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

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

Bottom Line: The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood.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.

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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Schematic diagram of an experiment on overshadowing of object category learning (A), together with our model’s predictions (B) and experimental results from an experiment with pigeons (C). Bars in the bottom figures represent responding to novel test objects from the training categories.
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Figure 7: Schematic diagram of an experiment on overshadowing of object category learning (A), together with our model’s predictions (B) and experimental results from an experiment with pigeons (C). Bars in the bottom figures represent responding to novel test objects from the training categories.

Mentions: Other studies have found evidence of an overshadowing effect in category learning (Soto and Wasserman, 2012a; Soto et al., 2012). Figure 7A shows a schematic representation of the training tasks given to pigeons in one of these experiments (Soto and Wasserman, 2012a). On each trial, two different objects were presented to the pigeons. In the overshadowing condition, these objects came from two categories that were both informative about the correct response. For example, in Figure 7A, both airplanes and chairs were consistently associated with Response 1. Here, the category-specific elements of both categories should acquire control over behavior quite fast, quickly reaching a point in which performance is good and learning stops. At this point, the two categories overshadow each other: each acquires only a proportion of the response control that they would have gained if they had been presented alone. In the control condition, two objects are presented in each trial, but a single target category is informative about the correct response. In the example in Figure 7A, butterflies and cars are informative about correct responses, but people and flowers are not. In both conditions, category learning was tested by presenting pigeons with new objects from the trained categories. As shown in Figure 7B, the model predicts that performance with the target categories (red bars) should be impaired in the overshadowing condition compared to the control condition. As shown in Figure 7C, this prediction of the model matched the pigeons’ behavior. Furthermore, performance with the competing categories (blue bars) was also close to the model’s predictions.


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

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

Schematic diagram of an experiment on overshadowing of object category learning (A), together with our model’s predictions (B) and experimental results from an experiment with pigeons (C). Bars in the bottom figures represent responding to novel test objects from the training categories.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Schematic diagram of an experiment on overshadowing of object category learning (A), together with our model’s predictions (B) and experimental results from an experiment with pigeons (C). Bars in the bottom figures represent responding to novel test objects from the training categories.
Mentions: Other studies have found evidence of an overshadowing effect in category learning (Soto and Wasserman, 2012a; Soto et al., 2012). Figure 7A shows a schematic representation of the training tasks given to pigeons in one of these experiments (Soto and Wasserman, 2012a). On each trial, two different objects were presented to the pigeons. In the overshadowing condition, these objects came from two categories that were both informative about the correct response. For example, in Figure 7A, both airplanes and chairs were consistently associated with Response 1. Here, the category-specific elements of both categories should acquire control over behavior quite fast, quickly reaching a point in which performance is good and learning stops. At this point, the two categories overshadow each other: each acquires only a proportion of the response control that they would have gained if they had been presented alone. In the control condition, two objects are presented in each trial, but a single target category is informative about the correct response. In the example in Figure 7A, butterflies and cars are informative about correct responses, but people and flowers are not. In both conditions, category learning was tested by presenting pigeons with new objects from the trained categories. As shown in Figure 7B, the model predicts that performance with the target categories (red bars) should be impaired in the overshadowing condition compared to the control condition. As shown in Figure 7C, this prediction of the model matched the pigeons’ behavior. Furthermore, performance with the competing categories (blue bars) was also close to the model’s predictions.

Bottom Line: The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood.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.

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