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      Mechanisms of object recognition: what we have learned from pigeons

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          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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          The many faces of configural processing.

          Adults' expertise in recognizing faces has been attributed to configural processing. We distinguish three types of configural processing: detecting the first-order relations that define faces (i.e. two eyes above a nose and mouth), holistic processing (glueing the features together into a gestalt), and processing second-order relations (i.e. the spacing among features). We provide evidence for their separability based on behavioral marker tasks, their sensitivity to experimental manipulations, and their patterns of development. We note that inversion affects each type of configural processing, not just sensitivity to second-order relations, and we review evidence on whether configural processing is unique to faces.
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            Getting formal with dopamine and reward.

            Recent neurophysiological studies reveal that neurons in certain brain structures carry specific signals about past and future rewards. Dopamine neurons display a short-latency, phasic reward signal indicating the difference between actual and predicted rewards. The signal is useful for enhancing neuronal processing and learning behavioral reactions. It is distinctly different from dopamine's tonic enabling of numerous behavioral processes. Neurons in the striatum, frontal cortex, and amygdala also process reward information but provide more differentiated information for identifying and anticipating rewards and organizing goal-directed behavior. The different reward signals have complementary functions, and the optimal use of rewards in voluntary behavior would benefit from interactions between the signals. Addictive psychostimulant drugs may exert their action by amplifying the dopamine reward signal.
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              Receptive fields and functional architecture of monkey striate cortex.

              1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two-thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.
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                Author and article information

                Contributors
                Journal
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                1662-5110
                13 October 2014
                2014
                : 8
                : 122
                Affiliations
                [1] 1Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, USA
                [2] 2Department of Psychology, University of Iowa Iowa City, IA, USA
                Author notes

                Edited by: Davide Zoccolan, International School for Advanced Studies, Italy

                Reviewed by: Hans P. Op De Beeck, University of Leuven, Belgium; Justin N. Wood, University of Southern California, USA

                *Correspondence: Fabian A. Soto, Department of Psychological and Brain Sciences, University of California, Santa Barbara, Building #251, Santa Barbara, CA 93106, USA e-mail: fabian.soto@ 123456psych.ucsb.edu

                This article was submitted to the journal Frontiers in Neural Circuits.

                Article
                10.3389/fncir.2014.00122
                4195317
                25352784
                fcf18947-f4aa-4b0e-b1cf-ddac480eff0d
                Copyright © 2014 Soto and Wasserman.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 June 2014
                : 15 September 2014
                Page count
                Figures: 11, Tables: 0, Equations: 0, References: 212, Pages: 22, Words: 17807
                Categories
                Neuroscience
                Review Article

                Neurosciences
                object recognition,categorization,invariance,learning,pigeon
                Neurosciences
                object recognition, categorization, invariance, learning, pigeon

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