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Title: Skill in Expert Dogs
Author(s): Helton, William S.
Source: Journal of Experimental Psychology: Applied, v13 n3 p171-178 Sep 2007. 8 pp.
Peer Reviewed: Yes
ISSN: 1076-898X
Descriptors: Motor Development, Cognitive Processes, Psychomotor Skills, Animals, Comparative Analysis, Human Body, Experience, Competition
Abstract: The motor control of novice participants is often cognitively demanding and susceptible to interference by other tasks. As people develop expertise, their motor control becomes less susceptible to interference from other tasks. Researchers propose a transition in human motor skill from active control to automaticity. This progression may also be the case with nonhuman animals. Differences in performance characteristics between expert, advanced, intermediate, and novice dogs competing in the sport of agility were investigated. There were statistically significant differences between dogs of varying competitive levels in speed, motor control, and signal detections suggestive of increasing motor control automaticity in highly skilled, or expert, dogs. The largest sequential motor control difference was between novice and intermediate dogs, d = 0.96, whereas the largest sequential signal detection difference was between advanced and expert dogs, d = 0.90. These findings have two significant implications for expertise researchers: first, the observed similarities between dogs and humans may enable dogs to be used as expert models; and second, expertise science and methods may be profitably employed in the future to create more proficient canine workers.
Abstractor: Author
Number of Pages: 8
Publication Type: Journal Articles; Reports - Research
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
URL: http://content.apa.org/journals/xap/13/3/171
Journal Code: FEB2014
Entry Date: 2007
Accession Number: EJ777458
Persistent link to this record (Permalink): https://login.ezp.pasadena.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ777458&site=ehost-live
Cut and Paste: Skill in Expert Dogs
Database: ERIC
By: William S. Helton
Department of Psychology, Michigan Technological University;
Acknowledgement: I thank Christina Sommer and the handlers at the Queen City Dog Training Club in Cincinnati, Ohio for facilitating this study. In addition, I thank Mark Williams, Anders Ericsson, Philip Ackerman, and four anonymous reviewers for their keen comments and assistance in preparing this article.
Expertisein humans has been studied for a long time and there are many stable findings (Ericsson, Charness, Feltovich, & Hoffman, 2006). However, humans are not the only experts (Helton, 2004, 2005; in press; Terrace, Son, & Brannon, 2003). In a similar vein to human experts, highly trained canines undergo a long period of formal skill training and practice, varying from 6 months to several years (Fjellanger, Andersen, & McLean, 2000; Helton, 2005, 2006, in press; Marschark & Baenniger, 2002). Helton (2007), for example, found that exceptional dogs in the sport of agility have accumulated over 1,000 hr of task-specific practice. Although this figure may seem trivial in comparison to 10,000 or more hr human experts accrue (Ericsson & Charness, 1994), it is substantially more practice than an animal in a laboratory study typically experiences.
The application of expertise research to nonhuman animals is being debated (Helton, 2005; Rossano, 2003). There are, however, two reasons to remain open to the possibility of nonhuman expertise, especially canine expertise. First, highly skilled canines may serve as convenient, already existing animal models for human experts (Helton, 2004, 2005, 2007). As Helton (2004) argued, dogs may be useful in determining the role of individual differences in expertise acquisition because unlike humans, they are subject to genetic control and their early life experiences can be manipulated (Schmutz & Schmutz, 1998; Slabbert & Rasa, 1997). Studying canines may, therefore, enable the investigation of issues difficult to study in humans. Second, dogs are often required to develop skills in areas of vital societal importance such as accelerant detection, blind assistance, epilepsy detection, explosives detection, forensic tracking, guarding, hearing assistance, herding livestock, medical diagnosis, narcotics detection, detection of insect infestations, and microbial growth (Brooks, Oi, & Koehler, 2003; Fjellanger et al., 2000; Furton & Myers, 2001; Gazit & Terkel, 2003; Holland, 1994; Marschark & Baenniger, 2002; Pickel, Manucy, Walker, Hall & Walker, 2004; Slabbert & Rasa, 1997; Wells & Hepper, 2003). The application of expertise science and methods to animals may assist in the development of more proficient dogs, thereby providing a benefit to society.
The focus of this paper on dogs should not be interpreted in anyway as suggesting that expertise occurs only in humans and dogs, as there is evidence that expertise is spread widely across species (Helton, in press). An extensive learning process has been documented in the acquisition of skills for a variety of animals, such as bees (Dukas & Visscher, 1994; Keasar, Motro, Shur, & Shmida, 1996), birds (Caldow et al., 1999; Yoerg, 1994), cats (Bailey, 1993; Caro, 1994), primates (Lonsdorf, 2005, 2006; Lonsdorf, Eberly, & Pusey, 2004), and spiders (Edwards & Jackson, 1994; Heiling & Herberstein, 1999; Morse, 2000). Skill learning appears to be similar even when remotely related species are compared (Helton, in press). Heiling and Herberstein's research, for example, on spider web design strongly resembles the findings of the knowledge of results (KR) literature on human skill learning (Salmoni, Schmidt, & Walter, 1984). There are, in addition, a number of other species that along with dogs serve useful, expert roles in society, including elephants, horses, rats, and a variety of aquatic mammals (Chatkupt, Sollod, & Sarobol, 1999; Diamond, 1999; Houser et al., 2005; Moore, 1997; Otto, Brown, & Long, 2002). Dogs are, however, a significant and readily available source of nonhuman experts (Helton, 2004).
For dogs to be used in the future as models for human experts or for expertise methodology to be applied to the study of canine expertise, similarities and differences between dog and human skill need to be elucidated. In human studies (Logan, 1985; Proctor & Dutta, 1995), changes in attention during skill development are well established. Fitts and Posner's (1967) three-stage model of expertise development is a case in point. In this model, the initial cognitive stage consists of close attention to cues and feedback. Performance during the cognitive stage is not fluid and requires active coordination of separate skill elements. Skill production during this stage is attentionally demanding. The associative-stage in the model consists of organizing these separate skill elements into larger units or chunks. This organization results in an increase in skill fluidity speed and a decrease in attentional requirements. The final autonomous stage results in the skill becoming relatively independent from cognitive control and attention, or in other words, automatic. The cognitive independence of the skill frees-up attention for coordinating other activities and operations. The development of skill automaticity may also occur in other animals. If this is the case, then animals may provide useful models for testing competing theories of expertise development and automaticity (see Helton, 2004).
In the Fitts and Posner (1967) model, and in other analogous stage models (e.g., Anderson, 1995), the early cognitive stage entails a declarative knowledge component. Animals are not capable of declarative knowledge, if by declarative one literally means the ability to declare in language what one knows. However, Terrace and his colleagues (2003) argued that declarative knowledge and language were distinct and that declarative knowledge predated language in evolution. A knowledge system in which information is encoded analogically as images is an example of a nonverbal declarative knowledge system (Kosslyn, 1980; Terrace et al., 2003). Some animals such as dogs use encoded images to guide their behavior (Adachi, Kuwahata, & Fujita, 2007; Topal, Byrne, Miklosi, & Csanyi, 2006). Regardless of whether dogs possess declarative knowledge or not, stage models of expertise have not been applied to canines and may prove useful in understanding the changes in attention that occur during skill development in canines.
In the present study, Fitts and Posner's (1967) stage model of expertise will be investigated in dogs competing in the sport of agility. Although in the human literature consciousness, automaticity, and attention appear to be conceptually mingled (Moors & De Houwer, 2006; Rossano, 2003), the present investigation focuses on attention and remains agnostic regarding animal consciousness. Researchers have proposed that there is limited attention available for information processing in humans (Helton et al., 2005; Hirst & Kalmar, 1987; Kahneman, 1973) and other animals (Bushnell, 1998; Bushnell, Benignus, & Case, 2003; Gottselig, Wasserman, & Young, 2001). A skill performed without attention is considered to be automatic (Logan, 1985; Moors & De Houwer, 2006). Automaticity is gradated; tasks require more or less attention. Most tasks have some attention cost, although they can be exceedingly small (Paul, Ada, & Canning, 2005). Researchers have suggested that skills become more automatic with practice (Bebko et al., 2003). Skill execution in the early stage of learning requires the executive attention system to actively integrate subcomponents of the skill and coordinate their production (Proctor & Dutta, 1995). The attention demands of skill production is less intense with practice because the subroutines have been stored in larger memory units or chunks (Ericsson & Charness, 1994) and/or the brain has actually reorganized (Foyer-Lea & Matthews, 2004; Hill & Schneider, 2006; Poldrack et al., 2005). Chunking has also been used as an explanation for findings in research with nonhuman animals (Macuda & Roberts, 1995; Terrace, 1987).
Movement Control
Aspects of human bipedal movement control appear to be cognitively demanding and susceptible to interference by other cognitive tasks (Beilock, Wierenga, & Carr, 2002; Bloem, Steijns, & Smits-Engelsman, 2003; Woollacott & Shumway-Cook, 2002). Although alternative explanations exist for some motor control tasks (Beauchet, Dubost, Aminian, Gonthier, & Kressig, 2005; Riley, Baker, Schmit, & Weaver, 2005), there is a growing consensus that the control of human locomotion in constrained settings (Abernethy, Hanna, & Plooy, 2002; Sparrow, Bradshaw, Lamoureux, & Tirosh, 2002) and in response to perturbations (Woollacott & Shumway-Cook, 2002) is cognitively demanding and susceptible to attentional resource limitations. The attention demand of gait and stance control in nonconstrained settings is difficult to discern, especially in healthy adults, because these tasks are overpracticed (Paul et al., 2005; Riley et al., 2005).
Bardy and Laurent (1991) demonstrated increased auditory reaction time (RT) in an unconstrained walking task versus a sitting control condition. Sparrow et al., (2002) investigated the attention costs of walking on RT to auditory and visual signals. Relative to no-walking baseline RT, walking increased the response latency in all conditions. Sparrow et al. included a condition in which they constrained the participants' walking by making them place their feet on specific targets. This constrained condition increased response latency during walking. Walking alone appears to have an attention cost. This cost is increased by additional constraints on walking. The control of motion is likely to require active attention, especially when perturbed or constrained.
Although biped and quadruped locomotion are distinct, quadrupeds, like bipeds, need to actively control their balance, especially during abrupt changes in direction and speed (Lee, Bertram, & Todhunter, 1999). The control of complex movements in animals should be initially attention demanding. Predators, for example, need to become skillful at dealing with movement constraints, as their motion is restricted both by uneven terrain and by an evasive prey. Predators, moreover, need to learn how to deal with perturbations, as many prey animals will literally attempt to knock the predator off balance. The attention costs of motor control should be reduced with practice, freeing resources for other tasks and operations, such as prey detection and decision making. If this is the case with nonhuman quadrupeds, then novice animals will differ from experienced animals in their ability to direct attention to nonmovement control operations.
Caro's (1994) ethological research on cheetahs is one of the most extensive investigations of skill development in predators and provides two important pieces of evidence. First, Caro reported that cheetah mothers provide their offspring with the opportunity to hunt in a controlled environment. The mothers capture live prey and bring it back to their cubs. The mothers then release the live prey and encourage the cubs to chase. These young cheetahs develop the ability to chase, knock down, and suffocate prey without the additional cognitive burdens of actual hunting, like prey detection and decision making. Second, one of the primary differences between novice adolescent cheetahs and experienced adult cheetahs is the average distance of abandoning a chase. Adolescents on average abandon a chase after 18 m, whereas adults abandon chase after only 2 m. Perhaps, adult cheetahs have more skill or attention available for reading prey signals and are, therefore, more aware of when a chase is futile.
Field studies with predators limit one's ability to rule out nonlearned, innate mechanisms. Cheetahs have evolved over a long period of time to be successful predators and these developmental changes noted by Caro (1994) may reflect underlying innate mechanisms. There is, however, a quadruped that develops skills in areas that are evolutionarily novel and therefore, are unlikely to be due to innate mechanisms: the working dog.
Canine Agility
Canine athletes, similar to human athletes, undergo a long period of formal skill training and practice (Helton, 2006, 2007). The popularity of training dogs for activities makes finding highly skilled canines relatively easy. In the current study, canine agility performance will be examined. Agility is a relatively new sport. It was developed in the 1970s and is, therefore, evolutionarily novel. The sport of agility involves a dog running through an obstacle course made up of inclined walls (A-frames), hurdles, tunnels, chutes (collapsed cloth tunnels), elevated dog walks, weave-poles, and see-saws. The dogs must follow a prescribed path through the obstacles and are directed by a handler using gestures and vocal commands. Faults are given for mistakes and speed is calculated. The sport involves endless variation as the placement of the obstacles is not static (for a picture of an agility competition, see Figure 1).
xap-13-3-171-fig1a.gifFigure 1. Two pictures of an actual agility competition. The obstacles are mobile and can be reconfigured to form endless variations. The handler is given a particular path through the obstacles and must direct the dog through the correct path.
Agility is interesting for researchers of motor control because the dog needs to simultaneously control body movement and detect handler signals. When completing an agility course, the dog is performing two tasks: (a) listening and looking for commands (handler signals); and (b) controlling movement in a constrained environment. Moreover, these dogs do not stop moving to detect handler signals; signals are detected while the dog is moving. Agility, in addition, enables the researcher to quantify both motor performance and signal detections. The dog's speed and various types of precision can be measured separately, enabling the examination of different skill components.
In the present investigation, novice, intermediate, advanced, and expert American Kennel Club (AKC) dogs will be compared for their running speed and various types of precision on a full agility course. More highly skilled dogs will, unquestionably, have overall better performance. The critical issue is whether the limited attention theories developed by researchers to explain human skill development can be applied to agility dogs. If they can be, then a distinct pattern should be noted. Initially, dogs' motor performance should be attentionally demanding, therefore, improvements in motor control should develop early. Once motor skills become sufficiently chunked or automatized, attention should be freed, enabling an increase in signal detections and running speed. The dogs' obedience to simple commands will also be examined to rule out the alternative possibility that any differences detected between the dogs of varying expertise levels are due simply to changes in self-control, obedience, or willingness to comply to the handler's commands. There should be little difference between the dogs of various expertise levels in their ability to follow simple commands, like to sit and stay, or in their self-control.
Method
Participants
Participants were 60 dogs and their handlers. The dogs and handlers were recruited at the Queen City Dog Training Club in Cincinnati, Ohio, an AKC affiliated center. The Club is nationally recognized as a premiere agility training facility and has produced a number of AKC champions. Only experienced handlers, with prior experience working with advanced and expert dogs, were included to reduce, though not completely eliminate, the impact of handler expertise experience. The dogs consisted of 15 each from four levels of ability: novice, intermediate, advanced, and expert. The determination of a dog's expertise level was made using the AKC's preestablished competitive designations (see http://www.akc.org/events/agility/index.cfm). The novice dogs in this study were not naïve. The dogs had some training in the sport of agility and were familiar with the obstacles. The dogs were matched for height (leg length), a factor that influences running speed in the sport (Helton, 2006, 2007) with 1 in. of tolerance. Where possible dog breed was matched across levels; however, as previous research does not indicate a significant effect for breed on agility performance when height is controlled, height was given higher priority for matching purposes (Helton, 2006, 2007). The dogs ranged in age from 2 years to 7 years (M = 3.6 years, SD = 1.5 years).
Procedure
The dogs were assessed at the training club over 3 days. The Queen City Dog Training Center is a 9,600 sq. ft. climate controlled building with antislip matting built especially for the sport of agility. The dogs competed in full agility courses consisting of all obstacle types and their respective combinations. Course length and the exact number of obstacles employed depended on the dogs' competitive abilities. More skilled dogs ran slightly longer courses than their less skilled counterparts. The height of jumping obstacles is adjusted for the height of the dog, as is the standard in the sport. All dogs competed at least two runs on separate days. The obstacles were arranged by an experienced AKC course setter for each session. The course lengths ranged from 127 to 173 yards (M = 152.1, SD = 16.4) with 15 to 19 obstacles (M = 17.5, SD = 1.6). The time to complete a course ranged from 43.9 to 101 seconds (M = 62.8, SD = 12.5).
In this study, performance measures were assessed from the dogs' runs for both speed and precision. Speed was the average time for a run, regardless of number of faults made during the course. The speed of each run was calculated by dividing the distance of the course measured in yards by the time of the run measured in seconds (yd/sec). Course time was measured using Signature Gear electronic timers (Signature Gear Corporation, Saint Louis, Missouri). These electronic timers are specifically designed for agility competitions and are accurate to 1 ms.
In agility, faults are given for a number of inappropriate actions by the dog. Different types of faults can be distinguished. Three types of faults of interest are refusals-runouts (R), obstacle faults (O), and table faults (T). A refusal is when a dog starts toward an obstacle and ceases forward movement. A runout is when the dog passes by the next correct obstacle. An obstacle fault is given when the dog fails to perform on an obstacle, for example, not touching contact zones or knocking bars on jumps. A table fault is when a dog leaves a rest or pausing zone, typically an elevated platform, prematurely.
The kind of faults made may be diagnostic of underlying skill differences between dogs of differing levels of ability. The exact underlying nature of these faults is open to speculation; however, they are objectively different in nature. R faults are made when the dog is not committing to an obstacle. These errors may indicate an underlying state of signal uncertainty. In the case of a refusal, the dog is second guessing the handler's signal, turning back to the handler, perhaps, for verification. In the case of a runout, the dog most likely missed a handler signal. An R fault, therefore, most likely reflects a missed or nearly missed handler signal. O faults are motor skill errors; the dog whereas engaging with the obstacle, fails to do so appropriately. T faults are errors of obedience or dog self-control. The handler indicates to the dog to sit and stay on the platform, but before being given the release command by the handler, the dog prematurely releases him or herself.
In addition to speed, the three different types of precision were collected: refusal/runout (R), obstacle (O), and table (T). To calculate precision values, the fault types were summed for each dog and divided by the total number of runs the dog ran. A constant (1) was added to these values and they were inverted (1/(x + 1)) to ensure normality (Kirk, 1995). The constant was added to deal with cases of zero faults. For these metrics, a higher value reflects more precision. These faults were determined by AKC-qualified agility judges who were blind to the purpose of the study. As is typical in the sport of agility, a primary ring judge determined the faults. At least two other judges were present and could be queried about unclear cases; none, however, occurred.
Results
To assess whether there were speed–accuracy trade-offs, the relationships between speed and the precision measures were calculated using Pearson product–moment correlations. Speed was significantly correlated with O precision, r(58) = .44, p < .01; and R precision, r(58) = .43, p < .01; but not with T precision, r(58) = .21, ns. O and R precision were significantly correlated with each other, r(58) = .42, p < .01; but neither O precision, r(58) = .20, ns; nor R precision, r(58) = .23, ns; were correlated significantly with T precision. There was no evidence for speed–accuracy trade-offs observed between dogs; more precise dogs tended to be faster, not slower.
Speed and the precision measures were analyzed with orthogonal comparisons (Keppel & Zedeck, 2001). Depending on whether the performance metric was hypothesized to differ late or early in skill development, Helmert or reverse-Helmert contrasts were conducted. For Helmert contrasts, each group was compared to the mean of all subsequent groups, whereas for reverse-Helmert contrasts each group is compared to the mean of all previous groups (Field, 2000). The speed means for the four expertise groups are displayed in Figure 2. For the speed reverse-Helmert contrasts (MSE = 2.84), experts were significantly different from the average of the other groups, t(56) = 5.99, p < .01, d = 1.60; advanced dogs were significantly different from the average of intermediates and novices, t(56) = 3.81, p < .01, d = 1.02; and intermediates were not significantly different from novices, t(56) = 1.76, ns, d = 0.47.
xap-13-3-171-fig2a.gifFigure 2. The mean running speeds (yd/sec) for the four expertise groups (error bars are 95% confidence intervals).
The precision scores for each expertise group can be seen in Figure 3. For T-precision Helmert contrasts (MSE = 0.01), novices did not differ significantly from the average of the other groups, t(56) = 1.00, ns, d = 0.27; intermediates did not differ significantly from the average of advanced and experts, t(56) = 0.64, ns, d = 0.17; and advanced dogs did not differ significantly from experts, t(56) = 0.24, ns, d = .06. For O-precision Helmert contrasts (MSE = 0.37), novices were significantly different from the average of the other groups, t(56) = 4.77, p < .01, d = 1.27; intermediates did not differ significantly from the average of advanced and experts, t(56) = 0.50, ns, d = 0.13; and advanced dogs did not differ significantly from experts, t(56) = 1.68, ns, d = 0.45. For R-precision reverse-Helmert contrasts (MSE = .35), experts were significantly different from the average of the other groups, t(56) = 4.53, p < .01, d = 1.21; advanced dogs did not differ significantly from the average of intermediates and novices, t(56) = 0.61, ns, d = 0.16; and intermediates did not differ significantly from novices, t(56) = 0.99, ns, d = 0.26.
xap-13-3-171-fig3a.gifFigure 3. The mean precision measures for the four expertise groups (error bars are 95% confidence intervals).
Discussion
As predicted, dogs of varying expertise levels differed significantly on objective measures of performance. R precision may be indicative of signal detections. The large differences between experts and novices for the amount of R precision indicate that a major aspect of dog agility skill is learning to accurately detect handler signals (e.g., to not miss them). The clear speed and O-precision differences between experts and novices indicate that along with the perceptual-cognitive skill learning, agility skill entails substantive changes in motor control. The expert dogs are not only more careful when moving through and on to obstacles (more O precision), they are also moving more quickly (speed). The lack of significant differences between dogs of different expertise levels for T precision and the overall high T-precision values, regardless of expertise level, may indicate a ceiling effect. To compete in agility, dogs are already under control and obey basic commands accurately.
Faster and more precise expert dogs are expected, as agility dogs' designated expertise levels should be based on objective features of performance. More critical was the unique pattern of skill differences with increasing agility expertise. In line with the limited attention theories developed by researchers using human participants (Bloem et al., 2003; Fitts & Posner, 1967; Woollacott & Shumway-Cook, 2002), a distinct pattern in the dogs' performance was predicted. From this perspective the dogs' motor performance initially should be attentionally demanding. Therefore, improvements in motor control should come earlier, namely O-precision improvements should come before improvements in signal detection, or R precision. Once motor skills become sufficiently chunked or automatized, then attentional resources should be freed, enabling an increase in R precision (signal detections) and an overall increase in running speed. The results of this study corroborate this theoretical pattern.
As can be seen in Figure 3, the largest sequential difference in R precision (signal detections) occurred when experts were compared to advanced dogs (d = 0.90), whereas the largest sequential difference in O precision occurred between novice and intermediate dogs (d = 0.96). Speed increases across the expertise levels; however, noticeable changes in speed occurred when intermediates were sequentially compared to advanced dogs (d = 0.65) and advanced dogs were compared to experts (d = 0.72), slightly later in skill level than the major O-precision gains. Although improvement in motor control continues throughout levels, the majority of the gains in these abilities occur at skill levels lower than the expert designation. This finding may indicate that earlier in skill development motor control is attentionally demanding and then later becomes increasingly automated, first allowing increases in speed and then finally major increases in signal detections.
The results of this study fit nicely within Fitts and Posner's (1967) three-stage model of expertise development. In their model, the initial cognitive stage consists of close attention to cues and feedback. Performance during the cognitive stage is not fluid and requires the active, attentive coordination of the separate skill elements. The transition from novice to intermediate is marked by improvement in motor control (O precision), or in other words, the accurate production of the individual motor elements. The next associative stage in their model consists of organizing these separate skill elements into larger units or chunks. This organization results in an increase in skill fluidity and speed. The transition from intermediate to advanced is marked by an increase in speed, or in other words, the fluidity of skill production. The final automatic or autonomous stage consists of the skill becoming independent from cognitive control and attention. The independence of the skill frees-up attention for coordinating other activities and operations. The transition from advanced to expert in this study is marked by an increase in signal detections (R precision), a task that presumably requires attention.
There are alternative explanations for the reported findings. One alternative explanation is that only exceptional dogs, those that make no faults to begin with, continue on to become experts. Handlers may, moreover, selectively remove dogs from competition. Handlers may first remove those dogs who fail to show motor expertise (O precision) and then eventually those dogs failing to detect signals (R precision). Because the data collected for this study were not longitudinal, this explanation is possible. There is some evidence that suggests that although possible, this explanation is implausible. First, looking at the overall fault rates, 6 of the 15 expert dogs had a completely flawless performance, whereas none of the 15 novices had a flawless performance. As far as can be determined, both from the data at hand and from conversations with AKC officials, flawless novice dogs do not exist, so there would be none to select out. There is, in addition, no reason to suspect a fault specific (O vs. R) weeding out process from one competitive level to the next. This issue will hopefully be resolved in the future with the employment of longitudinal studies, in which individual dogs are tracked from the beginning of their training.
Another possible issue is handler confounding. Agility is a team sport in which handlers play a critical role in directing the dogs' movements. It could be argued that the findings of this study may be due to handler differences instead of dog differences. Before continuing too far with this line of reasoning, one point should be made clear: the novice and intermediate dogs' handlers were not agility novices themselves. To be included in this study, all handlers needed to have previously competed with a dog at an advanced or expert level. Thus, whatever signaling method the handlers of novice and intermediate dogs employed had worked previously with a highly skilled dog. Although there will be differences in the exact signaling method a handler employs, the dogs' skill is to learn to detect their handler's signals. Handler differences do not, moreover, explain the developmental pattern in regards to motor skill (O precision).
Regardless of possible objections and alternative explanations, the findings of this study are intriguing and deserve closer examination. From a practical perspective, agility may prove useful as a real-world task revealing changes in attention during skill development and automaticity in animals. If, as is being suggested, some aspects of agility skill are automatizable in dogs, then dogs may serve a role in studying automaticity in general. A successful animal model of the development of skill automaticity may prove useful in the future for examining factors that influence skill automaticity that are difficult or restricted in human participants (e.g., underlying genetic factors and early rearing practices). Dogs may prove useful in this regard as they are subject to genetic control and their early life experiences can be manipulated (Schmutz & Schmutz, 1998; Slabbert & Rasa, 1997).
The dual-task paradigm is commonly used to measure task automaticity in humans (Abernethy, 1988; Pashler, 1994, 1998). Automatic tasks place little burden on attention and, subsequently, performance on concurrent secondary tasks are unaffected. Nonautomatic tasks, however, compete with secondary tasks for attention. This competition between the tasks for limited resources leads to performance deterioration in one or both of the tasks. Although agility is a natural dual task, with movement and signal detection occurring simultaneously, dual-task methodology was not rigorously employed in this study. Researchers in the future could emulate the dual-task method used in human expertise research (Beilock et al., 2002; Leavitt, 1979; Smith & Chamberlin, 1992) with agility dogs. The results of this study will hopefully encourage researchers to conduct these studies.
Although they do not rule out alternative explanations, these preliminary findings are provocative. The results of this study intriguingly match expectations based on Fitts and Posner's (1967) stage model of expertise development. Undoubtedly, more research is warranted. The sport of agility offers researchers an excellent setting to test theories of skill automaticity and executive attention in a nonhuman species. Regardless of its impact on the theoretical understanding of expertise, this paper provides an initial step toward a science of canine expertise. The need to study the operational performance of working dogs is critical. Knowing how an explosive detection dog works, for example, is as important as knowing how a human luggage inspector works, if the goal is to keep our airliners safe. More specific to the present study, K–9 law-enforcement dogs are actually trained in agility, in a manner very similar to the dogs in this study. The K–9s need to navigate quickly through all types of obstacles, while they simultaneously process the verbal and gestural commands from their human partners. Further research in this area would be extremely useful in understanding canine expertise and developing more proficient canine workers.
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Submitted: November 27, 2006 Revised: June 8, 2007 Accepted: June 11, 2007
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cognition in domestic dogs: object permanence & social cueing
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Title: Cognition in Domestic Dogs: Object Permanence & Social Cueing
Author(s): Clotfelter, Ethan D.; Hollis, Karen L.
Source: American Biology Teacher, v70 n5 p293-298 May 2008. 6 pp.
Peer Reviewed: Yes
ISSN: 0002-7685
Descriptors: Animals, Object Permanence, Cognitive Processes, Memory, Cognitive Ability, Brain Hemisphere Functions, Science Instruction, Laboratory Experiments, College Science, Science Experiments, Teaching Methods, Cues, Research Design, Research Methodology, Higher Education
Abstract: Cognition is a general term describing the mental capacities of an animal, and often includes the ability to categorize, remember, and communicate about objects in the environment. Numerous regions of the telencephalon (cerebral cortex and limbic system) are responsible for these cognitive functions. Although many researchers have used traditional laboratory animals such as rodents and pigeons in the study of animal cognition, an increasing number of studies focus on species such as non-human primates, dolphins, and domestic dogs ("Canis familiaris"). Such studies can provide insight into the evolution of cognitive processes in humans. In this article, the authors describe a laboratory exercise that they have used with college students, although the exercise would be equally effective at the middle- or high-school levels. The primary objective of this exercise is to use an animal familiar to all students, the domestic dog, to examine the phenomena of object permanence and social cueing. More specifically, the approach described here will teach students about the importance of careful experimental design and the interpretation of data. (Contains 3 tables and 3 figures.)
Abstractor: ERIC
Number of References: 13
Number of Pages: 6
Intended Audience: Teachers
Publication Type: Guides - Classroom - Teacher; Journal Articles; Reports - Descriptive
Availability: National Association of Biology Teachers. 12030 Sunrise Valley Drive #110, Reston, VA 20191. Tel: 800-406-0775; Tel: 703-264-9696; Fax: 703-264-7778; e-mail: publication@nabt.org; Web site: http://www.nabt.org
URL: http://www.nabt.org
Journal Code: FEB2014
Entry Date: 2008
Accession Number: EJ796402
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INQUIRY & INVESTIGATION
Cognition is a general term describing the mental capacities of an animal, and often includes the ability to categorize, remember, and communicate about objects in the environment. Numerous regions of the telencephalon (cerebral cortex and limbic system) are responsible for these cognitive functions. Although many researchers have used traditional laboratory animals such as rodents and pigeons in the study of animal cognition, an increasing number of studies focus on species such as non-human primates, dolphins, and domestic dogs (Cams familiaris). Such studies can provide insight into the evolution of cognitive processes in humans (Miklósi et al., 2004; Gomez, 2005). Here we describe a laboratory exercise we have used with great success with college students, although the exercise would be equally effective at the middle- or high-school levels. The primary objective of this exercise is to use an animal familiar to all students, the domestic dog, to examine the phenomena of object permanence and social cueing. More specifically, the approach described here will teach students about the importance of careful experimental design and the interpretation of data. In this regard, the activity is consistent with the National Science Education Standards (National Research Council, 1996) in that it encourages the skills of scientific inquiry (Standards B and E), allows students to engage in extended investigations (Standard D), and nurtures collaboration among students (Standard E). In addition, this exercise aligns with the National Standard that secondary science teachers should be able to demonstrate "Measurement as a way of knowing and organizing observations of constancy and change."
Object Permanence
A relatively simple form of cognition is illustrated by the phenomenon known as object permanence, the awareness that objects exist even when they are not visible. Object permanence is a universal characteristic among primates (Gomez, 2005). Most studies of object permanence utilize the scheme originally conceived by the Swiss developmental psychologist Jean Piaget from his studies of human infants and toddlers (e.g., Piaget & Inhelder, 1969; Table 1). In the 1980s and 1990s, comparative psychologists began applying this same scheme to dogs (Triana & Pasnak, 1981; Gagnon & Doré, 1992, 1993). Some studies found that dogs perform at a level consistent with a one- to two-year-old human toddler, meaning that they usually score better than 50% on Visible and Invisible Displacement Tests (see below). Other studies have suggested that dogs may simply associate the location of the hidden object with some fixture of the environment (Collier-Baker et al., 2004). Thus, the degree of object permanence in domestic dogs is still very much a question of scientific debate.
Social Cueing
A separate but equally interesting area of research focuses on the ability of animals to follow social cues from humans. The most frequently investigated cue is pointing toward a hidden object, which has been studied in a diverse array of mammals including non-human primates, dogs, wolves, goats, and cetaceans (Miklósi & Soproni, 2006). Interestingly, dogs perform much better at this task than do either wolves (Canis lupus) or chimpanzees (Pan troglodytes), which originally led some researchers (Hare et al., 2002) to speculate that domestication selects for a particular set of cognitive abilities that allow for human-dog communication. More recent work (Hare et al., 2005) suggests that social cueing may simply be a byproduct of the domestication process itself. At a minimum, dogs' ability to follow social cues means that they quickly learn to associate human pointing with rewards such as food or toys. A more intriguing possibility, however, is that dogs might possess what cognitive psychologists call a "theory of mind," the ability to recognize another individual — in this case a human — as a separate, sentient being in possession of information different from what the dog possesses.
How To Get Study Subjects
We have had great success in enlisting assistance from faculty and staff dog-owners at our respective institutions. Most owners are interested in learning more about animal cognition, are delighted to participate (even multiple times), and are quite curious as to how their dogs perform relative to their peers. Students are encouraged to seek out dogs belonging to friends and neighbors, as long as the testing sessions follow the same protocol. Students and teachers at the college level should be advised that any animal usage on college property is regulated by an Institutional Animal Care and Use Committee (IACUC). Because dogs will be tested in owners' homes, IACUC approval typically is not required; nonetheless, we suggest contacting the IACUC. Students and teachers at all levels should be aware of the possible safety issues involved in working with other peoples' dogs and should involve the owner in the testing procedure to the greatest extent possible.
Things To Do Before Each Testing Session Begins
Our own classes are typically 15-25 students; and we typically have students work in groups of two to four, with each group responsible for collecting data on eight to ten dogs. Data can be pooled from all groups, if needed. Prior to testing, we often bring a dog into the classroom to demonstrate proper testing procedure. Testing sessions require a minimum of two people, one of whom can be the owner of the dog. The students should select a testing site that is familiar to the dog (owner's home, office, yard, or nearby park) and has a minimum of distractions. With the exception of students with severe dog allergies or phobias, all students should familiarize themselves with the dog being tested. This familiarization process will minimize distractions during the testing process. During this time, the students can ask the owner to fill out the Dog Personality Questionnaire (see below).
The students should determine what breed of dog they're working with, and assign it to one of the eight AKC groups listed below in Table 2. In our experience, mixed-breed dogs often out-perform purebreds; thus, we usually add a ninth category to accommodate mixed breeds. The students should also find out the age and sex of the dog, and whether it has been spayed or neutered. The students should question the owner as to what test object (food, ball, or chew toy) would generate the most interest from the dog. In our experience, the vast majority of dogs are preoccupied with food, and we usually supply each group of students with a box of dog biscuits or soft dog treats for this purpose. Depending on the dog's level of interest in the object, students may need to play with it for several minutes before testing. If food is used as the test object, the students should not test a dog immediately after it has eaten. In our experience, the most reliable test object is a food item (dog biscuit). Thus, we use "food" for the remainder of this article.
Setting Up a Testing Session
The materials needed are relatively simple: the test object (food), a clipboard for recording data, a stopwatch or wristwatch, three containers of uniform size and color, and one container small enough to fit inside the others. We usually use three 5-gallon buckets and one 1-gallon bucket for our containers, but some dogs are fearful of these large buckets. Alternatively, students can use three 1-gallon paint buckets and a styrofoam cup. The students should arrange the three buckets in a semi-circle such that they are equidistant from the dog. The buckets should be placed upright and approximately one meter apart. The students (with help from the owner) should position the dog approximately 2-3 meters from the buckets. To visualize the spatial arrangement of dog and buckets, imagine a clock face in which the buckets are placed at 11, 12, and 1 and the dog is positioned at 6. To maintain consistency among trials, the students could ask the owner to mark the dog's position with masking tape.
Single Visible Displacement Test
The first test of object permanence is called the Visible Displacement Test, so called because the dog sees the experimenter hide (or displace) the food. The student playing the role of "experimenter" should walk back and forth behind the buckets and hold the food such that the dog can see it. Once the student is sure he/she has the dog's attention, the student should place the food inside one of the buckets. Another student records time on the stopwatch. After 10 seconds, the owner or handler can release the dog. Score the trial as successful if the dog attempts to move or displace the correct bucket. Score the trial as unsuccessful if the dog approaches the incorrect bucket to within one body length or does not make an appropriate response for 30 seconds. Repeat three times for each dog, each time using the same bucket. If a dog is successful in two or three trials, it has reached Stage 4 (Table 1). We have found that a 2-out-of-3 success rate is more conservative and precludes dogs that are making random selections.
Sequential Visible Displacement Test
Position the dog and buckets as before. Conduct these trials exactly as before, only this time conceal the food under a different bucket; remember to use the same bucket for each of the three tests. If the dog chooses the bucket under which the food was previously concealed, it is making what are called "A-not-B" errors and, thus, has not progressed past Stage 4. If the dog successfully locates the food under the new bucket, it has reached Stage 5 (Table 1). Remember that a dog who was unsuccessful in the first test but successful in the second test may not necessarily have learned anything the first time, and therefore may still be in Stage 4.
Invisible Displacement Test
Position the dog and buckets as before. The student ("experimenter") standing behind the bucket should hold the food in his/her hand, showing it to the dog. Once the dog sees the food, the student should place the object into the smaller container. The student should be sure that the dog sees the placement of the food. Put the small container inside one of the big buckets (still in place on the floor), remove the object, and place it inside the big bucket. Lift the small container up in the air and rotate it toward the dog to show that it is empty. Wait for 10 seconds and then release the dog. Score the trial as successful if the dog attempts to move or displace the correct bucket, or if the dog looks inside the correct bucket. Score the trial as unsuccessful if the dog chooses the incorrect bucket or does not make an appropriate response for 30 seconds. Conduct three tests, hiding the food inside the same bucket each time. If the dog is successful two or three times, it has reached Stage 6 of object permanence. To determine whether dogs truly have Stage 6 object permanence, we would ideally subject them to successive and alternating invisible displacement tests, but time- or dog-related constraints may not make this additional testing feasible.
Social Cueing
Remove the dog from the room or area. Remove one of the buckets and re-arrange the two remaining buckets such that they are equidistant from the dog's position. Hide the food inside one of the buckets. Bring the dog back into the room and position it in the same place as before. While the owner gently restrains the dog, the experimenter should stand behind the buckets and point and gaze at the bucket that conceals the hidden object for 10 seconds. The owner or handler can then release the dog. Score the trial as successful if the dog chooses the correct bucket, unsuccessful if it chooses the incorrect bucket or makes no appropriate choice. Conduct four tests, changing the position of the target object so that each bucket holds the object twice (remember to remove the dog each time). The dog must be successful on at least two consecutive tests to have succeeded at the task.
Dog Personality Questionnaire
An active area of animal behavior research is the study of behavioral syndromes, which are suites of correlated behaviors roughly analogous to an animal's "personality" (Sih et al., 2004). A key question in behavioral syndrome research is whether a set of behaviors that is adaptive in one context might be maladaptive in another. For example, a bold and inquisitive animal might be highly successful in situations where such behaviors are called for, but significantly less so in situations where the risk of predation or other hazards is high. Thus, in addition to our studies of canine object permanence and social cueing, we have sought to quantify dog personality to determine which behavior patterns are associated with cognitive performance. Table 2 represents a Dog Personality Questionnaire we have adapted from the Internet (© Wendy Volhard, http://www.volhard.com/training/cpp.htm). This questionnaire, originally conceived for dog training purposes, includes questions designed to assess a dog's predatory instincts, desire to be part of a pack, motivation for fighting, and tendency to flee. For each of these "drives" the questionnaire will generate a numerical score between 0 and 100. Other dog personality questionnaires are also available online (e.g., http://www.lincoln.ac.uk/dbs/staff%5fprofile/h%5fwright.htm). These personality scores can be used for additional analyses, if desired (see below).
Data Analysis
Endless possibilities exist for data analysis, which depend on student interest and the number of dogs to which the students have access. The simplest approach is to pool all dogs together and calculate the mean success rate (percentage or proportion) on each of the Object Permanence and Social Cueing Tests (Figure 1). That is, each dog would score 0%, 33%, 66% or 1.00% on the Object Permanence Test or 0%, 25%, 50%, 75% or 100% on the Social Cueing Test. In addition, students can analyze success on the tests as a function of dog sex (creating two additional categories of neutered and spayed dogs often produces interesting results), dog age (for example, categories of 0-1 years, 2-3 years, 4-6 years, 7-9 years, and 10+ years), or AKC breed group (Table 3, Figure 2). More advanced students could test for statistical differences between (t-tests) or among (analysis of variance) groups using statistical analysis software. The "Data Analysis" function under "Tools" in Microsoft Excel™ is capable of conducting both of these tests. First, organize your data into columns. An example of data for a two-sample t-test would be success (as proportions or percentages) in Visible Displacement Tests by mixed-breed dogs in one column and success in Visible Displacement Tests by purebred dogs in another column. Data to be analyzed by analysis of variance (ANOVA) would be in three or more columns (e.g., success in invisible displacement tests by multiple breed classes or age groups). A number of online resources may be consulted for discussion of how to interpret the results of these statistical tests (e.g., http://pareonline.net/getvn.asp?v=4&n=5).
Finally, students could create simple scatter plots in which each dog's score on the social cueing test (0-100%) is plotted against its score (0-100) on each of the dog personality profiles. For more advanced students, correlation coefficients and significance (P or α) values for each scatter plot could be calculated using statistical analysis software. As an example, Figure 3 illustrates a strong correlation between hypothetical social cueing scores and total pack drive scores (r = 0.81, P < 0.001).
An important aspect of scientific research, and an essential feature of classroom enquiry (National Research Council, 2000) is communicating one's results to the public. We have found that it is informative for students, as well as gratifying for dog owners, to prepare written reports detailing their findings. Students may want to protect the sensitive egos of the dog owners, however, by withholding the identity of the dogs in their study.
Integration into Curricula
This exercise has been used successfully in animal behavior courses at Amherst and Mt. Holyoke Colleges, and it could also be integrated into college-level courses on developmental or comparative psychology. Based on conversations we have had with high school teachers throughout New England, there are ample opportunities for integrating this exercise into high school curricula. For example, many schools have electives in physiology (some of which include neuroscience units), psychology, or even animal behavior. The Advanced Placement Biology curriculum includes a unit on animal behavior (taxis and kinesis), the emphasis of which is similar to our emphasis here: experimental design and interpretation of results. Thus, this exercise on dog cognition could supplement that part of the curriculum, particularly because (after some training) it can be performed by groups of students outside of the classroom.
Table 2a. Dog Personality Questionnaire (Adapted from © Wendy Volhard, http://www.volhard.com/training/cpp.htm).
For each answer, please choose: Almost always ( 10) Sometimes ( 5) Hardly ever (0)
1. Does YOUR DOG sniff the ground or air a lot?
2. Does YOUR DOG get along with other dogs?
3. Does YOUR DOG stand its ground or investigate strange objects or sounds?
4. Does YOUR DOG run away from new situations?
5. Does YOUR DOG get excited my moving objects, such as bikes or squirrels?
6. Does YOUR DOG get along with people?
7. Does YOUR DOG like to play tug-of-war games to win?
8. Does YOUR DOG hide behind you when unable to cope?
9. Does YOUR DOG stalk cats, other dogs, or things in the grass?
10. Does YOUR DOG bark when left alone?.
11. Does YOUR DOG bark or growl in a deep tone?
12. Does YOUR DOG act fearful in unfamiliar situations?
13. Does YOUR DOG, when excited, bark in a high-pitched voice?
14. Does YOUR DOG solicit petting or like to snuggle with you?
15. Does YOUR DOG guard territory?
16. Does YOUR DOG tremble or whine when unsure?
17. Does YOUR DOG pounce on toys?
18. Does YOUR DOG like to be groomed?
19. Does YOUR DOG guard food or toys?
20. Does YOUR DOG crawl or turn upside down when reprimanded?
21. Does YOUR DOG shake and "kill" toys?
22. Does YOUR DOG seek eye contact with you?
23. Does YOUR DOG dislike being petted?
24. Is YOUR DOG reluctant to come close to you when called?
25. Does YOUR DOG steal food or garbage?
26. Does YOUR DOG follow you around like a shadow?
27. Does YOUR DOG dislike being groomed or bathed?
28. Does YOUR DOG have difficulty standing still when groomed?
29. Does YOUR DOG like to carry things?
30. Does YOUR DOG play a lot with other dogs?
31. Does YOUR DOG guard the owner(s)?
32. Does YOUR DOG cringe when someone strange bends over him/her?
33. Does YOUR DOG wolf down food?
34. Does YOUR DOG jump up to greet people?
35. Does YOUR DOG like to fight with other dogs?
36. Does YOUR DOG urinate during greeting behavior?
37. Does YOUR DOG like to dig and bury things?
38. Does YOUR DOG show reproductive behaviors, such as courting or mounting other dogs?
39. Does YOUR DOG get picked on by other dogs (either now or when it was young)?
40. Does YOUR DOG tend to bite when cornered?
Table 2b. Scoring the Dog Personality Questionnaire. Write the scores from each question in the spaces below to obtain prey drive, pack drive, fight drive, and flight drive scores for each dog (© Wendy Volhard, http://www.volhard.com/training/cpp.htm)
1. 2. 3. 4.
5. 6. 7. 8.
9. 10. 11. 12.
13. 14. 15. 16.
17. 18. 19. 20.
21. 22. 23. 24.
25. 26. 27. 28.
29. 30 31. 32.
33. 34. 35. 36.
37. 38. 39. 40.
Total prey Total pack Total fight Total flight
drive: drive: drive: drive:
Table 1. Jean Piaget's stages of object permanence in human infants and toddlers
Stage 1 (0-1 month) No response to disappearance
Stage 2 (1-4 months) Coordinating modalities — e.g.,
looking for source of sound
Passive expectation — continued gaze
at point of disappearance
No following of dropped object or
anticipation of trajectory
Stage 3 (4-8 months) Visual anticipation of trajectory —
looks where dropped object is expected to
land
Responds to game of peek-a-boo
No retrieval of fully hidden object
Stage 4 (8-12 months) Retrieval of fully hidden object
Makes A-not-B errors (persistent searching
for hidden object in location where object
previously was found)
Stage 5 (12-18 months) Success on A-not-B task
Failure on invisible displacements —
search confined to visible hiding places
Stage 6 (18-24 months) Success on all of above
Table 3. Breed groups recognized by the American Kennel Club (www.akc.org)
Hunting Group Hound Group Working Group Terrier Group
Brittany Afghan Hound Akita Airedale Terrier
Pointer American Alaskan Malamute American
Foxhound Staffordshire
Terrier
German Basenji Anatolian Australian Terrier
Shorthaired Pointer Shepherd
German Basset Hound Bernese Mountain Bedlington Terrier
Wirehaired Pointer Dog
Chesapeake Bay Beagle Boxer Border Terrier
Retriever
Curly-Coated Black and Tan Bullmastiff Bull Terrier
Retriever Coonhound
Flat-Coated Bloodhound Doberman Cairn Terrier
Retriever Pinscher
Golden Retriever Borzoi German Pinscher Dandie Dinmont
Terrier
Labrador Dachshund Giant Schnauzer Irish Terrier
Retriever
English Setter English Great Dane Kerry Blue Terrier
Foxhound
Gordon Setter Greyhound Great Pyrenees Lakeland Terrier
Irish Setter Harrier Greater Swiss Manchester Terrier
Mountain Dog (Standard)
American Water Ibizan Hound Komondor Miniature Bull
Spaniel Terrier
Clumber Spaniel Irish Wolfhound Kuvasz Miniature Schnauzer
Cocker Spaniel Norwegian Mastiff Norfolk Terrier
Elkhound
English Cocker Otterhound Newfoundland Norwich Terrier
Spaniel
English Springer Petit Basset Portuguese Parson Russell
Spaniel Terrier Griffon Water Dog
Vendéen
Field Spaniel Pharaoh Hound Rottweiler Scottish Terrier
Irish Water Rhodesian Saint Bernard Sealyham Terrier
Spaniel Ridgeback
Nova Scotia Saluki Samoyed Skye Terrier
Duck Tolling
Retriever
Spinone Italiano Scottish Siberian Husky Smooth Fox Terrier
Deerhound
Sussex Spaniel Whippet Standard Soft Coated
Schnauzer Wheaten Terrier
Welsh Springer Staffordshire Bull
Spaniel Terrier
Vizsla Welsh Terrier
Weimaraner West Highland
White Terrier
Wirehaired Pointing Wire Fox Terrier
Griffon
Toy Group Non-Sporting Herding Group Miscellaneous Group
Group
Affenpinscher American Australian Beauceron
Eskimo Dog Cattle Dog
Brussels Griffon Bichon Frise Australian Plott
Shepherd
Cavalier King Boston Terrier Bearded Collie Redbone Coonhound
Charles Spaniel
Chihuahua Bulldog Belgian Malinois Swedish Vallhund
Chinese Crested Chinese Belgian Sheepdog Tibetan Mastiff
Shar-pei
English Toy Chow Chow Belgian Tervuren
Spaniel
Havanese Dalmatian Border Collie
Italian Greyhound Finnish Spitz Bouvier des Flandres
Japanese Chin French Bulldog Briard
Maltese Keeshond Canaan Dog
Miniature Lhasa Apso Cardigan Welsh Corgi
Pinscher
Papillon Löwchen Collie
Pekingese Poodle German Shepherd Dog
Pomeranian Schipperke Old English Sheepdog
Pug Shiba Inu Pembroke Welsh Corgi
Shih Tzu Tibetan Spaniel Polish Lowland Sheepdog
Silky Terrier Tibetan Terrier Puli
Toy Fox Terrier Shetland Sheepdog
Yorkshire Terrier
GRAPH: Figure 1. Success rates (%) in three tests of object permanence and one test of social cueing in 50 domestic dogs. Error bars represent standard errors.
GRAPH: Figure 2. Success rates (%) in The Invisible Displacement Test by 48 domestic dogs in five AKC breed groups (see Table 3). Sample sizes for each breed group are given in parentheses. Error bars represent standard errors.
GRAPH: Figure 3. Hypothetical data showing a strong relationship between one measure of dog personality, the total pack drive score (see Table 2a, b), and the success rate (%) on the Social Cueing Test.
References
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Gagnon, S. & Doré, F. Y. (1992). Search behavior in various breeds of adult dogs (Canis familiaris): Object permanence and olfactory cues. Journal of Comparative Psychology, 106, 58-68.
Gagnon, S. & Doré, F.Y. (1993). Search behavior of dogs (Canis familiaris) in invisible displacement problems. Animal Learning and Behavior, 21, 246-254.
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~~~~~~~~
By Ethan D. Clotfelter and Karen L. Hollis
ETHAN D. CLOTFELTER, Ph.D., is Associate Professor of Biology at Amherst College, Amherst, MA 01002.
KAREN L. HOLLIS, Ph.D., is Professor of Psychology at Mount Holyoke College, South Hadley, MA 01075.
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