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Proops, L., McComb, K., & Reby, D. (2008). Cross-modal individual vocal recognition in the domestic horse. In IESM 2008.
Abstract: Horses fulfill many of the criteria for a species in which it would be adaptive to be capable of individual recognition: they are highly social, form strong and long lasting bonds, their affiliations are rarely kin based, they have a fission-fusion social structure and they possess inter and intra-group dominance hierarchies.
We used a novel cross-modal, expectancy violation paradigm to provide the first systematic evidence that a non-human animal – the domestic horse- is capable of cross modal recognition. We believe this paradigm could provide an ideal way to study individual recognition across a wide range of species. For full published details see: Proops L, McComb K, Reby D (2009) Cross-modal individual recognition in domestic horses (Equus caballus). Proc Natl Acad Sci U S A 106: 947-951. |
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Proops, L., McComb, K., & Reby, D. (2008). Horse-human interactions: Attention attribution and the use of human cues by domestic horses (Equus caballus). In IESM 2008.
Abstract: Recent research has shown that domestic dogs are particularly good at reading human attentional cues, often outperforming chimpanzees and hand reared wolves [1, 2]. It has been suggested that the close evolutionary relationship between humans and dogs has led to the development of this ability, however very few other species have been studied [3]. We tested the ability of 24 domestic horses to discriminate between an attentive and inattentive person when choosing whom to approach for food. While the attentive person faced forwards, the inattentive person either stood with their body turned 180° away from the subject (body orientation condition), stood with their body facing forwards but their head facing away (head orientation condition) or stood facing forwards but with their eyes closed (eyes closed condition). A fourth, mixed condition was included where the attentive person stood with their body facing away from the subjects but their head turned towards the subject while the inattentive person stood with their body facing the subject but their head turned away. Horses chose the attentive person significantly more often using the body cue (n = 24, k = 19, p = 0.003), the head cue (n = 24, k = 18, p = 0.011), and the eye cue (n = 24, k = 19, p = 0.003) but not the mixed cue (n = 24, k = 13, p = 0.42). In an additional pilot study, horses were tested in an object choice task. A human experimenter cued one of two buckets by either tapping the bucket (tap condition), orienting their body towards the bucket and pointing (body and point condition), pointing while facing forwards (point condition) or orienting their body towards the bucket (body condition). If the subjects chose the correct bucket they were rewarded. Subjects were able to use the tap cue (n = 31, k = 21, p = 0.035), body + point cue (n= 31, k = 21, p = 0.035) and the point cue (n = 30, k = 21, p = 0.021) but not the body cue (n = 31, k = 11, p = 0.076). These results taken together suggest that domestic horses are also very sensitive to human attentional cues, including gaze.
Keywords: social cognition, animal-human interaction, horses, attention attribution, domestication 1. Hare, B., Brown, M., Williamson, C., and Tomasello, M. (2002). The domestication of social cognition in dogs. Science 298, 1634-1636. 2. Gacsi, M., Miklosi, A., Varga, O., Topal, J., and Csanyi, V. (2004). Are readers of our face readers of our minds` Dogs (Canis familiaris) show situation-dependent recognition of human’s attention. Animal Cognition 7, 144-153. 3. Hare, B., and Tomasello, M. (2005). Human-like social skills in dogs? Trends Cogn. Sci. 9, 439-444. Keywords: social cognition; animal-human interaction; horses; attention
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Proops, L., Grounds, K., Smith, A. V., & McComb, K. (2018). Animals Remember Previous Facial Expressions that Specific Humans Have Exhibited. Current Biology, 28(9), 1428–1432.e4.
Abstract: Summary For humans, facial expressions are important social signals, and how we perceive specific individuals may be influenced by subtle emotional cues that they have given us in past encounters. A wide range of animal species are also capable of discriminating the emotions of others through facial expressions [1, 2, 3, 4, 5], and it is clear that remembering emotional experiences with specific individuals could have clear benefits for social bonding and aggression avoidance when these individuals are encountered again. Although there is evidence that non-human animals are capable of remembering the identity of individuals who have directly harmed them [6, 7], it is not known whether animals can form lasting memories of specific individuals simply by observing subtle emotional expressions that they exhibit on their faces. Here we conducted controlled experiments in which domestic horses were presented with a photograph of an angry or happy human face and several hours later saw the person who had given the expression in a neutral state. Short-term exposure to the facial expression was enough to generate clear differences in subsequent responses to that individual (but not to a different mismatched person), consistent with the past angry expression having been perceived negatively and the happy expression positively. Both humans were blind to the photograph that the horses had seen. Our results provide clear evidence that some non-human animals can effectively eavesdrop on the emotional state cues that humans reveal on a moment-to-moment basis, using their memory of these to guide future interactions with particular individuals.
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Previc, F. H. (2002). Thyroid hormone production in chimpanzees and humans: implications for the origins of human intelligence. Am J Phys Anthropol, 118(4), 402–3; discussion 404–5.
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Preston, S. D., & de Waal, F. B. M. (2002). Empathy: Its ultimate and proximate bases. Behav Brain Sci, 25(1), 1–20; discussion 20–71.
Abstract: There is disagreement in the literature about the exact nature of the phenomenon of empathy. There are emotional, cognitive, and conditioning views, applying in varying degrees across species. An adequate description of the ultimate and proximate mechanism can integrate these views. Proximately, the perception of an object's state activates the subject's corresponding representations, which in turn activate somatic and autonomic responses. This mechanism supports basic behaviors (e.g., alarm, social facilitation, vicariousness of emotions, mother-infant responsiveness, and the modeling of competitors and predators) that are crucial for the reproductive success of animals living in groups. The Perception-Action Model (PAM), together with an understanding of how representations change with experience, can explain the major empirical effects in the literature (similarity, familiarity, past experience, explicit teaching, and salience). It can also predict a variety of empathy disorders. The interaction between the PAM and prefrontal functioning can also explain different levels of empathy across species and age groups. This view can advance our evolutionary understanding of empathy beyond inclusive fitness and reciprocal altruism and can explain different levels of empathy across individuals, species, stages of development, and situations.
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Powers, P., & Harrison, A. (2002). Effects of the rider on the linear kinematics of jumping horses. Sports Biomech, 1(2), 135–146.
Abstract: This study examined the effects of the rider on the linear projectile kinematics of show-jumping horses. SVHS video recordings (50 Hz) of eight horses jumping a vertical fence 1 m high were used for the study. Horses jumped the fence under two conditions: loose (no rider or tack) and ridden. Recordings were digitised using Peak Motus. After digitising the sequences, each rider's digitised data were removed from the ridden horse data so that three conditions were examined: loose, ridden (including the rider's data) and riderless (rider's data removed). Repeated measures ANOVA revealed significant differences between ridden and loose conditions for CG height at take-off (p < 0.001), CG distance to the fence at take-off (p = 0.001), maximum CG during the suspension phase (p < 0.001), CG position over the centre of the fence (p < 0.001), CG height at landing (p < 0.001), and vertical velocity at take-off (p < 0.001). The results indicated that the rider's effect on jumping horses was primarily due to behavioural changes in the horses motion (resulting from the rider's instruction), rather than inertial effects (due to the positioning of the rider on the horse). These findings have implications for the coaching of riders and horses.
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Pollmann, U. (2002). [Keeping of horses in circus and show businesses]. Dtsch Tierarztl Wochenschr, 109(3), 126–129.
Abstract: The conditions under which horses are kept and the performance of acts in the circus ring may give rise to animal protection-relevant aspects for circus and show horses. A number of intolerable conditions under which horses are kept and procedures adopted for the work with circus and show horses are described. In addition, attention is drawn to monitoring methods capable of exposing the deplorable shortcomings of these businesses.
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Pokorná, M., & Bartošová, J. (2012). Social learning in horses. In K. Krueger (Ed.), Proceedings of the 2. International Equine Science Meeting (Vol. in press). Wald: Xenophon Publishing.
Abstract: Social observational learning is one of learning abilities expected in domestic horses (Equus caballus) because of their ecological and evolutional history. However, a few studies on this type of learning in horses failed to provide clear evidence of observational learning and/or could not distinguished it from other types of learning. We tested interspecific observational learning abilities using the spatial task and a human demonstrator. We hypothesised that 1) horses with possibility of observing a human demonstrator will complete the task in shorter time than control horses without any demonstrator, and 2) horses observing a familiar demonstrator will carry out the task in shorter time than horses with an unfamiliar demonstrator due to established positive human-horse relationship. We randomly allocated 24 riding horses of mixed age and breed to three groups per 8 and started the task either with observing a familiar demonstrator, unfamiliar demonstrator or without demonstrator (control group). Each horse was released individually at the starting point in the experimental paddock and the latency to pass the task was recorded. A horse completed the task once it walked 25 m from the starting point to the squared area (4x4 m) fenced by a tape, went into it through the entrance on the opposite side and touched the bucket with food. Eight people served as demonstrators, each for one familiar and one unfamiliar horse. Horses from groups with a demonstrator, either familiar or unfamiliar, reached the food bucket significantly faster than control horses during the first trial (mean±SE: 29.1±3.13 s with familiar, 28.9±3.13 s unfamiliar and 41.5 ± 3.13 s without demonstrator, P<0.02, GLMM, PROC MIXED, SAS). Horses did not differ in time needed to reach the fence of the squared area, but in “solving time”, i.e. time from reaching the fence of the squared area and touching the bucket (14.6±2.34, 14.3±2.34 and 27.6±2.34 s in horses with familiar, unfamiliar or without demonstrator, P<0.001). Despite our presumption, the horses observing a familiar demonstrator finished the task in comparable time as horses with an unfamiliar demonstrator (P=0.85) which indicated little effect of long lasting positive relationship between a horse and a particular human. We found, however, large individual variability in performance of individual demonstrators. Further, horses did not differ in time needed to pass the same task without a demonstrator repeated either shortly or 7 days after the first test which supported that interspecific observational learning rather than social facilitation occurred. In conclusion, horses with a human demonstrator, regardless familiar or unfamiliar, were able to solve the task in shorter time compared to control horses but they did not differ in performing repeated task if they learned it by individual or social learning process. This indicates that interspecific observational learning does occur in horses. Supported by AWIN, EU FP7 project No. 266213.
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Pitchford, R. J., Visser, P. S., du Toit, J. F., de Pienaar, U. V., & Young, E. (1973). Observations on the ecology of Schistosoma mattheei Veglia & Le Roux, 1929, in portion of the Kruger National Park and surrounding area using a new quantitative technique for egg output. J S Afr Vet Assoc, 44(4), 405–420.
Keywords: Animals; Artiodactyla; Buffaloes; Cattle; Cattle Diseases/epidemiology; Dog Diseases/epidemiology; Dogs; Feces; Goats; Haplorhini; Horse Diseases/epidemiology; Horses; Humans; Methods; Monkey Diseases/epidemiology; Papio; Parasite Egg Count; Schistosomiasis/epidemiology/*veterinary; Sheep; Sheep Diseases/epidemiology; South Africa; Swine; Swine Diseases/epidemiology
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Pimenta, V., Barroso, I., Boitani, L., & Beja, P. (2018). Risks a la carte: Modelling the occurrence and intensity of wolf predation on multiple livestock species. Biol. Conserva., 228, 331–342.
Abstract: Predation on livestock is a source of human-wildlife conflicts and can undermine the conservation of large carnivores. To design effective mitigation strategies, it is important to understand the determinants of predation across livestock species, which often differ in husbandry practices, vulnerability to predators and economic value. Moreover, attention should be given to both predation occurrence and intensity, because these can have different spatial patterns and predictors. We used spatial risk modelling to quantify factors affecting wolf predation on five livestock species in Portugal. Within the 1619 parishes encompassing the entire wolf range in the country, the national wolf compensation scheme recorded 17,670 predation events in 2009-2015, each involving one or more livestock species: sheep (31.7%), cattle (27.7%), goats (26.8%), horses (14.8%) and donkeys (3.2%). Models built with 2009-2013 data and validated with 2014-2015 data, showed a shared general pattern of predation probability on each species increasing with its own density and proximity to wolf packs. For some species there were positive relations with the density of other livestock species, and with habitat variables such as altitude, and land cover by shrubland and natural pastures. There was also a general pattern for predation intensity on each species increasing with its own density, while proximity to wolf packs had no significant effects. Predation intensity on goats, cattle and horses increased with the use of communal versus private pastures. Our results suggest that although predation may occur wherever wolves coexist with livestock species, high predation intensity is mainly restricted to particular areas where husbandry practices increase the vulnerability of animals, and this is where mitigation efforts should concentrate.
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