Michael T. Mendl
Affiliations: | University of Cambridge, Cambridge, England, United Kingdom | ||
| Veterinary Sciences | University of Bristol, Bristol, England, United Kingdom |
Area:
Animal Welfare, Emotion and CognitionWebsite:
http://www.bristol.ac.uk/vetscience/people/michael-t-mendl/index.htmlGoogle:
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Publications
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| Mullan S, Nogueira SSC, Nogueira-Filho S, et al. (2024) Farming non-typical sentient species: ethical framework requires passing a high bar. Journal of Agricultural & Environmental Ethics. 37: 10 |
| Neville V, Mendl M, Paul ES, et al. (2023) A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals. Cognitive, Affective & Behavioral Neuroscience |
| Neville V, Lind J, Mendl E, et al. (2023) A mapping review of refinements to laboratory rat housing and husbandry. Lab Animal. 52: 63-74 |
| Mendl M, Neville V, Paul ES. (2022) Bridging the Gap: Human Emotions and Animal Emotions. Affective Science. 3: 703-712 |
| Neville V, Hunter K, Benato L, et al. (2022) Developing guidelines for pet rat housing through expert consultation. The Veterinary Record. e1839 |
| Neville V, Mounty J, Benato L, et al. (2022) Thinking outside the lab: Can studies of pet rats inform pet and laboratory rat welfare? Applied Animal Behaviour Science. 246: 105507 |
| Neville V, King J, Gilchrist ID, et al. (2021) Author Correction: Reward and punisher experience alter rodent decision-making in a judgement bias task. Scientific Reports. 11: 21387 |
| Neville V, Dayan P, Gilchrist ID, et al. (2021) Using Primary Reinforcement to Enhance Translatability of a Human Affect and Decision-Making Judgment Bias Task. Journal of Cognitive Neuroscience. 1-13 |
| Neville V, Mounty J, Benato L, et al. (2021) Pet rat welfare in the United Kingdom: The good, the bad and the ugly. The Veterinary Record. e559 |
| Neville V, Dayan P, Gilchrist ID, et al. (2021) Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. Plos Computational Biology. 17: e1008555 |