Pigeons spontaneously form three-dimensional shape categories

Peissig, Jessie J.; Young, Michael E.; Wasserman, Edward A.; Biederman, Irving

doi:10.1016/j.beproc.2018.11.003

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…When 3-D objects in different viewpoints were used as stimuli, pigeons showed a strong and instantaneous tendency to categorize shapes of geometric objects that were presented in different rotations. In this study, the animals were unable to learn a pseudocategorization, possibly due to the fact that the randomly chosen borders of pseudocategories contradicted the highly visible natural category borders (Peissig et al 2019 ).…”

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confidence: 84%

Visual categories and concepts in the avian brain

et al. 2022

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Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research—at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian ‘visual cortex’. Third, we focus on the neurocomputational basis of perceptual categorization in the bird’s visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

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confidence: 84%

Visual categories and concepts in the avian brain

et al. 2022

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“…For example, Peissig et al (2019) demonstrated that pigeons’ categorisation performance was significantly impaired in a pseudocategorisation task that required birds to peck a key corresponding to one of four viewpoint rotations (-90, 0, 90, and 180°) of four different 3-D geometric objects, relative to a categorisation task that required birds to peck a single viewpoint rotation of only one of the same four objects. Therefore, pigeons in the Pseudocategorisation group only responded based on perceptual groupings of local orientation-dependant features that did not correspond to object identity, whereas the Categorisation group achieved highly accurate responding with access to viewpoint-invariant information on a particular object’s identity.…”

Section: Discussionmentioning

confidence: 99%

Searching for Face-Category Representation in the Avian Visual Forebrain

2019

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Visual information is processed hierarchically along a ventral (‘what’) pathway that terminates with categorical representation of biologically relevant visual percepts (such as faces) in the mammalian extrastriate visual cortex. How birds solve face and object representation without a neocortex is a long-standing problem in evolutionary neuroscience, though multiple lines of evidence suggest that these abilities arise from circuitry fundamentally similar to the extrastriate visual cortex. The aim of the present experiment was to determine whether birds also exhibit a categorical representation of the avian face-region in four visual forebrain structures of the tectofugal visual pathway: entopallium (ENTO), mesopallium ventrolaterale (MVL), nidopallium frontolaterale (NFL), and area temporo-parieto-occipitalis (TPO). We performed electrophysiological recordings from the right and left hemispheres of 13 pigeons while they performed a Go/No-Go task that required them to discriminate between two sets of stimuli that included images of pigeon faces. No neurons fired selectively to only faces in either ENTO, NFL, MVL, or TPO. Birds’ predisposition to attend to the local-features of stimuli may influence the perception of faces as a global combination of features, and explain our observed absence of face-selective neurons. The implementation of naturalistic viewing paradigms in conjunction with electrophysiological and fMRI techniques has the potential to promote and uncover the global processing of visual objects to determine whether birds exhibit category-selective patches in the tectofugal visual forebrain.

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“…Further, artificial stimuli are generally 'neutral' without potential confounding effects due to ecological or social relevance unlike stimuli such as faces or food (Vogels, 1999). Previous studies have used basic geometric shapes (geons (Peissig et al, 2019); Attneave style polygons (Attneave & Arnoult, 1956;Collin & McMullen, 2002); rectangle vs. circle (Ashby et al, 1998)), stimuli constructed based on binary, multi-level features (bugs (Smith & Minda, 1998); colorcharts (Cook & Smith, 2006;Lech et al, 2016); cartoon animals (Bowman et al, 2020;Bozoki et al, 2006)), nonface objects with common spatial configuration ('greebles', (Gauthier et al, 1998;Gauthier & Tarr, 1997)), parameterized line drawings (Sigala & Logothetis, 2002)), digital embryos (created by simulating embryonic development (Hauffen et al, 2012;Hegdé et al, 2008;Kromrey et al, 2010)), abstract numerosity (Ditz & Nieder, 2016), and random dot patterns (Antzoulatos & Miller, 2011;Antzoulatos & Miller, 2014;Wutz et al, 2018) (see Fig. 1g-o).…”

Section: Previous Studies On Categorizationmentioning

confidence: 99%

RUBubbles as a novel tool to study categorization learning

Apostel

Rose

2021

Behav Res

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Grouping objects into discrete categories affects how we perceive the world and represents a crucial element of cognition. Categorization is a widespread phenomenon that has been thoroughly studied. However, investigating categorization learning poses several requirements on the stimulus set in order to control which stimulus feature is used and to prevent mere stimulus–response associations or rote learning. Previous studies have used a wide variety of both naturalistic and artificial categories, the latter having several advantages such as better control and more direct manipulation of stimulus features. We developed a novel stimulus type to study categorization learning, which allows a high degree of customization at low computational costs and can thus be used to generate large stimulus sets very quickly. ‘RUBubbles’ are designed as visual artificial category stimuli that consist of an arbitrary number of colored spheres arranged in 3D space. They are generated using custom MATLAB code in which several stimulus parameters can be adjusted and controlled separately, such as number of spheres, position in 3D-space, sphere size, and color. Various algorithms for RUBubble generation can be combined with distinct behavioral training protocols to investigate different characteristics and strategies of categorization learning, such as prototype- vs. exemplar-based learning, different abstraction levels, or the categorization of a sensory continuum and category exceptions. All necessary MATLAB code is freely available as open-source code and can be customized or expanded depending on individual needs. RUBubble stimuli can be controlled purely programmatically or via a graphical user interface without MATLAB license or programming experience. Graphical abstract

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Pigeons spontaneously form three-dimensional shape categories

Cited by 5 publications

References 26 publications

Visual categories and concepts in the avian brain

Visual categories and concepts in the avian brain

Searching for Face-Category Representation in the Avian Visual Forebrain

RUBubbles as a novel tool to study categorization learning

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