Evidence for individual differences in regulatory focus in rats, Rattus norvegicus

Abstract

Regulatory focus (Higgins, 1997) builds on the classic approach-avoidance distinction by identifying two important approach orientations: the promotion focus (approaching gains and attainment) and the prevention focus (approaching non-losses and safety). Though individual differences in regulatory focus have been widely studied in human psychology, it is unknown if such differences exist in other species. To explore this possibility, we designed a series of tests for laboratory rats, paralleling human regulatory focus research on risk-taking. In homecage tests, rats (N=23) were given an opportunity to prevent a loss by burying a noxious novel object. In solitary tests in a novel enclosure, the same rats had the opportunity to pursue gains (food rewards) and/or safety (darkness). Rats demonstrated stable individual differences on both tests (p’s < .001). Complementing the human research, duration with the noxious novel object was predicted by an individual’s tendency to pursue safety (p < .01) and not by its tendency to pursue gains (p > .8). Some aspects of these results were compatible with alternative approaches, such as the bold-shy axis and “if-then” personality profiles (Mischel & Shoda, 1995). Regulatory focus theory, however, was uniquely able to predict the overall pattern, which may be an indication that it could contribute to future research in animal personality, motivation, and welfare.

Central features of any environment are its potential gains and losses. The strength of an animal’s behavioral response to a potential gain or a potential loss may vary by time (e.g. midday vs. dusk, migration vs. breeding season), context (e.g. being surrounded by kin vs. rivals), species (e.g. predator vs. prey), and individual. Stable individual differences are well documented in nonhuman animals (see Gosling, 2001) and are a useful paradigm for exploring a range of scientific questions from the spread of invasive species (Cote, Fogarty, Weinersmith, Brodin, & Sih, 2010) to the molecular mechanisms underlying the inheritance of behavior (Champagne & Curley, 2009). In this paper we present regulatory focus theory (Higgins, 1997), heretofore examined only in humans, as a new approach to understanding individual differences in gain-maximization and loss-prevention behaviors in nonhuman animals.

Regulatory focus theory arose from developmental psychology research seeking to understand the fundamental ways in which people achieve self-regulation (Higgins, 1987). The hedonic principle, the ancient maxim that people approach pleasure and avoid pain, is a powerful predictor of behavior (e.g., Kahneman & Tversky, 1979), but beyond this principle Higgins (1997) has suggested that an individual’s regulatory focus also determines behavior. Unlike the hedonic principle, which is a unitary construct (approaching pleasure at one pole and avoiding pain at the other), regulatory focus identifies two distinct methods of approach: the motivation to approach gains, i.e. the promotion focus, and the motivation to approach non-losses, i.e. the prevention focus (Higgins, 1998). More specifically, within promotion focus, advancement and better-than-neutral states are approached while non-gains are avoided; within prevention focus, safety, security, and the status quo (satisfactory current conditions) are approached or actively maintained while losses are avoided (Higgins & Spiegel, 2004). Since its introduction, the deeper implications of this framework have been explored in social psychology (e.g., Higgins, 2000; Higgins et al., 2001). Additionally, its predictive utility has been demonstrated in a range of fields including behavioral neuroscience (Cunningham, Raye, & Johnson, 2005), health psychology (Strauman, Coe, McCrudden, Vieth, & Kwapil, 2008; Uskul, Keller, & Oyserman, 2008), decision making (Appelt, Zou, Arora, & Higgins, 2009; Crowe & Higgins, 1997), and communication science (Cesario & Higgins, 2008; De Boer, 2010).

Despite its growing influence on human research, regulatory focus has yet to be studied in other animals. To discover if it could be a useful model for understanding nonhuman behavior, we tested whether regulatory focus theory would be able to explain patterns of individual differences in laboratory rats. Human research has shown that an individual has a chronic promotion strength (his/her tendency to approach gains) and a chronic prevention strength (his/her tendency to approach non-losses; Higgins et al., 2001; Higgins, Shah, & Friedman, 1997). Promotion strength and prevention strength are theoretically and empirically orthogonal constructs: people can be high on both, low on both, or high on one and low on the other (Higgins & Spiegel, 2004).

Interestingly, individual differences in promotion and prevention strength sometimes lead to counterintuitive predictions of behavioral patterns. For instance and in parallel with the current program of research, recent studies on risk taking have shown that under specific conditions, prevention-focused individuals, i.e. those people who place a high value on safety and security, can be the riskiest individuals (Scholer, Stroessner, & Higgins, 2008). On the surface, it may seem that an advancement, gain-oriented motivation (promotion motivation) and not a safety, security motivation (prevention motivation) should always be more predictive of risk taking (Crowe & Higgins, 1997). However, when individuals are in a state of loss and taking a risk is the only way to return to a satisfactory status quo, it is their prevention motivation (the motivation to approach safety) and not their promotion motivation (the motivation to approach gains) that should predict the likelihood of taking the risk. A series of independent social psychology studies have supported this reasoning, demonstrating that participants’ prevention motivation—either measured as individual differences or induced by writing about times when they were focused on preventing losses—predicted risky behavior in a stock investment scenario (Scholer, Zou, Fujita, Stroessner, & Higgins, 2010). Specifically, a strong prevention motivation predicted risk-taking when participants were already in a state of loss, i.e. had lost money relative to the start of the experiment, and when the risky investment could return them to a non-loss state, i.e. the amount of money with which they began the experiment. These results follow logically from a regulatory focus perspective: when a behavior is the only way to return to the status quo or a safe state, individuals who are particularly concerned with safety and security—i.e. prevention individuals—should be the most likely to engage in that behavior, even if it is risky.

Following a similar line of argument, we hypothesized that when a behavior will return a nonhuman animal to a satisfactory status quo or a safe state, the animal’s loss-prevention motivation will predict the likelihood of engaging in that behavior, even if it is risky or unpleasant. Additionally, as was shown in human research (Scholer et al., 2008; Scholer et al., 2010), we hypothesized that gain-maximization motivation will not predict the likelihood of engaging in that behavior. To test these hypotheses, we designed two experiments to examine individual differences in the behavior of laboratory rats, Rattus norvegicus.

In the first experiment, we gave rats the opportunity to engage in a behavior that could restore their satisfactory status quo by reducing a current loss: burying a noxious novel object (NNO). In a separate experiment, six months later, we measured their motivational strength to pursue gains (acquire food rewards) or safety (maintain darkness, which is a presumably safe state for a nocturnal animal). The behaviors in this experiment were somewhat exclusive, but because the darkness affected the entire apparatus at once, animals could feasibly engage in both goal pursuits at once. Moreover, the experiment was not designed to be a forced choice paradigm; animals could engage in alterative behaviors instead (e.g. exploration or grooming). Consequently, our experimental design afforded an additional hypothesis: that overall regulatory focus approach motivation, i.e. the motivation to pursue gains or non-losses (promotion + prevention), would predict latency to approach the NNO. Approaching the NNO should have been motivating for both orientations because before inspection whether the NNO-cage contained a gain or a preventable-loss was unknown. As such, we predicted that individuals with the highest overall regulatory focus motivation should have the fastest approach. Unlike our first two hypotheses, which are unexplored in nonhuman animals but directly parallel human research (Scholer et al., 2008; Scholer et al., 2010), this final prediction would, to our knowledge, be a unique contribution to regulatory focus research in general. Together, support for our hypotheses could indicate that regulatory focus may be a useful model for understanding animal motivation and thereby suggest new avenues of research comparing the patterns of regulatory focus found in humans with the behavior of other species.

Method

Animals and husbandry

Long-Evans rats (N = 23) used for this study were bred (descendents from rats bought from Charles River Laboratories) and housed in our facility at the Department of Psychology at Columbia University in accordance with IACUC regulations. The rats were kept in complex cage-systems consisting of 6 polycarbonate, steel-wire lidded cages (20 × 23 × 46 cm) interconnected by a series of transparent tubes (see Figure 1). Male and female rats were housed in separate cage-systems (12 males in one cage-system and 11 females in another). Animals were maintained on a reverse 12D: 12L light cycle under constant temperature (21° C) and humidity (55%) with ad lib food and water. The cage-systems were enriched with toys, darkened cages, and food treats several times a week (see Figure 1). All behavioral observations and tests took place during the dark period of the light cycle.

Figure 1.

Overhead illustration of a homecage consisting of six interconnected cages: three food-and-water-cages, two darkened-cages, and one marginal cage that contained the noxious novel object during testing (NNO-cage). The NNO-cage was connected to the cage on its left with a transparent tube, but was otherwise self-contained. (The tube at the back of the NNO-cage provided a bypass between the food-and-water-cages and the darkened-cages.)

Materials and Procedures

Homecage-tests

At approximately five months of age, the rats in each cage-system were tested twice several days apart. Just prior to the start of the test, all animals were removed from the NNO-cage, but remained in the homecage with access to the other five cages (see Figure 1) while the experimenter arranged the NNO-cage for testing. The NNOs were constructed from a store-bought metallic teabag wired to a carabiner and filled with a cotton-ball soaked in either fantastikR or bleach. Fresh bedding was added to the NNO-cage and the carabiner was secured to the lid so that the NNO rested on the floor at the front of the NNO-cage. The connecting tube, which only permitted access by one rat at a time, was then unblocked so that the rats could enter the NNO-cage to inspect or bury the NNO. Importantly, to minimize extraneous reasons to approach and remain in proximity to the object, the NNO-cage was designed to be as marginal as possible: it did not provide passage to other cages and was barren of all necessities—i.e. did not contain food, water, or dark shelter. We observed that NNOs were buried after two hours when we returned to dismantle the experimental materials, but that they were never completely buried within the experimental session, i.e. the first ten minutes of the test. As such, during the period of time analyzed in this paper, all individuals would have encountered an unburied object. All tests were video recorded.

To enter the NNO-cage, a rat passed through the transparent connecting tube, the diameter of which permitted only one animal at a time. As each animal passed through the tube, its unique back-pattern was clearly visible allowing observers to record the times when it entered and exited the NNO-cage. On non-test videos, two observers were trained to greater than 90% reliability so that the duration scores they reported for each rat were highly correlated (r = .97, p < .0001). On the test videos, the observers, who were blind to the study predictions, coded the entrances and exits for each animal for the first ten minutes after the threat-cage was unblocked. Based on these codings, the duration each animal spent within the threat-cage was calculated as a percentage of total test time and each animal was assigned an entry order ranking such that lower numbers indicated faster entries.

Solitary-tests

At approximately eleven months of age, the rats were first trained (2 days in duration) and then tested (2 days in duration) in an opaque Plexiglas enclosure (94 × 94 X 61 cm). The enclosure was located in a small room close to the colony room and was illuminated under white light with a 60 Watt overhead lamp. At the midpoint of adjacent sides of the enclosure, two small boxes (10 cm cubes) were fixed to the floor. At one of these locations, treat-location, the small box contained a mixture of high-fat and/or high-sugar familiar food treats (e.g. almonds, sugary cereal, Peanut M&M’S®, etc.), providing a gain-maximization opportunity. At the other location, dark-location, the small box was empty but when the rat was within one body-length of it, the overhead light turned off for 30 seconds or until the animal moved away from the box, whichever took longer (manually executed by the experimenter).

On each test day, a rat was removed from its homecage, placed in a corner of the enclosure (equidistant from the two small boxes) and allowed to move freely throughout the space. Behavior was video recorded from overhead for 10 minutes, at which point the rat was returned to its homecage. The number of treats eaten was then determined and the number of fecal boli produced during the 10-minute session were counted as a physiological indicator of anxiety-like response (e.g. Lund, Rovis, Chung, & Handa, 2005). The enclosure was cleaned with 70% ethanol solution prior to testing each animal. Videos were analyzed with Noldus Observer software (Noldus, Spink, & Tegelenbosch, 2001). The duration of time each animal spent within 20 cm of the treat-box (treat-location) and dark-box (dark-location) was calculated as a percent of total test time. Animals were weighed one month after testing.

Statistical Analysis

All analyses were conducted with Stata v11.1 (College Station, TX). In both test-types, stability of individual behaviors (duration, ranking, dark-location, and treat-location) were assessed with Pearson’s correlations. All other associations were investigated using a multilevel model (a.k.a. mixed, random effects, or hierarchical model). In a multilevel model, the random effects (i.e. level two effects, in this case, random intercepts) are estimated to account for the correlated errors that may arise when sampling repeatedly from a single individual or group. By allowing the intercept of the statistical model to vary by rat id, we can account for this type of repeated sampling while we test for the relationship between the outcome and the fixed effects (treat number, weight, boli, or behavior, depending on the specific relationship being examined; Gelman & Hill, 2006; Rabe-Hesketh & Skrondal, 2008). Because of its flexibility and power, multilevel modeling is becoming an increasingly popular method of statistical analysis in animal personality research (e.g., Martin & Réale, 2008; Wray, Mattila, & Seeley, 2011) and animal behavior in general (see van de Pol & Wright, 2009). Unless otherwise noted, controlling for gender did not affect the nature of the results.

Results

Homecage tests

Rats spent an average duration of 18.42% of the test time in the NNO-cage, 95% confidence interval (CI) = 11.88, 24.96 (all subsequent CI refer to 95% coverage), which was equivalent to nearly 2 minutes. Duration did not differ by object filling (bleach vs. fantasticR p z .6), but did vary by rat, r = .75, p < .001, CI = .48, .89. Individual rats were also consistent in their ranking, r = .64, p < .001, CI = .31, .83.

Solitary-test

Treat-location was positively related to the total number of treats consumed, t(21) = 7.79, p < .0001, but was not related to weight, p > .3, indicating that treat-location was a measure of gain-maximization behavior and not general food-consumption behavior. By design, dark-location was yoked to darkness time and was not related to the number of fecal boli produced, p > .3, indicating that dark-location was a measure of loss-prevention behavior and not general anxiety. On average, dark-location was 16.48% of test time and treat-location was 34.27% of test time.

Though there were no significant sex differences in dark-location, p > .3, females had significantly higher treat-location than the males, t(21) = 5.76, p < .001; CI = 23.52, 47.79, which corresponded to eating an average of 4.14 more treats per session, t(21) = 3.16, p < .005; CI = 1.57, 6.71. Though there was a negative relationship between treat-location and dark-location, t(21) = 3.18, p < .01, most animals did not divide their time exclusively between the two pursuits. Indeed, combining treat-location and dark-location, we found percentages ranging from 0.60% to 94.91% with a mean of 50.75%, standard deviation = 27.16. We used this combined score as a measure of overall regulatory focus, i.e. an individual’s overall motivation to approach gains or non-losses. Across the two test days, an individual’s behavior was stable on all three measures, treat-location: r = .78, p < .0001, CI = .54, .90; dark-location: r = .62, p = .001, CI = .28, .82; overall regulatory focus: r = .81, p < .0001, CI = .60, .92.

Overall Pattern

Using the behavior on the fourth day of solitary testing to give the rats a maximum amount of time to learn the test contingencies, we found that time in the NNO-cage was unrelated to treat-location, p > .8, but was positively predicted by dark-location, t(21) = 2.95, p = .008. Moreover, when both variables (treat- and dark-location) were included as predictors in the multilevel model, only dark-location was significantly related to time in the NNO-cage, dark-location: t(20) = 2.99, p = .007; treat-location: p > .6. Interestingly, the dark-location slope was also significantly greater than the treat-location slope, t(20) = 2.17, p = .04¹. Finally, on their own, treat-location and dark-location were unrelated to ranking, i.e. entry order on the NNO tests, p’s > .2, but higher overall regulatory focus corresponded to marginally faster rankings, t(21) = 1.99, p < .06.

To explore these patterns in more depth, we classified individuals as being “low” (bottom third) “mid” (middle third) or “high” (top third) on each of the solitary-test behaviors (treat-location, dark-location, and overall regulatory focus). The treat-location grouping did not reveal new insights; treat-location remained unrelated to both NNO behaviors. The dark-location grouping, however, revealed that high dark-location individuals drove the positive relationship with duration (high compared to low and mid, t(21) = 2.30, p = .03) and that there was no evidence of a difference between the low- and mid-individuals, p > .6. On average, an individual with high dark-location had a duration of 30.48% (CI = 17.77, 43.19) compared to 11.98 % (CI = 2.70, 21.27) for the other individuals (see Figure 2). Similarly, individuals with high overall regulatory focus drove the negative relationship with NNO ranking (high compared to low and mid, t(21) = 2.32, p = .03) and there was no evidence of a difference between the low- and mid-individuals, p > .8. On average, an individual high on overall regulatory focus entered 4^th (CI = 3.25, 6.25) compared to 8^th (CI = 6.72, 9.82) for the other individuals (see Figure 3).

Figure 2.

Mean homecage duration (expressed as a percentage of total test time) spent in the cage with the noxious novel object (NNO) displayed as a function of the solitary test behaviors (error bars represent standard error of the mean). Individuals were categorized as “low,” “mid,” or “high” on the solitary test behaviors: treat-location and dark-location. There was no evidence of an association between duration and treat-location, but there was between duration and dark-location: Individuals classified as having a high dark-location spent significantly more time with the NNO than other individuals (ns, p > .8; *p = .03).

Figure 3.

Mean rank approaching the noxious novel object (NNO) displayed as a function of solitary test overall regulatory focus classification: “low,” “mid,” or “high” (error bars represent standard error of the mean). Individuals classified as having a high overall regulatory focus approached the NNO significantly faster than other individuals (*p = .03).

Discussion

We investigated the ability of regulatory focus theory (Higgins, 1997) to account for individual differences in the behavior of a nonhuman animal, the Long-Evans rat, and sought to compare these individual differences to the patterns found in humans (Higgins & Spiegel, 2004). Specifically, we were interested to discover if two distinct approach motivations—promotion and prevention—could provide a framework for understanding how an animal engages in risky/unpleasant behaviors that may serve to restore a satisfactory status quo. In humans, the prevention (non-loss) motivation and not the promotion (gain) motivation predicts the probability of engaging in such behaviors (Scholer et al., 2008; Scholer et al., 2010). In laboratory rats, we found evidence of a similar pattern. Individual rats who demonstrated a stable tendency to pursue darkness (safety) in an open enclosure, were the same individuals who remained longest with the NNO. As in the human research (Scholer et al., 2010), a rat’s tendency to pursue gains (treat-location) was not predictive of remaining with the NNO. Though neither loss-prevention nor gain-maximization behavior predicted approach latencies on their own, their combination did, which may suggest that both facets of regulatory focus are important in determining latencies to approach unknown, novel objects. Furthermore, these tests were separated by six months, which is a substantial portion of a laboratory rat’s 2–3 year life-span (Lipman, Smith, & Bronson, 1995), possibility indicating that, like humans (Higgins, 1997), the stability of these motivational differences is strong in rats.

Human research has indicated that regulatory focus is a useful elaboration of the hedonic principle in which approaching pleasure is simply and unitarily contrasted with avoiding pain (Liberman, Molden, Idson, & Higgins, 2001; Molden & Finkel, 2010; Molden, Lucas, Gardner, Dean, & Knowles, 2009). Similarly, the present research cannot be attributed to a single approach-avoidance dimension. The rats’ gain-maximization behavior, which is ostensibly most similar to approach, was insufficient to predict approach latencies. The rats’ loss-prevention behavior, which is ostensibly most similar to avoidance, related positively to duration with a NNO and contributed to the overall regulatory focus association with approach latency. Neither of these behaviors can be construed as avoidance behaviors and are, instead, more analogous to approach behavior. As such, the present framework builds on the hedonic principle by identifying for the first time in a nonhuman animal two approach orientations: the promotion focus and the prevention focus.

Likewise, this research resonates with nonhuman animal research documenting individual differences along a bold-shy axis (e.g., Jones & Godin, 2010; Reaney & Backwell, 2007; Sinn, Gosling, & Moltschaniwskyj, 2008; Wilson, Coleman, Clark, & Biederman, 1993). The bold-shy dimension is most often defined as a propensity to take risks (Wilson, Clark, Coleman, & Dearstyne, 1994) and, importantly, has been shown to have fitness-related consequences (e.g., Bergvall, Schapers, Kjellander, & Weiss, 2011; Reaney & Backwell, 2007; Sinn, Apiolaza, & Moltschaniwskyj, 2006; Smith & Blumstein, 2010). To the extent that the behaviors in each test can be related to boldness or risk-taking—the riskiness of approaching a NNO and the riskiness of maximizing gains at the potential expense of preventing losses—the behavioral consistency within each test complements research on the stability of individual differences in bold-shy behaviors.

Comparing across tests, however, a pattern of situational inconsistency emerges. Boldness in one environment (treat-location in the solitary tests) did not correspond to boldness in another environment (duration and rankings in the NNO tests). In this way, our results support an “if-then” approach to personality (Mischel & Shoda, 1995), which describes personality as a propensity to behave consistently within certain similar contexts but not across all situations, producing an “if situation X, then behavior Y” pattern of behavior. The “if-then” paradigm has been a powerful concept in human personality research (see Mischel, 2004; Mischel, Shoda, & Mendoza-Denton, 2002) and has been applied to nonhuman animal personality research as well (Uher, Asendorpf, & Call, 2008). Beyond a descriptive “if-then” framework, however, regulatory focus lends an a priori predictive model for understanding cross-situational patterns of behavior. In other words, regulatory focus predicted the specific “if-then” patterns of behavior observed in these experiments. For highly prevention oriented individuals, the contingencies were: “if boldness/riskiness reduced loss (duration in NNO tests), then high boldness/riskiness,” but “if boldness/riskiness could not reduce loss (solitary tests, treat-location), then no boldness/riskiness.” Conversely, for highly promotion oriented individuals, the contingencies were: “if boldness/riskiness could maximize gains (solitary test, treat-location), then high boldness/riskiness,” but if “boldness/riskiness could not maximize gains (duration in NNO tests), then no boldness/riskiness.”

Thus, we relied on a motivational model to predict behavior, which, for animal research, is perhaps more common in behavioral neuroendocrinology (e.g. Baldo, Daniel, Berridge, & Kelley, 2003; Zhang, Balmadrid, & Kelley, 2003) and addiction research (e.g. Ahmed, Walker, & Koob, 2000) than it is in the animal personality literature. Early animal behavior research, particularly that of Clark Hull and E.C. Tolman, who both sought to understand the fundamental principles that guide behavior (e.g., Hull, 1931; Tolman & Gleitman, 1949), also used motivational models to understand behavior. Expanding on these various traditions, we use a motivational model to predict individual differences in rat behavior across contexts. Importantly, describing individual differences in motivation has been a generative approach within the human personality literature (e.g. Cacioppo & Petty, 1982; Dweck & Leggett, 1988; Higgins, 1997; Kruglanski et al., 2000). Though much work remains to be done, the studies presented here provide evidence in favor of its potential utility in the study of animal personality.

Among the work that remains to be done, one aspect that is particularly relevant to animal research in general is to test whether regulatory focus theory is predictive of behavior in settings less restrictive than a laboratory environment. Though we attempted to leverage ecologically relevant rat behavior—e.g. burying threats, preferring darkness, omnivorous foraging—more research will be required to determine the scope of regulatory focus applicability. To begin to explore its range, observational research of non-captive or zoo-living animals could test for patterns predicted by regulatory focus theory. Prevention-related behaviors should be highly correlated (Higgins, 1997; Higgins, 1998); they could include a propensity to spend time in safety-related locations—such as shelters, places with the least exposure and maximal distance from predators/threats, etc.—and a propensity to engage in loss-prevention activities—e.g. patrolling the territory and vigilantly checking any changes to the status quo. Promotion-related behaviors should also be highly correlated (Higgins, 1997; Higgins, 1998); they could include a propensity to spend time in gain-related locations—such as feeding sites, places that offer trade-off between a gain and a loss, etc.—and a propensity to engage in gain-maximization activities—e.g. eagerly investigating novel food sources and working harder to achieve a gain. It would be especially interesting to discover if these individual differences impact fitness, as has been found in other animal personality research (Smith & Blumstein, 2008).

The present research also raises the question of why female rats engaged in more gain-maximization behaviors than the males (treat-location in the solitary tests). Differences in promotion strength are not found between human females and males (Higgins et al., 2001; Higgins et al., 1997), so exploring the roots of this potential species-difference could be revealing. Barring the possibility that it was a statistical anomaly, research should first address whether the sex difference extends to all promotion-related behaviors or whether it is an idiosyncratic consequence of the experimental design, perhaps a confound with some other sexually dimorphic phenomenon (Beatty, 1979; Fernandes, Gonzalez, Wilson, & File, 1999; Johnston & File, 1991). If the difference is found to extend to other promotion-related behaviors, exploring the potential relationship between gender differences in regulatory focus and the sexual dimorphism of the species could be an intriguing line of research.

Finally, the present research explores only a small facet of regulatory focus theory. Much of the human literature has been devoted to understanding the types of conditions that are most engaging, i.e. the best ‘fit,’ for strongly promotion vs. prevention oriented individuals (e.g. Cesario, Grant, & Higgins, 2004; Higgins, 2000; Spiegel, Grant-Pillow, & Higgins, 2004; Uskul et al., 2008). This body of literature may serve as a potentially useful comparative framework for researchers studying the intersection between animal personality and animal welfare (Fox & Millam, 2007; Izzo, Bashaw, & Campbell, 2011; Powell & Svoke, 2008; Watters & Meehan, 2007), though much more research is required to determine the extent to which regulatory focus patterns in humans contain corollaries in rats and other species. The results presented here provide evidence that such an approach is feasible and potentially fruitful.