Using 'virtual reality'-techniques to dissect important characteristics of highspeed decision-making in archerfish
Using 'virtual reality'-techniques to dissect important characteristics of highspeed decision-making in archerfish
批准号:
392737747
负责人:
Professor Dr. Stefan Schuster
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2017
资助国家:
德国
项目状态:
已结题
起止时间:
2016-12-31 至 2023-12-31
中文摘要
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英文摘要
The project aims to exploit a recently established technique to address a number of exciting but previously inaccessible questions about the rapid start-decisions of hunting archerfish. After having dislodged prey with a well-aimed shot these fish very rapidly decide to launch a start manoeuvre that turns the fish right towards were their prey is later going to land and pushes it off with just the right speed to arrive simultaneously with its prey. Surprisingly, information sampled in just 40 ms of the initial falling motion is all that the fish needs for its decision. Over the past years it turned out that the archerfish's start-decisions combine enormous speed with impressive complexity. The open questions we would like to solve in appropriately designed behavioural experiments are these: Is it really possible that archerfish can design a complete detour path, that optimally guides them past obstacles, in just 100 ms? How can they so rapidly and with sufficient precision access internal maps to feed obstacle-related information into their start-decisions? On the basis of what information can archerfish buffer their 'open-loop' starts against potential later disturbances of their prey's path? What is the cue that allows the fish to monocularly and in just 40 ms detect initial distance and vertical speed of their prey? What kind of 'feedback' allows the fish to evaluate if their past decisions were appropriate or systematically wrong? How many initial-value combinations can the decision-making process handle independently? A newly established technique brings all these questions within reach. The unusual combination of complexity and speed in the archerfish decisions will make the findings particularly interesting. They will be relevant for the fundamental question of what types of networks are really needed to drive complex behaviour and they will directly lay the foundation for a direct examination of the processing in the network that elicits the starts. Appreciating that speed and complexity can go together in remarkable ways questions concepts of human decision-making but could also be exploited in technical systems.
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