| Title |
Using leap motion to investigate the emergence of structure in speech and language
|
|---|---|
| Published in |
Behavior Research Methods, November 2016
|
| DOI | 10.3758/s13428-016-0818-x |
| Pubmed ID | |
| Authors |
Kerem Eryilmaz, Hannah Little |
| Abstract |
In evolutionary linguistics, experiments using arti?cial signal spaces are being used to investigate the emergenceof speech structure. These signal spaces need to be continuous, non-discretized spaces from which discrete unitsand patterns can emerge. They need to be dissimilar from-but comparable with-the vocal tract, in order tominimize interference from pre-existing linguistic knowledge, while informing us about language. This is a hardbalance to strike. This article outlines a new approach that uses the Leap Motion, an infrared controller that canconvert manual movement in 3d space into sound. The signal space using this approach is more ?exible than signalspaces in previous attempts. Further, output data using this approach is simpler to arrange and analyze. Theexperimental interface was built using free, and mostly open- source libraries in Python. We provide our sourcecode for other researchers as open source. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 21% |
| United Kingdom | 2 | 14% |
| Netherlands | 2 | 14% |
| Japan | 1 | 7% |
| Spain | 1 | 7% |
| Germany | 1 | 7% |
| Unknown | 4 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 79% |
| Scientists | 3 | 21% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 22% |
| Student > Ph. D. Student | 4 | 17% |
| Student > Bachelor | 2 | 9% |
| Student > Doctoral Student | 2 | 9% |
| Researcher | 2 | 9% |
| Other | 2 | 9% |
| Unknown | 6 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 4 | 17% |
| Linguistics | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Neuroscience | 2 | 9% |
| Agricultural and Biological Sciences | 1 | 4% |
| Other | 4 | 17% |
| Unknown | 6 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 15 November 2016.
All research outputs
#4,152,753
of 25,374,917 outputs
Outputs from Behavior Research Methods
#500
of 2,525 outputs
Outputs of similar age
#63,862
of 319,095 outputs
Outputs of similar age from Behavior Research Methods
#5
of 29 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,525 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done well, scoring higher than 80% of its peers.
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We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.