Dataset Information

Landmark navigation in a mantis shrimp.

ABSTRACT: Mantis shrimp commonly occupy burrows in shallow, tropical waters. These habitats are often structurally complex where many potential landmarks are available. Mantis shrimp of the species Neogonodactylus oerstedii return to their burrows between foraging excursions using path integration, a vector-based navigational strategy that is prone to accumulated error. Here, we show that N. oerstedii can navigate using landmarks in parallel with their path integration system, correcting for positional uncertainty generated when navigating using solely path integration. We also report that when the path integration and landmark navigation systems are placed in conflict, N. oerstedii will orientate using either system or even switch systems enroute. How they make the decision to trust one navigational system over another is unclear. These findings add to our understanding of the refined navigational toolkit N. oerstedii relies upon to efficiently navigate back to its burrow, complementing its robust, yet error prone, path integration system with landmark guidance.

SUBMITTER: Patel RN

PROVIDER: S-EPMC7657848 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Landmark navigation in a mantis shrimp.

Patel Rickesh N RN Cronin Thomas W TW

Proceedings. Biological sciences 20201007 1936

Mantis shrimp commonly occupy burrows in shallow, tropical waters. These habitats are often structurally complex where many potential landmarks are available. Mantis shrimp of the species <i>Neogonodactylus oerstedii</i> return to their burrows between foraging excursions using path integration, a vector-based navigational strategy that is prone to accumulated error. Here, we show that <i>N. oerstedii</i> can navigate using landmarks in parallel with their path integration system, correcting for ...[more]

PMID: 33023415

Similar Datasets

Project description:Memories of places often include landmark cues, i.e., information provided by the spatial arrangement of distinct objects with respect to the target location. To study how humans combine landmark information for navigation, we conducted two experiments: To this end, participants were either provided with auditory landmarks while walking in a large sports hall or with visual landmarks while walking on a virtual-reality treadmill setup. We found that participants cannot reliably locate their home position due to ambiguities in the spatial arrangement when only one or two uniform landmarks provide cues with respect to the target. With three visual landmarks that look alike, the task is solved without ambiguity, while audio landmarks need to play three unique sounds for a similar performance. This reduction in ambiguity through integration of landmark information from 1, 2, and 3 landmarks is well modeled using a probabilistic approach based on maximum likelihood estimation. Unlike any deterministic model of human navigation (based e.g., on distance or angle information), this probabilistic model predicted both the precision and accuracy of the human homing performance. To further examine how landmark cues are integrated we introduced systematic conflicts in the visual landmark configuration between training of the home position and tests of the homing performance. The participants integrated the spatial information from each landmark near-optimally to reduce spatial variability. When the conflict becomes big, this integration breaks down and precision is sacrificed for accuracy. That is, participants return again closer to the home position, because they start ignoring the deviant third landmark. Relying on two instead of three landmarks, however, goes along with responses that are scattered over a larger area, thus leading to higher variability. To model the breakdown of integration with increasing conflict, the probabilistic model based on a simple Gaussian distribution used for Experiment 1 needed a slide extension in from of a mixture of Gaussians. All parameters for the Mixture Model were fixed based on the homing performance in the baseline condition which contained a single landmark. from the 1-Landmark Condition. This way we found that the Mixture Model could predict the integration performance and its breakdown with no additional free parameters. Overall these data suggest that humans use similar optimal probabilistic strategies in visual and auditory navigation, integrating landmark information to improve homing precision and balance homing precision with homing accuracy.

Dataset Information

Landmark navigation in a mantis shrimp.

Publications

Landmark navigation in a mantis shrimp.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets