Visual perception of planar-rotated 2D objects in goldfish (Carassius auratus)

DeLong, Caroline M.; Fobe, Irene; O’Leary, Taylor; Wilcox, Kenneth Tyler

doi:10.1016/j.beproc.2018.10.009

Cited by 4 publications

(21 citation statements)

References 53 publications

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“…However, our dolphin showed the lowest performance at 180°, which contrasts with the sea lion in Mauck and Dehnhardt’s (1997) study showing lowest performance at 90° (but highest RTs at 180°). Our results contrast with studies that have not shown systematic decrements in performance with planar-rotated 2-D stimuli as a function of aspect angle (fish: DeLong et al, 2018; lion-tailed macaques: Burmann et al, 2005). Our results showing that performance with planar-rotated objects was significantly better at 0° than 180° agree with other studies.…”

Section: Discussioncontrasting

confidence: 99%

“…The ability to perceive visually objects rotated in the picture or depth planes has been investigated in a wide variety of nonhuman animals that live in terrestrial habitats (rats: Minini & Jeffery, 2006; Sutherland, 1969; ferrets: Pollard, Beale, Lysons, & Preston, 1967; sheep: Kendrick, Atkins, Hinton, Heavens, & Keverne, 1996; newborn chicks: Wood, 2013; dogs: Racca et al, 2010; and baboons: Hopkins, Fagot, & Vauclair, 1993), arboreal habitats (monkeys: Freedman, Riesenhuber, Poggio, & Miller, 2006; Köhler, Hoffmann, Dehnhardt, & Mauck, 2005; Logothetis, Pauls, Bülthoff, & Poggio, 1994; Nielsen et al, 2008; Parr, 2011; Parr & Heintz, 2008; lion-tailed macaques: Burmann, Dehnhardt, & Mauck, 2005; and chimpanzees: Parr, 2011), aerial habitats (pigeons: Cook & Katz, 1999; Delius & Hollard, 1995; Hamm, Matheson, & Honig, 1997; Hollard & Delius, 1982; Jitsumori & Ohkubo, 1996; Spetch, Friedman, & Reid, 2001; Wasserman et al, 1996; honeybees: Dyer & Vuong, 2008; Plowright et al, 2001), and aquatic habitats (sea lions: Mauck & Dehnhardt, 1997; Schusterman & Thomas, 1966; octopus: Sutherland, 1969; fish: Bowman & Sutherland, 1969; DeLong, Fobe, O’Leary, & Wilcox, 2018; Schluessel, Kraniotakes, & Bleckmann, 2014; Wang & Takeuchi, 2017). The results of such studies can vary based on stimulus type (simple or complex), task (match-to-sample, forced choice paradigm, go/no-go, same-different task), type of training (one view vs. multiple views), and rotation plane.…”

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confidence: 99%

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Visual perception in a bottlenose dolphin (Tursiops truncatus): Successful recognition of 2-D objects rotated in the picture and depth planes.

DeLong

Fellner

Wilcox

et al. 2020

Journal of Comparative Psychology

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Aquatic species such as bottlenose dolphins can move in 3 dimensions and frequently view objects from different orientations. This study examined their ability to identify 2-D objects visually despite changes in orientation across 2 rotation planes. A dolphin performed a matching-to-sample task in which a sample was presented at a different orientation from its match in a 3-alternative choice array. Samples were presented at 6 aspect angles in the picture plane (0°, ±45°, ±135°, 180°) and 6 aspect angles in the depth plane (0°, −45°, ±90°, +135°, 180°). Alternatives were always presented at 0°. Performance was significantly better than chance for all aspect angles in both rotation plane tests. There was a significant linear decline in accuracy as the sample object was rotated from 0° toward 180° in the picture plane. Performance with familiar objects (M = 97.1%) exceeded performance with novel objects (M = 76.9%). In the depth plane rotation test, there was a significant quadratic trend in accuracy as the sample object was rotated from 0° toward 180°, in which performance was significantly lower at ±90° than at all other orientations. Performance in the picture plane where all object features were available irrespective of orientation was significantly better than performance in the depth plane where the availability of visible features were dependent upon orientation (M = 81.2% vs. M = 63.0%). The dolphin’s performance in this study shows evidence of both viewpoint-independent and viewpoint-dependent processes.

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Section: Discussioncontrasting

confidence: 99%

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confidence: 99%

Visual perception in a bottlenose dolphin (Tursiops truncatus): Successful recognition of 2-D objects rotated in the picture and depth planes.

DeLong

Fellner

Wilcox

et al. 2020

Journal of Comparative Psychology

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“…An additional important study of object recognition in teleosts was performed in the goldfish, where fish were able to identify a handful of single objects irrespective of rotation ( DeLong et al, 2018 ). Our study extends this finding by showing that teleostei are able to categorize and overcome transformation of translation, rotation, size (or taken together, what is known as similarity transformations) and contrast.…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies have indicated that fish also have the capacity to differentiate between different shapes ( Lucon-Xiccato et al, 2019 ; Mackintosh and Sutherland, 1963 ; May et al, 2016 ; Oliveira et al, 2015 ; Siebeck et al, 2009 ) and intensities ( Agrillo et al, 2016 ), use hierarchy of visual processing in discriminating shapes ( Truppa et al, 2010 ) and fish faces ( Parker et al, 2020 ), and the archerfish can even be trained to discriminate between human faces ( Newport et al, 2016 , 2018 ). For simple abstract stimuli, fish are capable of overcoming changes in orientation ( DeLong et al, 2018 ), size consistency ( Douglas et al, 1988 ) and the occlusion of objects ( Sovrano and Bisazza, 2008 ). In addition to these findings in teleosts, there is an interesting study in sharks, showing they were able to discriminate teleost fish from snails ( Schluessel and Düngen, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Recognition of natural objects in the archerfish

Volotsky

Ben-Shahar

Donchin

et al. 2022

Journal of Experimental Biology

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Recognition of individual objects and their categorization is a complex computational task. Nevertheless, visual systems can perform this task in a rapid and accurate manner. Humans and other animals can efficiently recognize objects despite countless variations in their projection on the retina due to different viewing angles, distance, illumination conditions and other parameters. To gain a better understanding of the recognition process in teleosts, we explored it in archerfish, a species that hunts by shooting a jet of water at aerial targets and thus can benefit from ecologically relevant recognition of natural objects. We found that archerfish not only can categorize objects into relevant classes but also can do so for novel objects, and additionally they can recognize an individual object presented under different conditions. To understand the mechanisms underlying this capability, we developed a computational model based on object features and a machine learning classifier. The analysis of the model revealed that a small number of features was sufficient for categorization, and the fish were more sensitive to object contours than textures. We tested these predictions in additional behavioral experiments and validated them. Our findings suggest the existence of a complex visual process in the archerfish visual system that enables object recognition and categorization.

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“…[23], [30] Movement may have altered the orientation of the initial record, so effective identification of the shape requires rotation invariance. [31], [32] Further, the entire complement of boundary markers for a given shape may not all be present due to occlusion. If the object is hidden behind a dense thicket of sea-plants or coral, only fragmented portions of the boundary may be visible at a given moment (see Figure 6).…”

Section: Elementary Shape Filtersmentioning

confidence: 99%

An Evolutionary Perspective on the Design of Neuromorphic Shape Filters

Greene

2020

IEEE Access

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A substantial amount of time and energy has been invested to develop machine vision using connectionist (neural network) principles. Most of that work has been inspired by theories advanced by neuroscientists and behaviorists for how cortical systems store stimulus information. Those theories call for information flow through connections among several neuron populations, with the initial connections being random (or at least non-functional). Then the strength or location of connections are modified through training trials to achieve an effective output, such as the ability to identify an object. Those theories ignored the fact that animals that have no cortex, e.g., fish, can demonstrate visual skills that outpace the best neural network models. Neural circuits that allow for immediate effective vision and quick learning have been preprogrammed by hundreds of millions of years of evolution and the visual skills are available shortly after hatching. Cortical systems may be providing advanced image processing, but most likely are using design principles that had been proven effective in simpler systems. The present article provides a brief overview of retinal and cortical mechanisms for registering shape information, with the hope that it might contribute to the design of shape-encoding circuits that more closely match the mechanisms of biological vision.

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Visual perception of planar-rotated 2D objects in goldfish (Carassius auratus)

Cited by 4 publications

References 53 publications

Visual perception in a bottlenose dolphin (Tursiops truncatus): Successful recognition of 2-D objects rotated in the picture and depth planes.

Visual perception in a bottlenose dolphin (Tursiops truncatus): Successful recognition of 2-D objects rotated in the picture and depth planes.

Recognition of natural objects in the archerfish

An Evolutionary Perspective on the Design of Neuromorphic Shape Filters

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