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dc.contributor.authorMcBride, Sebastian D
dc.contributor.authorMorton, A Jennifer
dc.date.accessioned2018-11-14T00:32:18Z
dc.date.available2018-11-14T00:32:18Z
dc.date.issued2018-12
dc.identifier.issn0014-4819
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285081
dc.description.abstractUnderstanding the cognitive capacities of animals is important, because (a) several animal models of human neurodegenerative disease are considered poor representatives of the human equivalent and (b) cognitive capacities may provide insight into alternative animal models. We used a three-stage process of cognitive and neuroanatomical comparison (using sheep as an example) to assess the appropriateness of a species to model human brain function. First, a cognitive task was defined via a reinforcement-learning algorithm where values/constants in the algorithm were taken as indirect measures of neurophysiological attributes. Second, cognitive data (values/constants) were generated for the example species (sheep) and compared to other species. Third, cognitive data were compared with neuroanatomical metrics for each species (endocranial volume, gyrification index, encephalisation quotient, and number of cortical neurons). Four breeds of sheep (n = 15/sheep) were tested using the two-choice discrimination-reversal task. The 'reversal index' was used as a measure of constants within the learning algorithm. Reversal index data ranked sheep as third in a table of species that included primates, dogs, and pigs. Across all species, number of cortical neurons correlated strongest against the reversal index (r2 = 0.66, p = 0.0075) followed by encephalization quotient (r2 = 0.42, p = 0.03), endocranial volume (r2 = 0.30, p = 0.08), and gyrification index (r2 = 0.16, p = 0.23). Sheep have a high predicted level of cognitive capacity and are thus a valid alternative model for neurodegenerative research. Using learning algorithms within cognitive tasks increases the resolution of methods of comparative cognition and can help to identify the most relevant species to model human brain function and dysfunction.
dc.description.sponsorshipCHDI Inc
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBrain
dc.subjectAnimals
dc.subjectSheep
dc.subjectHumans
dc.subjectModels, Animal
dc.subjectCognition
dc.subjectConditioning, Operant
dc.subjectDiscrimination Learning
dc.subjectReversal Learning
dc.subjectPsychomotor Performance
dc.subjectSpecies Specificity
dc.subjectAlgorithms
dc.subjectModels, Psychological
dc.subjectDiscrimination, Psychological
dc.titleIndices of comparative cognition: assessing animal models of human brain function.
dc.typeArticle
prism.endingPage3390
prism.issueIdentifier12
prism.publicationDate2018
prism.publicationNameExp Brain Res
prism.startingPage3379
prism.volume236
dc.identifier.doi10.17863/CAM.32451
dcterms.dateAccepted2018-08-29
rioxxterms.versionofrecord10.1007/s00221-018-5370-8
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-12
dc.contributor.orcidMorton, Jennifer [0000-0003-0181-6346]
dc.identifier.eissn1432-1106
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-09-28


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International