↓ Skip to main content

Rule learning by zebra finches in an artificial grammar learning task: which rule?

Overview of attention for article published in Animal Cognition, September 2012
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 X users

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
113 Mendeley
citeulike
1 CiteULike
Title
Rule learning by zebra finches in an artificial grammar learning task: which rule?
Published in
Animal Cognition, September 2012
DOI 10.1007/s10071-012-0559-x
Pubmed ID
Authors

Caroline A. A. van Heijningen, Jiani Chen, Irene van Laatum, Bonnie van der Hulst, Carel ten Cate

Abstract

A hallmark of the human language faculty is the use of syntactic rules. The natural vocalizations of animals are syntactically simple, but several studies indicate that animals can detect and discriminate more complex structures in acoustic stimuli. However, how they discriminate such structures is often not clear. Using an artificial grammar learning paradigm, zebra finches were tested in a Go/No-go experiment for their ability to distinguish structurally different three-element sound sequences. In Experiment 1, zebra finches learned to discriminate ABA and BAB from ABB, AAB, BBA, and ABB sequences. Tests with probe sounds consisting of four elements suggested that the discrimination was based on attending to the presence or absence of repeated A- and B-elements. One bird generalized the discrimination to a new element type. In Experiment 2, we continued the training by adding four-element songs following a 'first and last identical versus different' rule that could not be solved by attending to repetitions. Only two out of five birds learned the overall discrimination. Testing with novel probes demonstrated that discrimination was not based on using the 'first and last identical' rule, but on attending to the presence or absence of the individual training stimuli. The two birds differed in the strategies used. Our results thus demonstrate only a limited degree of abstract rule learning but highlight the need for extensive and critical probe testing to examine the rules that animals (and humans) use to solve artificial grammar learning tasks. They also underline that rule learning strategies may differ between individuals.

X Demographics

X Demographics

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 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 2 2%
Netherlands 1 <1%
France 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Spain 1 <1%
Unknown 105 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 26%
Student > Master 24 21%
Student > Bachelor 16 14%
Researcher 15 13%
Professor 7 6%
Other 14 12%
Unknown 8 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 37%
Psychology 22 19%
Neuroscience 11 10%
Linguistics 9 8%
Computer Science 7 6%
Other 11 10%
Unknown 11 10%
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 18 September 2012.
All research outputs
#15,427,590
of 24,456,171 outputs
Outputs from Animal Cognition
#1,219
of 1,540 outputs
Outputs of similar age
#101,441
of 171,495 outputs
Outputs of similar age from Animal Cognition
#15
of 17 outputs
Altmetric has tracked 24,456,171 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,540 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.3. This one is in the 19th percentile – i.e., 19% 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 171,495 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.