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Mechanisms of object recognition: what we have learned from pigeons.

Soto FA, Wasserman EA - Front Neural Circuits (2014)

Bottom Line: Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA.

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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Experimental results (A) and simulated results (B) of a study on the feature-positive effect in object category learning by pigeons (Edwards and Honig, 1987). In the feature-positive discrimination, objects from a category predict the delivery of reward, whereas in the feature-negative discrimination, objects from a category predict absence of reward. In the pseudocategorization task, different objects from the same category predict either reward or no reward.
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Figure 5: Experimental results (A) and simulated results (B) of a study on the feature-positive effect in object category learning by pigeons (Edwards and Honig, 1987). In the feature-positive discrimination, objects from a category predict the delivery of reward, whereas in the feature-negative discrimination, objects from a category predict absence of reward. In the pseudocategorization task, different objects from the same category predict either reward or no reward.

Mentions: Another important training factor for studies using a go/no-go task is whether responses are rewarded to images showing the category, in what is called a feature-positive task, or to images showing no category, in what is called a feature-negative task. For example, Edwards and Honig (1987; see also Aust and Huber, 2001, 2002) trained pigeons to discriminate photographs of various scenes from photographs of the same scenes with people in them. Their results, reproduced in Figure 5A, show that pigeons were quite fast in learning the feature-positive discrimination, in which responses to people were rewarded, but they were slow in learning the feature-negative discrimination, in which responses to scenes without people were rewarded. In fact, learning of the feature-negative discrimination was as slow as learning a pseudocategorization task, suggesting that pigeons do not show any benefit from perceptual coherence when responses to the category are not rewarded.


Mechanisms of object recognition: what we have learned from pigeons.

Soto FA, Wasserman EA - Front Neural Circuits (2014)

Experimental results (A) and simulated results (B) of a study on the feature-positive effect in object category learning by pigeons (Edwards and Honig, 1987). In the feature-positive discrimination, objects from a category predict the delivery of reward, whereas in the feature-negative discrimination, objects from a category predict absence of reward. In the pseudocategorization task, different objects from the same category predict either reward or no reward.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4195317&req=5

Figure 5: Experimental results (A) and simulated results (B) of a study on the feature-positive effect in object category learning by pigeons (Edwards and Honig, 1987). In the feature-positive discrimination, objects from a category predict the delivery of reward, whereas in the feature-negative discrimination, objects from a category predict absence of reward. In the pseudocategorization task, different objects from the same category predict either reward or no reward.
Mentions: Another important training factor for studies using a go/no-go task is whether responses are rewarded to images showing the category, in what is called a feature-positive task, or to images showing no category, in what is called a feature-negative task. For example, Edwards and Honig (1987; see also Aust and Huber, 2001, 2002) trained pigeons to discriminate photographs of various scenes from photographs of the same scenes with people in them. Their results, reproduced in Figure 5A, show that pigeons were quite fast in learning the feature-positive discrimination, in which responses to people were rewarded, but they were slow in learning the feature-negative discrimination, in which responses to scenes without people were rewarded. In fact, learning of the feature-negative discrimination was as slow as learning a pseudocategorization task, suggesting that pigeons do not show any benefit from perceptual coherence when responses to the category are not rewarded.

Bottom Line: Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA.

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.

Show MeSH
Related in: MedlinePlus