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Feature- versus rule-based generalization in rats, pigeons and humans

Overview of attention for article published in Animal Cognition, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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12 X users
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1 peer review site

Citations

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28 Dimensions

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66 Mendeley
Title
Feature- versus rule-based generalization in rats, pigeons and humans
Published in
Animal Cognition, July 2015
DOI 10.1007/s10071-015-0895-8
Pubmed ID
Authors

Elisa Maes, Guido De Filippo, Angus B Inkster, Stephen E. G. Lea, Jan De Houwer, Rudi D’Hooge, Tom Beckers, Andy J. Wills

Abstract

Humans can spontaneously create rules that allow them to efficiently generalize what they have learned to novel situations. An enduring question is whether rule-based generalization is uniquely human or whether other animals can also abstract rules and apply them to novel situations. In recent years, there have been a number of high-profile claims that animals such as rats can learn rules. Most of those claims are quite weak because it is possible to demonstrate that simple associative systems (which do not learn rules) can account for the behavior in those tasks. Using a procedure that allows us to clearly distinguish feature-based from rule-based generalization (the Shanks-Darby procedure), we demonstrate that adult humans show rule-based generalization in this task, while generalization in rats and pigeons was based on featural overlap between stimuli. In brief, when learning that a stimulus made of two components ("AB") predicts a different outcome than its elements ("A" and "B"), people spontaneously abstract an opposites rule and apply it to new stimuli (e.g., knowing that "C" and "D" predict one outcome, they will predict that "CD" predicts the opposite outcome). Rats and pigeons show the reverse behavior-they generalize what they have learned, but on the basis of similarity (e.g., "CD" is similar to "C" and "D", so the same outcome is predicted for the compound stimulus as for the components). Genuinely rule-based behavior is observed in humans, but not in rats and pigeons, in the current procedure.

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X Demographics

The data shown below were collected from the profiles of 12 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 65 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 29%
Researcher 9 14%
Student > Master 7 11%
Student > Bachelor 6 9%
Student > Postgraduate 5 8%
Other 13 20%
Unknown 7 11%
Readers by discipline Count As %
Psychology 37 56%
Agricultural and Biological Sciences 8 12%
Neuroscience 6 9%
Arts and Humanities 2 3%
Computer Science 2 3%
Other 3 5%
Unknown 8 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 17 March 2022.
All research outputs
#4,009,857
of 23,365,820 outputs
Outputs from Animal Cognition
#673
of 1,477 outputs
Outputs of similar age
#50,069
of 265,105 outputs
Outputs of similar age from Animal Cognition
#10
of 38 outputs
Altmetric has tracked 23,365,820 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,477 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.8. This one has gotten more attention than average, scoring higher than 54% 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 265,105 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.