| Title |
Decoding intentions from movement kinematics
|
|---|---|
| Published in |
Scientific Reports, November 2016
|
| DOI | 10.1038/srep37036 |
| Pubmed ID | |
| Authors |
Andrea Cavallo, Atesh Koul, Caterina Ansuini, Francesca Capozzi, Cristina Becchio |
| Abstract |
How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others' intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others' intentional actions. |
X Demographics
The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 4 | 31% |
| United Kingdom | 1 | 8% |
| United States | 1 | 8% |
| Japan | 1 | 8% |
| Unknown | 6 | 46% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 77% |
| Scientists | 3 | 23% |
Mendeley readers
The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 2 | 1% |
| Japan | 1 | <1% |
| Unknown | 180 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 39 | 21% |
| Researcher | 36 | 20% |
| Student > Master | 21 | 11% |
| Student > Bachelor | 14 | 8% |
| Student > Doctoral Student | 11 | 6% |
| Other | 30 | 16% |
| Unknown | 32 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 60 | 33% |
| Neuroscience | 31 | 17% |
| Engineering | 11 | 6% |
| Agricultural and Biological Sciences | 6 | 3% |
| Medicine and Dentistry | 5 | 3% |
| Other | 25 | 14% |
| Unknown | 45 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 03 May 2022.
All research outputs
#1,326,475
of 24,594,795 outputs
Outputs from Scientific Reports
#12,873
of 134,165 outputs
Outputs of similar age
#23,817
of 312,049 outputs
Outputs of similar age from Scientific Reports
#373
of 3,345 outputs
Altmetric has tracked 24,594,795 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 134,165 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done particularly well, scoring higher than 90% of its peers.
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We're also able to compare this research output to 3,345 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.