If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals? DOI Creative Commons
Masanori Kohda, Takashi Hotta, Tomohiro Takeyama

et al.

PLoS Biology, Journal Year: 2019, Volume and Issue: 17(2), P. e3000021 - e3000021

Published: Feb. 7, 2019

The ability to perceive and recognise a reflected mirror image as self (mirror self-recognition, MSR) is considered hallmark of cognition across species. Although MSR has been reported in mammals birds, it not known occur any other major taxon. Potentially limiting our test for taxa that the established assay, mark test, requires animals display contingency testing self-directed behaviour. These behaviours may be difficult humans interpret taxonomically divergent animals, especially those lack dexterity (or limbs) required touch mark. Here, we show fish, cleaner wrasse Labroides dimidiatus, shows behaviour reasonably interpreted passing through all phases test: (i) social reactions towards reflection, (ii) repeated idiosyncratic mirror, (iii) frequent observation their reflection. When subsequently provided with coloured tag modified fish attempt remove by scraping body presence but no response transparent marks or absence mirror. This remarkable finding presents challenge interpretation test—do accept these behavioural responses, which are taken evidence self-recognition species during lead conclusion self-aware? Or do rather decide patterns have basis cognitive process than pass test? If former, what does this mean understanding animal intelligence? latter, application metric abilities?This Short Report received both positive negative reviews experts. Academic Editor written an accompanying Primer publishing alongside article (https://doi.org/10.1371/journal.pbio.3000112). linked complementary expert perspective; discusses how current study should context against self-awareness wide range animals.

Language: Английский

Deep learning tools for the measurement of animal behavior in neuroscience DOI
Mackenzie Weygandt Mathis, Alexander Mathis

Current Opinion in Neurobiology, Journal Year: 2019, Volume and Issue: 60, P. 1 - 11

Published: Nov. 29, 2019

Language: Английский

Citations

379

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning DOI Open Access
Weizhe Hong, Ann Kennedy, Xavier P. Burgos-Artizzu

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2015, Volume and Issue: 112(38)

Published: Sept. 9, 2015

A lack of automated, quantitative, and accurate assessment social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying interactions their disorders such as autism. Here we present a new integrated hardware software system that combines video tracking, depth sensing, machine learning for automatic detection quantification involving close dynamic between two mice different coat colors home cage. We designed setup integrates traditional cameras with camera, developed computer vision tools to extract the body "pose" individual animals context, used supervised algorithm classify several well-described behaviors. validated robustness automated classifiers various experimental settings them examine how genetic background, Black Tan Brachyury (BTBR) (a previously reported autism model), influences behavior. Our approach allows rapid, measurement across diverse designs also affords ability develop new, objective behavioral metrics.

Language: Английский

Citations

286

Quantifying behavior to understand the brain DOI
Talmo Pereira, Joshua W. Shaevitz, Mala Murthy

et al.

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1537 - 1549

Published: Nov. 9, 2020

Language: Английский

Citations

256

Foraging for foundations in decision neuroscience: insights from ethology DOI
Dean Mobbs, Pete C. Trimmer, Daniel T. Blumstein

et al.

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(7), P. 419 - 427

Published: May 11, 2018

Language: Английский

Citations

252

Revealing the structure of pharmacobehavioral space through motion sequencing DOI
Alexander B. Wiltschko, Tatsuya Tsukahara,

Ayman Zeine

et al.

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(11), P. 1433 - 1443

Published: Sept. 21, 2020

Language: Английский

Citations

249

Hypothalamic Agrp Neurons Drive Stereotypic Behaviors beyond Feeding DOI Creative Commons
Marcelo O. Dietrich,

Marcelo R. Zimmer,

Jeremy Bober

et al.

Cell, Journal Year: 2015, Volume and Issue: 160(6), P. 1222 - 1232

Published: March 1, 2015

Language: Английский

Citations

247

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications DOI Creative Commons
J. M. Górriz, Javier Ramı́rez, Andrés Ortíz

et al.

Neurocomputing, Journal Year: 2020, Volume and Issue: 410, P. 237 - 270

Published: June 2, 2020

Language: Английский

Citations

240

Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering DOI Creative Commons
João C. Marques, Simone Lackner, Rita Félix

et al.

Current Biology, Journal Year: 2018, Volume and Issue: 28(2), P. 181 - 195.e5

Published: Jan. 1, 2018

Language: Английский

Citations

232

Computational Analysis of Behavior DOI
S.E. Roian Egnor, Kristin Branson

Annual Review of Neuroscience, Journal Year: 2016, Volume and Issue: 39(1), P. 217 - 236

Published: April 19, 2016

In this review, we discuss the emerging field of computational behavioral analysis-the use modern methods from computer science and engineering to quantitatively measure animal behavior. We aspects experiment design important both obtaining biologically relevant data enabling machine vision learning techniques for automation. These two goals are often in conflict. Restraining or restricting environment can simplify automatic behavior quantification, but it also degrade quality alter To enable biologists experiments obtain better measurements, scientists pinpoint fruitful directions algorithm improvement, review known effects artificial manipulation on tracking, feature extraction, automated classification, discovery, assumptions they make, types work best with.

Language: Английский

Citations

201

Chimpanzee face recognition from videos in the wild using deep learning DOI Creative Commons
Daniel Schofield, Arsha Nagrani, Andrew Zisserman

et al.

Science Advances, Journal Year: 2019, Volume and Issue: 5(9)

Published: Sept. 4, 2019

Wild ape face recognition using artificial intelligence opens the way for fully automated analysis of large-scale video datasets.

Language: Английский

Citations

200