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Attention Score in Context
| Title |
Self-organizing map models of language acquisition
|
|---|---|
| Published in |
Frontiers in Psychology, January 2013
|
| DOI | 10.3389/fpsyg.2013.00828 |
| Pubmed ID | |
| Authors |
Ping Li, Xiaowei Zhao |
| Abstract |
Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 60% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 8% |
| Algeria | 1 | 1% |
| Poland | 1 | 1% |
| Unknown | 75 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 20% |
| Student > Master | 14 | 17% |
| Researcher | 13 | 15% |
| Professor | 9 | 11% |
| Lecturer | 7 | 8% |
| Other | 17 | 20% |
| Unknown | 7 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Linguistics | 20 | 24% |
| Psychology | 18 | 21% |
| Neuroscience | 8 | 10% |
| Computer Science | 8 | 10% |
| Social Sciences | 4 | 5% |
| Other | 13 | 15% |
| Unknown | 13 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 28 July 2014.
All research outputs
#7,435,621
of 22,731,677 outputs
Outputs from Frontiers in Psychology
#10,869
of 29,561 outputs
Outputs of similar age
#84,173
of 280,774 outputs
Outputs of similar age from Frontiers in Psychology
#481
of 969 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,561 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 62% of its peers.
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 280,774 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 969 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.