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Mechanisms of object recognition: what we have learned from pigeons

Overview of attention for article published in Frontiers in Neural Circuits, October 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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8 X users
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1 Q&A thread

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Title
Mechanisms of object recognition: what we have learned from pigeons
Published in
Frontiers in Neural Circuits, October 2014
DOI 10.3389/fncir.2014.00122
Pubmed ID
Authors

Fabian A. Soto, Edward A. Wasserman

Abstract

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the "simple" brains of pigeons.

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X Demographics

The data shown below were collected from the profiles of 8 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Chile 1 2%
United States 1 2%
Unknown 54 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Ph. D. Student 10 18%
Student > Bachelor 8 14%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Other 9 16%
Unknown 8 14%
Readers by discipline Count As %
Psychology 14 25%
Neuroscience 11 19%
Agricultural and Biological Sciences 9 16%
Computer Science 3 5%
Linguistics 2 4%
Other 8 14%
Unknown 10 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 2021.
All research outputs
#4,936,535
of 24,140,950 outputs
Outputs from Frontiers in Neural Circuits
#300
of 1,265 outputs
Outputs of similar age
#52,869
of 260,292 outputs
Outputs of similar age from Frontiers in Neural Circuits
#5
of 25 outputs
Altmetric has tracked 24,140,950 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,265 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done well, scoring higher than 76% 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 260,292 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 79% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.