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Motor patterns during active electrosensory acquisition

Overview of attention for article published in Frontiers in Behavioral Neuroscience, May 2014
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Title
Motor patterns during active electrosensory acquisition
Published in
Frontiers in Behavioral Neuroscience, May 2014
DOI 10.3389/fnbeh.2014.00186
Pubmed ID
Authors

Volker Hofmann, Bart R. H. Geurten, Juan I. Sanguinetti-Scheck, Leonel Gómez-Sena, Jacob Engelmann

Abstract

Motor patterns displayed during active electrosensory acquisition of information seem to be an essential part of a sensory strategy by which weakly electric fish actively generate and shape sensory flow. These active sensing strategies are expected to adaptively optimize ongoing behavior with respect to either motor efficiency or sensory information gained. The tight link between the motor domain and sensory perception in active electrolocation make weakly electric fish like Gnathonemus petersii an ideal system for studying sensory-motor interactions in the form of active sensing strategies. Analyzing the movements and electric signals of solitary fish during unrestrained exploration of objects in the dark, we here present the first formal quantification of motor patterns used by fish during electrolocation. Based on a cluster analysis of the kinematic values we categorized the basic units of motion. These were then analyzed for their associative grouping to identify and extract short coherent chains of behavior. This enabled the description of sensory behavior on different levels of complexity: from single movements, over short behaviors to more complex behavioral sequences during which the kinematics alter between different behaviors. We present detailed data for three classified patterns and provide evidence that these can be considered as motor components of active sensing strategies. In accordance with the idea of active sensing strategies, we found categorical motor patterns to be modified by the sensory context. In addition these motor patterns were linked with changes in the temporal sampling in form of differing electric organ discharge frequencies and differing spatial distributions. The ability to detect such strategies quantitatively will allow future research to investigate the impact of such behaviors on sensing.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Canada 1 2%
Unknown 42 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Student > Bachelor 7 16%
Researcher 6 13%
Student > Doctoral Student 3 7%
Professor 2 4%
Other 6 13%
Unknown 12 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 38%
Neuroscience 9 20%
Business, Management and Accounting 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Arts and Humanities 1 2%
Other 4 9%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 July 2018.
All research outputs
#14,781,203
of 22,757,090 outputs
Outputs from Frontiers in Behavioral Neuroscience
#2,029
of 3,159 outputs
Outputs of similar age
#126,725
of 226,672 outputs
Outputs of similar age from Frontiers in Behavioral Neuroscience
#50
of 77 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,159 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.2. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 226,672 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.