetown University
Tue, Apr 18, 2023 · 05:00 UTC
Sasha Dall & Damien Farine · University of Exeter & Max Planck Institute of Animal Behavior
Tue, Mar 21, 2023 · 07:00 UTC
Barbara Webb · University of Edinburgh
Tue, Mar 14, 2023 · 06:00 UTC
Many insect species maintain a nest around which their foraging behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions tha
Lucia Jacobs & Hannah Payne · University of California Berkeley, Columbia University
Tue, May 17, 2022 · 05:00 UTC
David Barack & Thomas Hills · University of Pennsylvania, University of Warwick
Tue, May 10, 2022 · 05:00 UTC
Cindy Poo & Pauline Fleischmann · Champalimaud Center for the Unknown & University of Würzburg
Tue, Apr 19, 2022 · 05:00 UTC
Holger Goerlitz & Abel Corver · Max Planck Institute for Biological Intelligence & Johns Hopkins
Tue, Apr 12, 2022 · 05:00 UTC
Nathaniel Daw · Princeton University
Wed, Mar 30, 2022 · 05:00 UTC
A key insight from the foraging literature is the importance of assessing the overall environmental quality — via global reward rate or similar measures, which capture the opportunity cost of time and can guide behavioral allocation toward relatively richer options. Meanwhile, the majority of research in decision neuroscience and computational psychiatry has focused instead on how choices are guided by much more local, event-locked evaluations: of individual situations, actions, or outcomes. I review a combination of research and theoretical speculation from my lab and others that emphasizes t
Alexandra Rosati & Ben Hayden · University of Michigan & University of Minnesota
Tue, May 25, 2021 · 05:00 UTC
Venkatesh Murthy & Thierry Emonet · Harvard University & Yale University
Tue, May 11, 2021 · 05:00 UTC
Audrey Dussutour & Rong Gong · CNRS & HHMI Janelia Research Campus
Tue, Apr 20, 2021 · 05:00 UTC
Ahmed El Hady & Nils Kolling · Princeton University & University of Oxford
Tue, Apr 13, 2021 · 05:00 UTC
Susan Healy & Lars Chittka · University of St. Andrews; Queen Mary, University of London
Tue, Mar 30, 2021 · 05:00 UTC
Tue, Mar 16, 2021 · 07:00 UTC
Foraging research aims at describing, understanding, and predicting resource-gathering behaviour. Optimal Foraging Theory (OFT) is a sub-discipline that emphasises that these aims can be aided by segmenting foraging behaviour into discrete problems that can be formally described and examined with mathematical maximization techniques. Examples of such segmentation are found in the isolated treatment of issues such as patch residence time, prey selection, information gathering, risky choice, intertemporal decision making, resource allocation, competition, memory updating, group structure, and so
Duda Kvitsiani · Aarhus University
Wed, Jul 29, 2020 · 13:30 UTC
Understanding what drives foraging decisions in animals requires careful manipulation of the value of available options while monitoring animal choices. Value-based decision-making tasks, in combination with formal learning models, have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is even though fruit flies have served as a model organism for many complex behavioural paradigms. To fill
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