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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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Related in: MedlinePlus

Schematic diagram of an experiment on blocking of object category learning (A), together with our model’s predictions (B) and experimental results of studies with pigeons (C) and people (D). Bars in the bottom figures represent responding to novel test objects from the training categories during Phase 3.
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Figure 6: Schematic diagram of an experiment on blocking of object category learning (A), together with our model’s predictions (B) and experimental results of studies with pigeons (C) and people (D). Bars in the bottom figures represent responding to novel test objects from the training categories during Phase 3.

Mentions: One example is the blocking design illustrated in Figure 6A. In the blocking condition (Soto and Wasserman, 2010b,d), objects from the same category are first assigned to different responses in a pseudocategorization task (Phase 1). According to the model, accurate performance in this task requires strong connections between stimulus-specific elements and the correct responses. Once the pseudocategorization task is learned, it is possible to transform it into a true categorization task by dropping half of the trials, as shown in the middle panel of Figure 6A (Phase 2). Under normal circumstances, experience with this new categorization task should lead to strong control by category-specific elements and good generalization to new objects when they are presented during a test (Phase 3). In a control condition, pigeons were exposed only to this categorization task and a generalization test (Phases 2 and 3). In the blocking condition, however, the stimulus-response mapping is already known at the beginning of Phase 2; thus, pigeons should make few, if any, errors in predicting the correct response for each of the stimuli in this phase. No prediction error means no category learning; so, the model predicts less generalization of categorical performance to new objects in the blocking group than in the control group.


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

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

Schematic diagram of an experiment on blocking of object category learning (A), together with our model’s predictions (B) and experimental results of studies with pigeons (C) and people (D). Bars in the bottom figures represent responding to novel test objects from the training categories during Phase 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Schematic diagram of an experiment on blocking of object category learning (A), together with our model’s predictions (B) and experimental results of studies with pigeons (C) and people (D). Bars in the bottom figures represent responding to novel test objects from the training categories during Phase 3.
Mentions: One example is the blocking design illustrated in Figure 6A. In the blocking condition (Soto and Wasserman, 2010b,d), objects from the same category are first assigned to different responses in a pseudocategorization task (Phase 1). According to the model, accurate performance in this task requires strong connections between stimulus-specific elements and the correct responses. Once the pseudocategorization task is learned, it is possible to transform it into a true categorization task by dropping half of the trials, as shown in the middle panel of Figure 6A (Phase 2). Under normal circumstances, experience with this new categorization task should lead to strong control by category-specific elements and good generalization to new objects when they are presented during a test (Phase 3). In a control condition, pigeons were exposed only to this categorization task and a generalization test (Phases 2 and 3). In the blocking condition, however, the stimulus-response mapping is already known at the beginning of Phase 2; thus, pigeons should make few, if any, errors in predicting the correct response for each of the stimuli in this phase. No prediction error means no category learning; so, the model predicts less generalization of categorical performance to new objects in the blocking group than in the control group.

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