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iScience2024; 27(9); 110857; doi: 10.1016/j.isci.2024.110857

Unveiling directional physiological coupling in human-horse interactions.

Abstract: This research investigates the human-horse bond, aiming to unveil the physiological mechanisms regulating interspecies interactions. We hypothesized observing a physiological synchronization in human-horse dynamics, akin to human interactions. Through time-frequency Granger causality analysis of heart rate variability (HRV) and behavioral data, this study reveals the establishment of bidirectional synchronization in HRV between humans and horses. The coupling directionality is influenced by behavior and familiarity. In exploration scenarios led by horses, bidirectional interactions occur, particularly with familiar individuals. Conversely, during human-led activities such as grooming, physiological connectivity direction varies based on the familiarity level. In addition, the methodology allows in-depth analysis of sympathetic and parasympathetic nervous system contributions, highlighting their intricate role in the human-horse relationship. Such a physiological coupling estimate, correlated with behavioral data, provides a quantitative tool applicable across contexts and species This holds significant promise for assessing animal-assisted therapies and for applications in sports and various animal-related domains.
© 2024 The Author(s).
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Publication Date: 2024-08-31 PubMed ID: 39310749PubMed Central: PMC11414536DOI: 10.1016/j.isci.2024.110857Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study explores how humans and horses interact through physiological signals, specifically heart rate variability (HRV).
  • It investigates how these physiological signals synchronize between species and how the direction of influence varies based on behavior and familiarity.

Background and Aim

  • The research focuses on the human-horse bond, a relationship important in contexts such as therapy, sports, and animal-assisted activities.
  • The main aim is to uncover the underlying physiological mechanisms regulating interactions between humans and horses.
  • The researchers hypothesized that a physiological synchronization similar to what is seen in human-human interactions exists between humans and horses.

Methodology

  • Data Collection:
    • Simultaneous measurements of heart rate variability (HRV) from both humans and horses were recorded.
    • Behavioral observations were conducted to complement physiological data.
  • Analysis Technique:
    • Time-frequency Granger causality analysis was applied to HRV signals to identify the direction and strength of physiological coupling.
    • This method evaluates how past values of one time series help predict future values of another, revealing influence directionality.

Key Findings

  • Bidirectional Physiological Synchronization:
    • A bidirectional synchronization of HRV between humans and horses was found, indicating mutual physiological influence.
  • Influence of Behavior and Familiarity:
    • During exploration led by horses, synchronized interactions were strong, especially when humans were familiar to the horse.
    • In human-led activities such as grooming, the direction of physiological influence varied depending on how familiar the horse was with the human participant.
  • Sympathetic and Parasympathetic Contributions:
    • The study separated HRV data into sympathetic and parasympathetic nervous system components.
    • This detailed analysis revealed the complex role these autonomic branches play in regulating physiological coupling between species.

Significance

  • The demonstrated physiological coupling, combined with behavioral observations, offers a new quantitative tool to assess interspecies relationships.
  • This method can be applied in multiple contexts such as:
    • Evaluating the effectiveness of animal-assisted therapies.
    • Improving horse-human interactions in sports.
    • Enhancing welfare considerations in various animal-related domains.
  • Overall, this research advances our understanding of how physiological signals coordinate and regulate interspecies social bonds.

Cite This Article

APA
(2024). Unveiling directional physiological coupling in human-horse interactions. iScience, 27(9), 110857. https://doi.org/10.1016/j.isci.2024.110857

Publication

iScience
ISSN: 2589-0042
NlmUniqueID: 101724038
Country: United States
Language: English
Volume: 27
Issue: 9
Pages: 110857
PII: 110857

Researcher Affiliations

Conflict of Interest Statement

Authors declare no competing interests.

References

This article includes 50 references
  1. Kendall E, Maujean A, Pepping CA, Downes M, Lakhani A, Byrne J, Macfarlane K. A systematic review of the efficacy of equine-assisted interventions on psychological outcomes.. Eur. J. Psychother. Counsell. Health 2015;17:57–79.
  2. Jardat P, Lansade L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans.. Anim. Cognit. 2022;25:369–384.
    pubmed: 34476652
  3. Trestman M. Clever hans, alex the parrot, and kanzi: what can exceptional animal learning teach us about human cognitive evolution?. Biol. Theory 2015;10:86–99.
  4. Maeda T, Ochi S, Ringhofer M, Sosa S, Sueur C, Hirata S, Yamamoto S. Aerial drone observations identified a multilevel society in feral horses.. Sci. Rep. 2021;11:71.
    pmc: PMC7794487pubmed: 33420148
  5. Proops L, McComb K, Reby D. Cross-modal individual recognition in domestic horses (equus caballus). Proc. Natl. Acad. Sci. USA 2009;106:947–951.
    pmc: PMC2630083pubmed: 19075246
  6. Cozzi A, Sighieri C, Gazzano A, Nicol CJ, Baragli P. Post-conflict friendly reunion in a permanent group of horses (equus caballus). Behav. Process. 2010;85:185–190.
    pubmed: 20659538
  7. Wathan J, Proops L, Grounds K, Mccomb K. Horses discriminate between facial expressions of conspecifics.. Sci. Rep. 2016;6.
    pmc: PMC5171796pubmed: 27995958
  8. Briefer EF, Mandel R, Maigrot A-L, Briefer Freymond S, Bachmann I, Hillmann E. Perception of emotional valence in horse whinnies.. Front. Zool. 2017;14:8–12.
    pmc: PMC5303229pubmed: 28203263
  9. Borgi M, Loliva D, Cerino S, Chiarotti F, Venerosi A, Bramini M, Nonnis E, Marcelli M, Vinti C, De Santis C. Effectiveness of a standardized equine-assisted therapy program for children with autism spectrum disorder.. J. Autism Dev. Disord. 2016;46:1–9.
    pubmed: 26210515
  10. Mendonça T, Bienboire-Frosini C, Menuge F, Leclercq J, Lafont-Lecuelle C, Arroub S, Pageat P. The impact of equine-assisted therapy on equine behavioral and physiological responses.. Animals 2019;9:409.
    pmc: PMC6681086pubmed: 31266217
  11. Rault J-L, Waiblinger S, Boivin X, Hemsworth P. The power of a positive human–animal relationship for animal welfare.. Front. Vet. Sci. 2020;7.
    pmc: PMC7680732pubmed: 33240961
  12. Bilek E, Zeidman P, Kirsch P, Tost H, Meyer-Lindenberg A, Friston K. Directed coupling in multi-brain networks underlies generalized synchrony during social exchange.. Neuroimage 2022;252.
    pmc: PMC8987739pubmed: 35231631
  13. Cheong JH, Molani Z, Sadhukha S, Chang LJ. Synchronized affect in shared experiences strengthens social connection.. Commun. Biol. 2023;6:1099.
    pmc: PMC10613250pubmed: 37898664
  14. Palumbo RV, Marraccini ME, Weyandt LL, Wilder-Smith O, McGee HA, Liu S, Goodwin MS. Interpersonal autonomic physiology: A systematic review of the literature.. Pers. Soc. Psychol. Rev. 2017;21:99–141.
    pubmed: 26921410
  15. McCraty R. New frontiers in heart rate variability and social coherence research: techniques, technologies, and implications for improving group dynamics and outcomes.. Front. Public Health. 2017;5:267.
    pmc: PMC5643505pubmed: 29075623
  16. Von Borell E, Langbein J, Després G, Hansen S, Leterrier C, Marchant J, Marchant-Forde R, Minero M, Mohr E, Prunier A. Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals—a review. Physiol. Behav. 2007;92:293–316.
    pubmed: 17320122
  17. Müller V, Lindenberger U. Cardiac and respiratory patterns synchronize between persons during choir singing. PLoS One 2011;6.
    pmc: PMC3177845pubmed: 21957466
  18. Guidi A, Lanata A, Baragli P, Valenza G, Scilingo E. A wearable system for the evaluation of the human-horse interaction: A preliminary study. Electronics 2016;5:63.
  19. Lanata A, Guidi A, Valenza G, Baragli P, Scilingo E.P. Quantitative heartbeat coupling measures in human-horse interaction. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE 2016;pp. 2696–2699.
    pubmed: 28268877
  20. Timmons A.C, Margolin G, Saxbe D.E. Physiological linkage in couples and its implications for individual and interpersonal functioning: A literature review. J. Fam. Psychol. 2015;29:720–731.
    pmc: PMC4593729pubmed: 26147932
  21. Totterdell P, Kellett S, Teuchmann K, Briner R.B. Evidence of mood linkage in work groups. J. Pers. Soc. Psychol. 1998;74:1504–1515.
  22. Palagi E, Dall’Olio S, Demuru E, Stanyon R. Exploring the evolutionary foundations of empathy: consolation in monkeys. Evol. Hum. Behav. 2014;35:341–349.
  23. Levine M.A. Domestication and early history of the horse. Domest. Horse Orig. Dev. Manag. Behav. 2005:5–22.
  24. Scopa C, Greco A, Contalbrigo L, Fratini E, Lanatà A, Scilingo E.P, Baragli P. Inside the interaction: Contact with familiar humans modulates heart rate variability in horses. Front. Vet. Sci. 2020;7:998.
    pmc: PMC7734029pubmed: 33330706
  25. Azhari A, Bizzego A, Esposito G. Father-child dyads exhibit unique inter-subject synchronization during co-viewing of animation video stimuli. Soc. Neurosci. 2021;16:522–533.
    pubmed: 34407724
  26. Duranton C, Courby-Betremieux C, Gaunet F. One-and two-month-old dog puppies exhibit behavioural synchronization with humans independently of familiarity. Animals 2022;12:3356.
    pmc: PMC9740725pubmed: 36496877
  27. Chartrand T.L, Bargh J.A. The chameleon effect: The perception–behavior link and social interaction. J. Pers. Soc. Psychol. 1999;76:893–910.
    pubmed: 10402679
  28. Scopa C, Maglieri V, Baragli P, Palagi E. Getting rid of blinkers: the case of mirror self-recognition in horses (equus caballus). Anim. Cognit. 2022;25:711–716.
    pubmed: 35704243
  29. König von Borstel U, Krienert N. Influence of familiarity with the rider and type of work on horses’ fear reactions. Proceedings of the 8th International Equitation Science Conference in Edinburgh, UK 2012.
  30. Maurício L.S, Leme D.P, Hötzel M.J. How to understand them? a review of emotional indicators in horses. J. Equine Vet. Sci. 2023;126.
    pubmed: 36806715
  31. Torcivia C, McDonnell S. Equine discomfort ethogram. Animals 2021;11:580.
    pmc: PMC7931104pubmed: 33672338
  32. Scopa C, Palagi E, Sighieri C, Baragli P. Physiological outcomes of calming behaviors support the resilience hypothesis in horses. Sci. Rep. 2018;8.
    pmc: PMC6269543pubmed: 30504840
  33. Baccalá L.A, Sameshima K. Direct coherence: a tool for exploring functional interactions among brain structures. Methods Neural Ensemble Rec. 1998.
  34. Billman G.E. The lf/hf ratio does not accurately measure cardiac sympatho-vagal balance. Front. Physiol. 2013;4.
    pmc: PMC3576706pubmed: 23431279
  35. Callara A.L, Sebastiani L, Vanello N, Scilingo E.P, Greco A. Parasympathetic-sympathetic causal interactions assessed by time-varying multivariate autoregressive modeling of electrodermal activity and heart-rate-variability. IEEE Trans. Biomed. Eng. 2021;68:3019–3028.
    pubmed: 33617448
  36. Sankey C, Richard-Yris M.-A, Leroy H, Henry S, Hausberger M. Positive interactions lead to lasting positive memories in horses, equus caballus. Anim. Behav. 2010;79:869–875.
  37. Fureix C, Jego P, Sankey C, Hausberger M. How horses (equus caballus) see the world: humans as significant “objects”. Anim. Cognit. 2009;12:643–654.
    pubmed: 19381698
  38. Lampe J.F, Andre J. Cross-modal recognition of human individuals in domestic horses (equus caballus). Anim. Cognit. 2012;15:623–630.
    pubmed: 22526687
  39. Proops L, McComb K. Cross-modal individual recognition in domestic horses (equus caballus) extends to familiar humans. Proc. Biol. Sci. 2012;279:3131–3138.
    pmc: PMC3385734pubmed: 22593108
  40. Sankey C, Henry S, Clouard C, Richard-Yris M.-A, Hausberger M. Asymmetry of behavioral responses to a human approach in young naive vs. trained horses. Physiol. Behav. 2011;104:464–468.
    pubmed: 21605580
  41. Treisman A.M. Strategies and models of selective attention. Psychol. Rev. 1969;76:282–299.
    pubmed: 4893203
  42. Rainville P, Bechara A, Naqvi N, Damasio A.R. Basic emotions are associated with distinct patterns of cardiorespiratory activity. Int. J. Psychophysiol. 2006;61:5–18.
    pubmed: 16439033
  43. Hatfield E, Cacioppo J.T, Rapson R.L. Emotional contagion. Curr. Dir. Psychol. Sci. 1993;2:96–100.
  44. Keeling L.J, Jonare L, Lanneborn L. Investigating horse–human interactions: The effect of a nervous human. Vet. J. 2009;181:70–71.
    pubmed: 19394879
  45. Guidi A, Lanata A, Valenza G, Scilingo E.P, Baragli P. Validation of smart textile electrodes for electrocardiogram monitoring in free-moving horses. J. Vet. Behav. 2017;17:19–23.
  46. Valenza G, Lanatà A, Paradiso R, Scilingo E.P. Advanced technology meets mental health: how smartphones, textile electronics, and signal processing can serve mental health monitoring, diagnosis, and treatment. IEEE Pulse 2014;5:56–59.
    pubmed: 24838213
  47. Plomp G, Quairiaux C, Michel C.M, Astolfi L. The physiological plausibility of time-varying granger-causal modeling: normalization and weighting by spectral power. Neuroimage 2014;97:206–216.
    pubmed: 24736179
  48. Callara A.L, Greco A, Frasnelli J, Rho G, Vanello N, Scilingo E.P. Cortical network and connectivity underlying hedonic olfactory perception. J. Neural. Eng. 2021;18.
    pubmed: 34547740
  49. Faes L, Erla S, Nollo G. Measuring connectivity in linear multivariate processes: definitions, interpretation, and practical analysis. Comput. Math. Methods Med. 2012;2012.
    pmc: PMC3359820pubmed: 22666300
  50. North B.V, Curtis D, Sham P.C. A note on the calculation of empirical p values from monte carlo procedures. Am. J. Hum. Genet. 2002;71:439–441.
    pmc: PMC379178pubmed: 12111669

Citations

This article has been cited 1 times.
  1. Manolăchescu D, Tripon M, Crecan C, Tătaru M, Papuc I. Emotional contagion in human-horse interactions: A pilot study investigating the role of stress and body language in emotional transfer.. Open Vet J 2025 Nov;15(11):6050-6058.
    doi: 10.5455/OVJ.2025.v15.i11.60pubmed: 41630745google scholar: lookup
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