An artificial intelligence approach to predicting personality types in dogs.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 Jan 2024
29 Jan 2024
Historique:
received:
12
05
2023
accepted:
25
01
2024
medline:
30
1
2024
pubmed:
30
1
2024
entrez:
29
1
2024
Statut:
epublish
Résumé
Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs' behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable's contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: "Excitable/Hyperattached", "Anxious/Fearful", "Aloof/Predatory", "Reactive/Assertive", and "Calm/Agreeable". Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles.
Identifiants
pubmed: 38286813
doi: 10.1038/s41598-024-52920-9
pii: 10.1038/s41598-024-52920-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2404Informations de copyright
© 2024. The Author(s).
Références
Costa, P. T. & McCrae, R. R. The five-factor model of personality and its relevance to personality disorders. J. Pers. Disord. 6(4), 343–359. https://doi.org/10.1521/pedi.1992.6.4.343 (1992).
doi: 10.1521/pedi.1992.6.4.343
Jones, A. C. & Gosling, S. D. Temperament and personality in dogs (Canis familiaris): A review and evaluation of past research. Appl. Anim. Behav. Sci. 95, 1–53 (2005).
doi: 10.1016/j.applanim.2005.04.008
Rayment, D. J., De Groef, B., Peters, R. A. & Marston, L. C. Applied personality assessment in domestic dogs: Limitations and caveats. Appl. Anim. Behav. Sci. 163, 1–18 (2015).
doi: 10.1016/j.applanim.2014.11.020
Rothbart, M. K. & Bates, J. E. Temperament. In Handbook of Child Psychology: Social, Emotional, and Personality Development (eds Eisenberg, N. et al.) 99–166 (Wiley, 2006).
Pegram, C. et al. Proportion and risk factors for death by euthanasia in dogs in the UK. Sci. Rep. 11, 9145. https://doi.org/10.1038/s41598-021-88342-0 (2021).
doi: 10.1038/s41598-021-88342-0
pubmed: 33947877
pmcid: 8096845
Duffy, D. L., Kruger, K. & Serpell, J. A. Evaluation of a behavioral assessment tool for dogs relinquished to shelters. Prev. Vet. Med. 117, 601–609 (2014).
doi: 10.1016/j.prevetmed.2014.10.003
pubmed: 25457136
Miller, D. M., Stats, S. R., Partlo, B. S. & Rada, K. Factors Associated with the decision to surrender a pet to an animal shelter. JAVMA 209, 738–742 (1996).
doi: 10.2460/javma.1996.209.04.738
pubmed: 8756871
Patronek, G. J., Glickman, L. T., Beck, A. M., McCabe, G. P. & Ecker, C. Risk factors for relinquishment of dogs to an animal shelter. JAVMA 209, 572–581 (1996).
doi: 10.2460/javma.1996.209.03.572
pubmed: 8755975
Salman, M. D. et al. Human and animal factors related to the relinquishment of dogs and cats in 12 selected animal shelters in the United States. J. Appl. Anim. Welfare Sci. 1, 207–226 (1998).
doi: 10.1207/s15327604jaws0103_2
Salman, M. D. et al. Behavioral reasons for relinquishment of dogs and cats to 12 shelters. J. Appl. Anim. Welfare Sci. 3, 93–106 (2000).
doi: 10.1207/S15327604JAWS0302_2
Serpell, J. A. How happy is your pet? The problem of subjectivity in the assessment of companion animal welfare. Anim. Welf. 28, 57–66 (2019).
doi: 10.7120/09627286.28.1.057
Edgar Snyder Injury Law Firm. Dog Bite Statistics. Available at: https://www.edgarsnyder.com/statistics/dog-bite-statistics.html (2023).
Serpell, J. A. & Duffy, D. L. Dog breeds and their behavior. In Domestic Dog Cognition and Behavior (ed. Horowitz, A.) 31–57 (Springer-Verlag, 2014).
doi: 10.1007/978-3-642-53994-7_2
Rayment, D. J., Peters, R. A., Marston, L. C. & De Groef, B. Investigating canine personality structure using owner questionnaires measuring pet dog behaviour and personality. Appl. Anim. Behav. Sci. 180, 100–106 (2016).
doi: 10.1016/j.applanim.2016.04.002
Bray, E. E. et al. Predictive models of assistance dog training outcomes using the Canine Behavioral Assessment and Research Questionnaire and a standardized temperament evaluation. Front. Vet. Sci. https://doi.org/10.3389/fvets.2019.00049 (2019).
doi: 10.3389/fvets.2019.00049
pubmed: 30873418
pmcid: 6400848
Lazarowski, L. et al. Validation of a behavior test for predicting puppies’ suitability as detection dogs. Animals 11, 993. https://doi.org/10.3390/ani11040993 (2021).
doi: 10.3390/ani11040993
pubmed: 33916191
pmcid: 8065730
Ley, J. M., Bennett, P. C. & Coleman, G. J. A refinement and validation of the Monash canine personality questionnaire (MCPQ). Appl. Anim. Behav. Sci. 116, 220–227. https://doi.org/10.1016/j.applanim.2008.09.009 (2009).
doi: 10.1016/j.applanim.2008.09.009
Mirkó, E., Kubinyi, E., Gácsi, M. & Miklósi, A. Preliminary analysis of an adjective-based dog personality questionnaire developed to measure some aspects of personality in the domestic dog (Canis familiaris). Appl. Anim. Behav. Sci. 138, 88–98. https://doi.org/10.1016/j.applanim.2012.02.016 (2012).
doi: 10.1016/j.applanim.2012.02.016
Hsu, Y. & Serpell, J. A. Development and validation of a questionnaire for measuring behavior and temperament traits in pet dogs. J. Am. Vet. Med. Assoc. 223, 1293–1300 (2003).
doi: 10.2460/javma.2003.223.1293
pubmed: 14621216
Jones, A.C. Development and Validation of a Dog Personality Questionnaire. Unpublished PhD Dissertation: University of Texas at Austin (2008).
Serpell, J. A. & Hsu, Y. Development and validation of a novel method for evaluating temperament and behavior in guide dogs. Appl. Anim. Behav. Sci. 74, 347–364 (2001).
doi: 10.1016/S0168-1591(00)00210-0
Duffy, D. L. & Serpell, J. A. Predictive validity of a method for evaluating temperament in young guide and service dogs. Appl. Anim. Behav. Sci. 138, 99–109 (2012).
doi: 10.1016/j.applanim.2012.02.011
Duffy, D. L. & Serpell, J. A. Behavioral assessment of guide and service dogs. J. Vet. Behav. 3, 186–188 (2008).
doi: 10.1016/j.jveb.2007.12.010
Jakuba, T. et al. Differences in evaluation of a dog’s temperament by individual members of the same household. Soc. Anim. 21, 582–589 (2013).
doi: 10.1163/15685306-12341314
Foyer, P., Bjallerhag, N., Wilsson, E. & Jensen, P. Behavior and experiences of dogs during the first year of life predict the outcome in a later temperament test. Appl. Anim. Behav. Sci. 155, 93–100 (2014).
doi: 10.1016/j.applanim.2014.03.006
Arvelius, P., Asp, H. E., Fikse, W. F., Strandberg, E. & Nilsson, K. Genetic analysis of a temperament test as a tool to select against everyday life fearfulness in Rough Collie. J. Anim. Sci. 92, 4843–4855 (2014).
doi: 10.2527/jas.2014-8169
pubmed: 25253803
Barnard, S., Siracusa, C., Reisner, I., Valsecchi, P. & Serpell, J. A. Validity of model devices used to assess canine temperament in behavioral tests. Appl. Anim. Behav. Sci. 138, 79–87 (2012).
doi: 10.1016/j.applanim.2012.02.017
Dalla Villa, P. et al. Validation of the Socially Acceptable Behaviour (SAB) test in a central Italy pet dog population. Vet. Italiana https://doi.org/10.12834/VetIt.321.1283.3 (2016).
doi: 10.12834/VetIt.321.1283.3
De Meester, R. et al. A preliminary study on the use of the socially acceptable behaviour test as a test for shyness/confidence in the temperament of dogs. Journal of Veterinary Behavior 3, 161–170 (2008).
doi: 10.1016/j.jveb.2007.10.005
Svartberg, K. A comparison of behaviour in test and in everyday life: Evidence of three consistent boldness-related personality traits in dogs. Appl. Anim. Behav. Sci. 91, 103–128 (2005).
doi: 10.1016/j.applanim.2004.08.030
Van der Borg, J. A. M. et al. Evaluation of behaviour testing for human directed aggression in dogs. Appl. Anim. Behav. Sci. 128(1–4), 78–90 (2010).
doi: 10.1016/j.applanim.2010.09.016
Vermeire, S. et al. Neuro-imaging the serotonin 2A receptor as a valid biomarker for canine behavioural disorders. Res. Vet. Sci. 91, 465–472 (2011).
doi: 10.1016/j.rvsc.2010.09.021
pubmed: 21051063
Vermeire, S. et al. Serotonin 2A receptor, serotonin transporter and dopamine transporter alterations in dogs with compulsive behavior as a promising model for human obsessive-compulsive disorder. Psychiatry Res. Neuroimaging 201, 78–87 (2012).
doi: 10.1016/j.pscychresns.2011.06.006
Dutrow, E., Serpell, J. A. & Ostrander, E. Domestic dog lineages reveal genetic drivers of behavioral diversification. Cell 185, 4737–4756. https://doi.org/10.1016/j.cell.2022.11.003 (2022).
doi: 10.1016/j.cell.2022.11.003
pubmed: 36493753
pmcid: 10478034
MacLean, E. L., Snyder-Mackler, N., VonHoldt, B. & Serpell, J. A. Highly heritable and functionally relevant breed differences in dog behavior. Proc. Soc. B Biol. Sci. 286, 20190716. https://doi.org/10.1098/rspb.2019.0716 (2019).
doi: 10.1098/rspb.2019.0716
Zapata, I., Herron, M. E., Serpell, J. A. & Alvarez, C. E. Genetic testing of dogs predicts problem behaviors in clinical and nonclinical samples. BMC Genom. 23, 102. https://doi.org/10.1186/s12864-022-08351-9 (2022).
doi: 10.1186/s12864-022-08351-9
Zapata, I., Eyre, A. W., Alvarez, C. A. & Serpell, J. A. Latent class analysis of behavior across dog breeds reveal underlying temperament profiles. Nat. Sci. Rep. 12, 15627. https://doi.org/10.1038/s41598-022-20053-6 (2022).
doi: 10.1038/s41598-022-20053-6
Amirhosseini, M. H. & Kazemian, H. Machine learning approach to personality type prediction based on the Myers-Briggs type indicator®. Multimodal Technol. Interact. 4(1), 9. https://doi.org/10.3390/mti4010009 (2020).
doi: 10.3390/mti4010009
Louwen, C., Reidlinger, D. & Milne, N. Profiling health professionals’ personality traits, behaviour styles and emotional intelligence: A systematic review. BMC Med. Educ. 23(1), 120. https://doi.org/10.1186/s12909-023-04003-y (2023).
doi: 10.1186/s12909-023-04003-y
pubmed: 36803372
pmcid: 9938999
Elam, C. Application of Holland’s theory of vocational personalities and work environments to medical student specialty selection. J. Career Dev. 21, 37–48 (1994).
doi: 10.1177/089484539402100104
Russ, R. R. Personality Type and Job Satisfaction in the Home Office Environment (Oklahoma State University, 1996). Available at: https://core.ac.uk/download/pdf/215243328.pdf
Nguyen, D., Doogruöz, A. S., Rosé, C. P. & Jong, F. D. Computational sociolinguistics: A survey. Comput. Linguist 42, 537–593 (2016).
doi: 10.1162/COLI_a_00258
Gjurkovic, M., Snajder, J. Reddit: A gold mine for personality prediction. In Proceedings of the Second Workshop on Computational Modelling of People’s Opinions, Personality and Emotions in Social Media 87–97. Available online: https://peopleswksh.github.io/pdf/PEOPLES12.pdf (2018).
Golbeck, J., Robles, C., Edmondson, M. & Turner, K. Predicting personality from Twitter. In Proceedings of the IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing. Available online: https://ieeexplore.ieee.org/document/6113107/ (2011).
Komisin, M. & Guinn, C. Identifying personality types using document classification methods. In Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference 232–237 (2012).
Wan, D., Zhang, C., Wu, M. & An, Z. Personality prediction based on all characters of user social media information. In Proceedings of the Chinese National Conference on Social Media Processing 220–230 (2014).
Tandera, T., Suhartono, D., Wongso, R. & Prasetio, Y. Personality prediction system from Facebook users. In Proceedings of the 2nd International Conference on Computer Science and Computational Intelligence (Bali, Indonesia, 2017).
Hernandez, R. & Knight, I. S. Predicting Myers-bridge type indicator with text classification. In Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA. Available online: https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1184/reports/6839354.pdf (2017).
Cui, B. & Qi, C. Survey Analysis of Machine Learning Methods for Natural Language Processing for MBTI Personality Type Prediction. Available online: http://cs229.stanford.edu/proj2017/final-reports/5242471.pdf (2017).
Mitchell, J. Myers-Briggs Personality Type Dataset. Includes a Large Number of People’s MBTI Type and Content Written by Them. Available online: https://www.kaggle.com/datasnaek/mbti-type (2018).
Kodinariya, T. M. & Makwana, P. R. Review on determining number of cluster in K-mean clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1(6), 90–95 (2013).
Svartberg, K. Shyness-boldness predicts performance in working dogs. Appl. Anim. Behav. Sci. 2, 157–174. https://doi.org/10.1016/S0168-1591(02)00120-X (2002).
doi: 10.1016/S0168-1591(02)00120-X
Wright, H. F., Mills, D. & Pollux, P. M. Development and validation of a pyschometric tool for assessing impulsivity in the domestic dog (Canis familiaris). Int. J. Comp. Psychol. 24, 210–225. https://doi.org/10.46867/ijcp.2011.24.02.03 (2011).
doi: 10.46867/ijcp.2011.24.02.03
Kujala, M. V. et al. Modulation of dog-owner relationship and dog social and cognitive behavior by owner temperament and dog breed group. Sci. Rep. 13, 14739. https://doi.org/10.1038/s41598-023-41849-0 (2023).
doi: 10.1038/s41598-023-41849-0
pubmed: 37679427
pmcid: 10484941
Bray, E. E., Samuel, M. D., Cheney, D. L., Serpell, J. A. & Seyfarth, R. M. Effects of maternal investment, temperament, and cognition on guide dog success. PNAS 114, 9128–9133. https://doi.org/10.1073/pnas.1704303114 (2017).
doi: 10.1073/pnas.1704303114
pubmed: 28784785
pmcid: 5576795
Bray, E. E. et al. Enhancing the selection and performance of working dogs. Front. Vet. Sci. 12, 8 (2021).
Hare, E., Joffe, E., Wilson, C., Serpell, J. A. & Otto, C. M. Behavior traits associated with career outcome in a prison puppy-raising program. Appl. Anim. Behav. Sci. 236, 105218. https://doi.org/10.1016/j.applanim.2021.105218 (2021).
doi: 10.1016/j.applanim.2021.105218