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• Published: 13 April 2022

Cats learn the names of their friend cats in their daily lives

• Saho Takagi 1 , 2 , 3 ,
• Atsuko Saito 4 ,
• Minori Arahori 5 , 6 ,
• Hitomi Chijiiwa 1 ,
• Hikari Koyasu 2 ,
• Miho Nagasawa 2 ,
• Takefumi Kikusui 2 ,
• Kazuo Fujita 1 &
• Hika Kuroshima 1  

Scientific Reports volume  12 , Article number:  6155 ( 2022 ) Cite this article

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• Developmental biology

An Author Correction to this article was published on 10 August 2023

This article has been updated

Humans communicate with each other through language, which enables us talk about things beyond time and space. Do non-human animals learn to associate human speech with specific objects in everyday life? We examined whether cats matched familiar cats’ names and faces (Exp.1) and human family members’ names and faces (Exp.2). Cats were presented with a photo of the familiar cat’s face on a laptop monitor after hearing the same cat’s name or another cat’s name called by the subject cat’s owner (Exp.1) or an experimenter (Exp.2). Half of the trials were in a congruent condition where the name and face matched, and half were in an incongruent (mismatch) condition. Results of Exp.1 showed that household cats paid attention to the monitor for longer in the incongruent condition, suggesting an expectancy violation effect; however, café cats did not. In Exp.2, cats living in larger human families were found to look at the monitor for increasingly longer durations in the incongruent condition. Furthermore, this tendency was stronger among cats that had lived with their human family for a longer time, although we could not rule out an effect of age. This study provides evidence that cats link a companion's name and corresponding face without explicit training.

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Introduction.

Many human words have referential meanings: they evoke a visual mental image when heard or read 1 . For example, the word “apple” causes us to imagine a red or green fruit even if no such fruit is present. This language property, which expands the plasticity of communication, is also seen to some extent in non-human animals, mainly in the context of intraspecific vocal communication. Seyfarth, Cheney and Marler reported that vervet monkeys (now called Chlorocebus pygerythrus ) responded differently to different types of alarm calls 2 (although some of the calls overlap acoustically 3 and this view is currently debated 4 ). More recently, west African green monkeys ( Chlorocebus sabaeus ) rapidly learned the novel referent of an alarm call that was given in response to a drone 5 . Referential signaling is not limited to primates. Suzuki showed that tits ( Parus minor ) detected snake-like motion more rapidly when a snake-specific alarm call rather than a general alarm call was played back, suggesting that tits recall things to which at least one specific call refers 6 . Such studies show that animals have specific calls with a referential meaning, increasing the likelihood of responses appropriate for survival.

In contrast to studies dealing with life-or-death-related issues and ecology, some studies have reported that companion animals understand human utterances in more neutral situations and use them in communication with us [e.g., dogs ( Canis lupus familiaris ) 7 , 8 , 9 , 10 , 11 , 12 ]. Dogs in particular have been studied in this context; for example, a few “expert” dogs trained in object-name fetching over several months remembered hundreds of object names and fetched the correct object upon verbal command 7 , 8 , 12 . According to a recent report, “gifted” dogs learned object names after few exposures during social interactions, whereas the majority of dogs did not show such object-name association learning despite intensive training 12 .

Similar to dogs, cats ( Felis catus ) are one of the most widespread companion animals in the world 13 . Although the ancestral Libyan wildcat ( Felis lybica ) is a solitary species 14 , many domestic cats live with humans and show evidence of social cognitive operations concerning humans. They can use human pointing cues 15 and gaze cues 16 to find food. They also discriminate between human facial expressions 17 , 18 , 19 and attentional states 20 , 21 , 22 , and identify their owner’s voice 23 . Furthermore, they cross-modally match their owner's voice and face 24 when tested with their owner’s photo presented on a screen, and human emotional sounds and expressions 19 .

Cats have been shown to distinguish their own from another familiar cat’s name in a habituation–dishabituation procedure 25 , and they also distinguished those names from general nouns. Interestingly, cats living in multi-cat households habituated less to their companion cats’ names than to other nouns. Conceivably, therefore, cats might also recognize the name of another cat living in the same household.

Here we examined whether cats linked a human utterance and the corresponding object, using a relatively simple task that is applicable to many species: a visual-auditory expectancy violation task previously used to test cats’ ability to predict an object when hearing that objects’ name 24 . As stimuli we used the names of other cats (“models”) cohabiting with the subjects in Exp.1, and human family members’ names in Exp.2. Cats were presented with the face of the other cat (Exp.1) or human (Exp.2) following presentation of the model’s name, called by the owner (Exp.1) or an experimenter (Exp.2). Half of the trials were “congruent,” i.e., the model’s face and name matched, whereas the other half were “incongruent” (the stimuli mismatched). Previous research showed that cats matched human photos and voices 24 , which established the validity of presenting photos as stimuli. Our hypothesis was that cats learned face–name relationships by observing interactions involving their owner, and that more such observations would lead to stronger learning. We tested two groups of cats, differing in the number of other cats they lived with: cats belonging to cat cafés where many cats live together, and household cats. The latter probably have more opportunities to observe interactions between the owner and each of the other cohabitating cats, which might facilitate learning of the face–name relationship. Therefore, we analyzed data from household cats and cat café cats separately in Exp.1. In Exp.2, analysis concerned the number of cohabiting family members because more members would have more opportunities to hear other members’ names (e.g., people living as a couple probably say each other’s name less often than people living in a larger family). In Exp.2 we considered length of time living with the family as well as the number of family members.

We made two predictions. First, attention toward the stimulus face displayed on the monitor should be longer in incongruent trials due to expectancy violation. Second, the amount of violation is related to the amount of exposure to relevant interactions; specifically, household cats should show stronger violation effects than café cats in Exp.1, and cats living in households with more people should show more evidence of expectancy violation in Exp.2.

Experiment 1

Materials and methods.

We tested 48 cats (28 males and 19 females). Twenty-nine (17 males and 12 females, mean age 3.59 years, SD 2.71 years) lived in five “cat cafés” (mean number living together: 14.2, SD 10.01), where visitors can freely interact with the cats. The other 19 (11 males and 8 females, mean age 8.16 years, SD 5.16 years) were household cats (mean number living together: 6.37, SD 4.27). We tested household cats living with at least two other cats because the experiment required two cats as models. The model cats were quasi-randomly chosen from the cats living with the subject, on condition of a minimum period of 6 months cohabiting, and having different coat colors so that their faces might be more easily identified. We did not ask the owner to make any changes to water or feeding schedules.

For each subject, visual stimuli consisted of two photos of two cats other than the subject who lived together, and auditory stimuli consisting of the voice of the owner calling the cats’ names. We asked the owner to call each cat’s name as s/he would usually do, and recorded the call using a handheld digital audio recorder (SONY ICD-UX560F, Japan) in WAV format. The sampling rate was 44,100 Hz and the sampling resolution was 16-bit. The call lasted about 1 s, depending on the length of cat’s name (mean duration 1.04 s, SD 0.02). All sound files were adjusted to the same volume with the help of version 2.3.0 of Audacity(R) recording and editing software 26 . We took a digital, frontal face, neutral expression, color photo of each cat against a plain background (resolution range: x = 185 to 1039, y = 195 to 871) which was expanded or shrunk to fit the monitor size (12.3″ PixelSense™ built-in display).

We tested cats individually in a familiar room. The cat was softly restrained by Experimenter 1, 30 cm in front of the laptop computer (SurfacePro6, Microsoft) which controlled the auditory and visual stimuli. Each cat was tested in one session consisting of two phases. First, in the name phase the model cat’s name was played back from the laptop’s built-in speaker four times, each separated by a 2.5-s inter-stimulus interval. During this phase, the monitor remained black. Immediately after the name phase, the face phase began, in which a cat's face appeared on the monitor for 7 s. The face photos were ca. 16.5 × 16 cm on the monitor. Experimenter 1 gently restrained the cat, looking down at its head; she never looked at the monitor, and so was unaware of the test condition. When the cat was calm and oriented toward the monitor, Experimenter 1 started the name phase by pressing a key on the computer. She restrained the cat until the end of the name phase, and then released it. Some cats remained stationary, whereas others moved around and explored the photograph presented on the monitor. The trial ended after the 7-s face phase.

We conducted two congruent and two incongruent trials for each subject (Fig.  1 ), in pseudo-random order, with the restriction that the same vocalization was not repeated on consecutive trials. The inter-trial interval was at least 3 min. The subject’s behaviors were recorded on three cameras (two Gopros (HERO black 7) and SONY FDR-X3000): one beside the monitor for a lateral view, one in front of the cat to measure time looking at the monitor, and one recording the entire trial from behind.

Diagram illustrating each condition in Exp.1. Two model cats were chosen from cats living with subject. The model cat’s name called by owner was played through the speaker built into the laptop computer (Name phase). Immediately after playback, a cat’s face appeared on the monitor (Face phase). On half of the trials the name and face matched (congruent condition), on the other half they mismatched (incongruent condition).

One cat completed only the first trial before escaping from the room and climbing out of reach. For the face phase we measured time attending to the monitor, defined as visual orientation toward or sniffing the monitor. Trials in which the subject paid no attention to the monitor in the face phase were excluded from the analyses. In total, 34 congruent trials and 33 incongruent trials for café cats, and 26 congruent trials and 27 incongruent trials for house cats were analyzed (69 trials excluded overall). A coder who was blind to the conditions counted the number of frames (30 frames/sec.) in which the cat attended to the monitor. To check inter-observer reliability, an assistant who was blind to the conditions coded a randomly chosen 20% of the videos. The correlation between the two coders was high and positive (Pearson’s r   \(=\)  0.88, n   \(=\)  24, p  < 0.001).

We used R version 3.5.1 for all statistical analyses 27 . Time attending to the monitor was analyzed by a linear mixed model (LMM) using a lmer function in a lme4 package version 1.1.10 28 . We log-transformed attention time to get close to normal distribution. Congruency (congruent/ incongruent), environment (cat café/house), and the interaction were entered as fixed factors, and subject identity was a random factor. We ran F tests using an Anova function in a car package 29 to test whether effects of each factor were significant. To test for differences between conditions, an emmeans function in an emmeans package 30 was used, testing differences of least squares means. Degrees of freedom were adjusted by the Kenward–Roger procedure.

In addition to attention to the monitor, we calculated the Violation Index (VI), which indicates how much longer cats attended in the incongruent condition than the congruent condition. VI was calculated by subtracting the mean congruent value from the mean incongruent value for each subject. Greater VI values indicate longer looking in incongruent conditions. Note that we used data only from subjects with at least one congruent—incongruent pair. Thus, if a subject had one congruent/incongruent data point, we used that value for analysis instead of calculating the mean. Data from 14 household cats and 16 café cats were analyzed. We ran a linear model (LM) using a lmer function in a lme4 package version 1.1.10 28 . Living environment (café/house) was entered as a fixed factor. To examine whether VI was greater than 0, we also conduct a one-sample t-test for each group.

Results and discussion

Figure  2 shows time attending to the monitor for each group. House cats attended for longer in the incongruent than the congruent condition, as predicted; however, café cats did not show this difference.

Time attending to the monitor during the face phase for each group in Exp.1. Red bar represents congruent condition; Blue bar represents incongruent condition. Left panel shows café cat data, right panel shows house cat data. The y-axis is log-transformed.

LMM revealed a significant main effect of living environment ( \({\rm X}\) 2 (1) = 16.544, p  < 0.001), and a congruency x living environment interaction ( \({\rm X}\) 2 (1) = 6.743, p  = 0.009). The differences of least squares means test confirmed a significant difference between congruent and incongruent conditions in house cat ( t (86) = 2.027, p  = 0.045), but not café cats ( t (97.4) = 1.604, p  = 0.110).

Figure  3 shows the difference in VI between groups. House cats had a significantly greater VI than café cats ( F (1,28) = 6.334, p  = 0.017). A one-sample t-test revealed that house cats’ VI was greater than 0 ( t (13) = 2.522, p  = 0.025) whereas that of café cats was not ( t (15) = 1.309, p  = 0.210).

Violation Index for each group in Exp.1. Red boxplot (left) shows café cat data; blue boxplot (right) shows house cat data.

These results indicate that only household cats anticipated a specific cat face upon hearing the cat’s name, suggesting that they matched the stimulus cat’s name and the specific individual. Cats probably learn such name-face relationships by observing third-party interactions; a role for direct receipt of rewards or punishments seems highly unlikely. The ability to learn others’ names would involve a form of social learning. New behaviors or other knowledge can also be acquired by observing other cats 31 . Recent study has reported that cats learn new behaviors from humans 32 . However, we could not identify the mechanism of learning. It is still an open question how cats learn the other cats’ names and faces.

Environmental differences between house cats and café cats include how often they observe other cats being called and reacting to calls. Contrary to human infants who are able to disambiguate the referent of a new word among many potential ones 33 , cats might not do that at least in this study. Saito et al. showed that café cats did not distinguish their own name from the name of cohabiting cats whereas household cats did so, in a habituation–dishabituation procedure 25 . We extend this finding by showing that café cats also do not appear to learn the association between another cat’s name and its face.

We also asked whether the ability to recall another cat’s face upon hearing its name was limited to conspecifics. How about human family members? In Exp.2 we used household cats and re-ran the same experiment using a family member’s name and face.

A limitation of Exp.1 was that we could not analyze the effect of the duration of cohabiting with the model cat because this differed across cats, and in some cases the information was lacking (i.e., it was hard to track the exact length of time subject and model cats lived together, as the owner quarantined cats that didn't get along with others.). We predicted that the longer the cat and human had lived together, the stronger the association between name and face would be, due to more opportunities to learn it.

Experiment 2

The procedure in Exp.2 was almost the same as in Exp.1, but we used human instead of cat stimuli. In view of likely differential exposure to name–face relationships depending on the number of people living together (for example, someone living with a single other person calls that person’s names less often than someone living with multiple others), we took this factor, along with length of time living together, into account in the analysis.

We tested 26 household cats (15 males and 11 females, mean age 5.2 years, SD 3.27 years) living in houses with more than two people. Thirteen cats lived with two-person families, seven with three-person families, four with four-person families, and two with five-person families. Durations of living together ranged between 6 and 180 months ( mean 49.79 months, SD 41.50). We did not ask the owner to change water or feeding schedules.

The experimental stimuli were the same as Exp.1 except that we used human names and faces instead of cat names and faces, and unfamiliar voices instead of owners’ voices (mean duration 0.80 s, SD 0.30) (i.e., the person calling the name was never the person whose face was presented). As in Exp. 1, we used habitually used names instead of real names to ensure that the cats had the opportunity to learn on a daily basis (e.g., “mother”). All sound files were adjusted to the same volume with Audacity(R). One experimenter took the photos, face-forward and smiling, with a plain background (resolution range x = 304 to 4608, y = 340 to 3512) which were adjusted to the monitor size. If the model could not be present on the day of the experiment the owner sent a family photo by e-mail in advance. In households of more than two people, the models were decided randomly.

The procedure was the same as in Exp.1.

We conducted almost the same statistical analysis as in Exp. 1. One cat was tested only on the first trial because she escaped from the room. In total, 32 congruent and 27 incongruent trials were analyzed, after excluding 42 “no attention” trials. We measured duration of attending to the monitor as in Exp.1 and analyzed the data by a linear mixed model (LMM) using a lmer function in a lme4 package version 1.1.10 28 . We log-transformed the attention data to better approximate a normal distribution. We log-transformed the duration of living together to reduce variance. Congruency (congruent/incongruent), number of family members (2–5), duration of living together and interactions were entered as fixed factors, with subject identity as random factor. To clarify the effect of duration of living together, we assigned cats to two groups: those living with their humans for above-median durations were the “Long” group, and those with below-median durations were the “Short” group.

In addition to attention, we analyzed VI. Because we used data from subjects with at least one congruent—incongruent pair, this concerned 16 subjects. We ran a linear model (LM) using a lmer function in a lme4 package version 1.1.10 28 with the number of family members (2–5) and duration of living together entered as fixed factors.

Figure  4 shows time spent attending to the monitor according to the number of family members. The more the number of family members increased, the longer cats attended to the monitor in the incongruent compared to the congruent condition. LMM revealed significant interactions of congruency × number of family members ( \({\rm X}^{2}\) (1) = 3.885, p  = 0.048) and congruency × number of family member × duration of living together ( \({\rm X}^{2}\) (1) = 3.920, p  = 0.047). There was no significant main effect of congruency ( \({\rm X}^{2}\) (1) = 0.066, p  = 0.797). Figure  5 shows attention to the monitor for each group divided by length of time living together, to illustrate the 3-way interaction. The Long group strengthened the tendency (the more family members, the greater attention in the incongruent condition), whereas the short group weakened the tendency, with fewer differences between congruent and incongruent conditions.

Time attending to the monitor during the face phase in Exp.2. Red points represent congruent condition; blue points represents incongruent condition. Each line represents a regression line predicted by the LMM. Each ribbon represents the 95% confidence interval. The y-axis is log-transformed.

Time attending to the monitor during the face phase grouped by time living together in Exp.2. We separated time living together into short and long groups by median for convenience because we found a significant interaction of time together, number of family members and congruency. Left panel represents short group; right panel represents long group. Red points represent congruent condition; blue points represent incongruent condition. Each line represents a regression line predicted by the LMM. Each ribbon represents the 95% confidence interval. The y-axis is log-transformed.

Figure  6 shows the relation between VI and the number of family members. With increasing family size, the VI scores were higher. LM revealed a significant main effect of number of family members ( F (1,12) = 6.522, p  = 0.025). However, there was no significant interaction between number of family members and duration of living together.

The relationship between Violation Index and number of family members. Grey area indicates the 95% confidence interval predicted by the LM.

These results suggested that not all cats predict a familiar human’s face upon hearing that name; we found no main effect of congruency. However, the interaction among congruency, number of family members and time living together indicated that attending to the monitor was affected by time spent together and family environment: the bigger the family, the more cats attended in the incongruent condition and the less they attended in the congruent condition; this was especially so for the cats that had lived longest with their human family. Our interpretation is that cats living with more people have more opportunities to hear names being used than cats living with fewer people, and that living with a family for a longer time increases this experience. In other words, the frequency and number of exposure to the stimuli may make the name–face association more likely.

Ethical statement

This study adhered to the ethical guidelines of Kyoto University and Azabu University, and was approved by the Animal Experiments Committee of the Graduate School of Letters, Kyoto University and the Ethics Committee of Azabu University, which follows “Guidelines for Proper Conduct of Animal Experiments” by the Science Council of Japan (2006). Informed consent was obtained from all owners.

General discussion

This study examined whether domestic cats learn that human utterances indicate specific objects in their daily life. In Exp.1, cats were presented with the face of another cat from the same household after hearing playback of either a matching or a mismatching name. Results revealed that house cats, but not café cats, attended to the monitor for longer in a name-face incongruent condition than the congruent condition. Upon hearing a cats’ name, the subjects expected the corresponding face. In Exp.2, we used human stimuli to examine whether cats also expect the face of human family members upon hearing their names. Results showed that, although not all cats attended for longer duration in the incongruent condition, the number of household members affected their responses: with more family members, cats attended for longer to the monitor in the incongruent condition. Furthermore, cats that had lived with their family for longer showed the longest durations of attention when the name–face relationship was incongruent. These results might suggest that cats might learn names from observing interactions between humans: a third-party perspective. However, it was not procedurally possible to identify the mechanism of learning in this study. Further study should clarify how cats learned the association. In summary, house cats matched at least their companion cats’ names and faces, and possibly their human family members’ names. This is the first evidence that domestic cats link human utterances and their social referents through every day experiences.

Acquisition of new signal-meaning pairs requires a high level of social cognition, such as knowing to whom others are talking and paying attention 34 . Many recent reports on social cognition in cats (see review 35 ), have shown their high sensitivity to human attentional states 20 , 21 , 22 . These results of the present study suggest that cats might understand who is talking to whom in everyday situations, which is consistent with those studies. However, it is still unclear how cats learned the name-face association. Further study should address this point.

In Exp.1, we found a difference between household cats and café cats. Previous studies have reported several behavioral differences between these two groups 24 , 25 , 36 . In Saito et al. house cats but not café cats were shown to recognize their own name; café cats did not discriminate their own name from names of other cats living in the same environment. Whereas house cats probably learn by observing the reaction of the specific cat whose name was called, café cats are more likely to hear different names called by different guests, making such learning more difficult. Additionally, the number of cats living together might have an influence, as more cats probably means fewer opportunities to learn specific cat name-identity relationships. In our experiment, 75% of café cats tested lived in cafés holding over 30 cats (Supplementary Table S1 ). In fact, recent research has shown that people with larger social networks show poorer voice recognition 37 . To untangle any effects of number of cats living together and fewer opportunities to observe interactions of each cat, cats from cafés of different sizes could be tested.

In this study we did not take into account the nature of the social relationships between cats. Model cats were randomly chosen among subjects’ cohabitants, without regard to the quality or their relationship with the subject. It could be useful for further studies to examine this factor as well as the influences of experience, environment, and familiarity of model cats on cats’ learning of human utterances.

We used familiar voices as auditory stimuli in Exp.1 and unfamiliar voices in Exp.2. Cats responded more in incongruent condition in Exp.1 but less clearly so in Exp.2. Perhaps cats will generally show clearer expectancy violation effects when names are called by familiar voices. Some previous studies have shown cat-human communication effects specific to the owner, with little generalization of social cognitive abilities to a stranger 18 , 38 . Galvan and Vonk reported that cats differentiate between happy and aggressive expressions of their owner but not a stranger 18 . Although Saito et al. reported that cats recognized their own name even when called by a stranger 25 , this was not the case for a family member’s name, possibly due to weaker association in the latter situation. To more closely examine whether cats understand common phonetic characteristics in human utterances beyond the owner’s voice, future studies should use strangers’ voices as stimuli.

We found that cats recognize at least one companion cat’s name and possibly a human family member’s name. It might be asked what motive cats have for remembering names. One possible explanation has to do with competition. For example, a cat might receive food when the owner calls her name but not when she calls another cat’s name. The fact that humans are probably not in competition with cats might explain the weaker association between human names and faces.

In Experiment 2 we found that cats' looking behavior changed with the length of time living with a human family. However, this length was highly correlated with cat age (Pearson's r = 0.89). Because cognitive abilities develop with age, the relationship we observed may reflect an age effect. We were unable to isolate these factors in this study; this should be done in future research.

Previous studies of companion animals’ understanding of human speech have focused on “exceptional” subjects with intensive training and subsequent excellent performance in remembering names of many objects (in dogs 7 , 8 ). By contrast, recent work revealed that “normal” dogs did not perform as impressively as “exceptional” dogs 39 . However, those studies did not clarify whether subjects had a visual image of the referent after hearing a name. Our study demonstrated that cats expect a specific face upon hearing the specific name of a companion. We conducted no training, but exploited cats’ spontaneous learning of relationships between names and faces in their everyday experiences, similar to what human children do. Further study should test whether cats are sensitive to the symbolic nature of some human utterances.

We did not control or measure affective aspects of hearing the other cat's or human’s name. Further studies along these lines should consider using emotionally neutral items and their names, removing possible affect-related influences, to shed further light on cats’ ability to link names and objects of more neutral valence.

In conclusion, house cats linked at least two conspecific housemates’ human-given “names”. They might have a cross-modally integrated concept of another cat’s name and face, similar to humans. This study differs from well-known field studies 2 in that the stimulus utterance was not related to any urgent, potential life or death situation. A remaining question is how cats learn names. Language learning is known to be affected by prosodic aspects. Infant-directed speech characterized by prosodic exaggeration and lexical and syntactic simplification facilitates word learning in infants 40 . An fMRI study revealed that dog brains dissociated lexical and emotional prosodic information in human spoken words, similar to humans 9 , which might facilitate language learning. Prosodic factors might affect cats in the same way, which would be interesting for how they learn about the referential nature of human utterances. Another question concerns the evolution of this ability. Some researchers have proposed that (self-) domestication was important in human language evolution 34 , 41 . Future research could address this issue by working with African wildcats, or other domesticated animals such as dogs and horses.

Data availability

We attached data on e-letter which contains all data we used this study.

Change history

10 august 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-023-40125-5

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Acknowledgements

The authors acknowledge with thanks all owners and cats who volunteered in this study. The authors also wish to thank James R. Anderson for editing the article.

This study was financially supported by Grants-in-aid for Scientific Research (KAKENHI) No. 17J08974, No. 19J01485 to S. Takagi, No. JP16J1034 to M. Arahori, No. JP16J08691 to H. Chijiiwa, No. 25118003 to A. Saito and Nos. 25240020, 26119514, 16H01505, 15K12047, 25118002, and 16H06301 to K. Fujita from the Japan Society for the Promotion of Science (JSPS).

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Saho Takagi, Hitomi Chijiiwa, Kazuo Fujita & Hika Kuroshima

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Saho Takagi

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Atsuko Saito

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S.T. participated in the design of the study, collected and analyzed the data, and drafted the manuscript as corresponding author. M.A, H.C. and A.S. collected data. A.S., K.F., M.N. T.K., and H.K. gave S.T. suggestions about experiment design, data analysis, discussions and helped draft the manuscript. All authors critically revised the report, commented on drafts of the manuscript, and approved the final report.

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Takagi, S., Saito, A., Arahori, M. et al. Cats learn the names of their friend cats in their daily lives. Sci Rep 12 , 6155 (2022). https://doi.org/10.1038/s41598-022-10261-5

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Research Article

The ‘Feline Five’: An exploration of personality in pet cats ( Felis catus )

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Centre for Social Change, School of Psychology, Social Work & Social Policy, University of South Australia, Adelaide, South Australia, Australia

Roles Data curation, Formal analysis, Investigation, Validation, Writing – original draft

Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Discovery Circle, School of Natural and Built Environments, University of South Australia, Adelaide, South Australia, Australia

Roles Data curation, Formal analysis, Validation, Writing – review & editing

Affiliation School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia

Roles Data curation, Funding acquisition, Investigation, Project administration, Resources, Writing – review & editing

Affiliation Centre for Biodiversity and Restoration Ecology, Victoria University of Wellington, Wellington, New Zealand

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

• Carla A. Litchfield, 
• Gillian Quinton, 
• Hayley Tindle, 
• Belinda Chiera, 
• K. Heidy Kikillus, 
• Philip Roetman

• Published: August 23, 2017
• https://doi.org/10.1371/journal.pone.0183455
• Reader Comments

The idea of animals possessing personalities was once dismissed by the scientific community, but has since gained traction with evidence for potential application to improve captive animal management and welfare. Although domestic cats are popular companion animals, research has tended to overlook the value of personality assessment for management and care of pet cats. The aim of this study was to investigate personality in a large sample of pet cats with a view to understanding practical implications for pet cats in the home. Personality of 2,802 pet cats, from South Australia and New Zealand, was rated by their owners utilising a survey measuring 52 personality traits. Five reliable personality factors were found using principal axis factor analysis: Neuroticism, Extraversion, Dominance, Impulsiveness and Agreeableness. Implications for the ‘Feline Five’ are discussed in relation to their potential application to improving the management and welfare of pet cats. Highly Impulsive cats for example, may be reacting to something stressful in their environment, whereas cats with low Agreeableness scores, showing irritability may indicate underlying pain or illness. Thus, the need for a systematic and holistic approach to personality that includes both the individual pet cat and its environment is recommended, and opens the door to future interdisciplinary intervention.

Citation: Litchfield CA, Quinton G, Tindle H, Chiera B, Kikillus KH, Roetman P (2017) The ‘Feline Five’: An exploration of personality in pet cats ( Felis catus ). PLoS ONE 12(8): e0183455. https://doi.org/10.1371/journal.pone.0183455

Editor: Christopher A. Lepczyk, Auburn University, UNITED STATES

Received: August 3, 2016; Accepted: August 4, 2017; Published: August 23, 2017

Copyright: © 2017 Litchfield et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The Domestic Cat Personality data are available from the University of South Australia database and has a DOI (DOI: 10.4226/78/58b65fc48a5f0 , link: https://doi.org/10.4226/78/58b65fc48a5f0 ).

Funding: The project (PR) received funding from the South Australian Dog and Cat Management Board ( http://www.dogandcatboard.com.au/ ). The project (PR) was also funded by the University of South Australia’s Discovery Circle Initiative ( http://www.discoverycircle.org.au ) which receives external funding from the Adelaide and Mt Lofty Ranges Natural Resources Management Board; Department of Environment, Water and Natural Resources; City of Marion; and City of Salisbury.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Domestic cats have been companion animals for thousands of years [ 1 ], and are popular pets in Australia, New Zealand, Europe, North America and elsewhere [ 2 ]. Yet, we know little about typical pet cat behaviour, with most behavioural studies conducted in laboratories, shelters or on free-ranging feral cat colonies [ 3 ]. This gap in knowledge is problematic since the typical environment for domestic cats is arguably the home, with tens of millions of pet cats, some kept exclusively indoors [ 3 ]. Cat owners, veterinarians, animal behaviourists and scientists often focus on the behavioural problems of stressed cats rather than on the behaviour of psychologically healthy cats and their inter- and intra-species interactions. Development of an accurate standardised ethogram (inventory of species-specific behaviours) for pet cats would facilitate creation of standards for optimal housing and welfare, like the Five Freedoms for captive animals [ 4 ].

It is possible for cats living in a multi-cat household to time-share favourite places [ 5 ], choose to spend time in close proximity with another cat [ 6 ], or play with a dog companion, with both species able to interpret each other’s behaviour correctly if they have been together from a young age [ 7 ]. Cats are more sociable than many people realise [ 8 ], and a basic understanding of cat behaviour [ 9 ] and signals (e.g. vocalisations) can allow owners to assess social stress in their cats [ 8 ], with veterinarians and other professionals able to provide this information to kitten or cat owners [ 10 ]. Understanding social interactions between cats (in the same household or neighbourhood) and between cats and their owners is important since many urban pet cats may be suffering chronic stress, as a result of lack of control over their environment [ 8 ]. A better understanding of cat personality by means of assessment could help owners improve conditions for their cats at home, thereby supporting the optimal wellbeing of their feline companions.

Personality in animals

Personality refers to consistent individual differences in behavioural patterns [ 11 ] and is sometimes labelled as temperament [ 12 ] or behavioural syndromes [ 13 ], although a standardised term, personality, should be applicable in all cases. Personality in animals has been investigated by scientists in various fields [ 14 ], with a bias towards species considered most useful to humans, such as primates for their genetic closeness [ 15 ] and canids for their working ability [ 16 ]. However, as popular pets, an understanding of domestic cat personality could improve domestic cat welfare, by allowing carers to tailor management strategies to suit individual cats, since animal personality has been shown to influence behaviour [ 17 ], health outcomes [ 18 ], wellbeing [ 19 ], and welfare [ 20 ]. In Australia, research has tended to focus on behavioural problems related to owned and un-owned/stray cats [ 21 , 22 ] rather than on the potential value of personality assessment. In Australia, almost 53,000 cats were received by RSPCA shelters in 2014–2015, with about a third of these cats eventually euthanized [ 23 ], and in the United States, an estimated 3.4 million cats enter animal shelters annually, with about 41% of these euthanized [ 24 ]. Personality assessment may increase compatibility of cat-owner placements through shelter adoption [ 2 ], with the understanding that personality of owners also influences cat behaviour [ 25 ] and therefore ideally both personality of prospective owner and cat would be assessed for compatibility [ 26 ]. For example, people scoring high on Neuroticism may have fewer and less complex interactions with their cats [ 25 ]. Cat owners through use of informed management practices, such as appropriate housing, enrichment, grouping, health and wellbeing strategies related to individual cat personalities may enhance the quality of life of their cats.

Measures of cat personality

Early cat personality studies relied on systematic observation of cat behaviour and coding methods, which involves generating an ethogram, and then systematically recording frequency or duration of behavioural categories [ 16 , 27 , 28 ], such as a cat’s responses to presentations of novel objects [ 29 , 30 ] or unfamiliar persons [ 2 , 31 , 32 ]. Though coding was considered to be objective [ 14 ], the subjective rating of comprehensive personality traits by people (usually carers) who know the animals well (the rating method), is now used more frequently [ 8 , 25 ], and is considered a more reliable, practical and time-efficient approach [ 33 ]. Following the generation of a comprehensive list of species-relevant behavioural traits [ 34 ], rating usually occurs along a Likert scale to indicate the level of trait expression generally demonstrated by the animals [ 16 ]. Data are then typically reduced into a consolidated number of personality dimensions or factors, each comprising reflective personality traits using dimension reduction statistics, such as principal components analysis [ 28 , 35 ].

Research on personality in domestic cats

Investigations of cat personality have focused on either: the continuum of one personality dimension, such as dominance-warmth [ 36 ], which may allow for a more thorough investigation; or more commonly on multiple dimensions at once [ 37 , 38 ], typically adapting the commonly used approach in human personality research, the Five-Factor Model (FFM), sometimes known as the Big Five [ 37 , 39 ]. The FFM model is comprised of the dimensions Neuroticism, Extraversion, Openness to Experience, Conscientiousness and Agreeableness. The theory behind the model suggests an individual’s personality is determined by where they exist along each factor continuum [ 34 ] (see Fig 1 adapted from [ 40 ]).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0183455.g001

Domestic cat personality research has tended to explore development of behaviour directed at humans, socialisation, and the shy-bold continuum of cat personality in relation to humans [ 30 – 32 , 35 , 41 ]. Individual differences exist in the extent to which cats accept the approach of, or handling by an unfamiliar person [ 32 , 35 ], which is potentially influenced by the friendliness of a cat’s sire as well as socialisation effects [ 30 , 41 ]. The shy-bold continuum in relation to inter-cat social behaviour has also been investigated [ 42 ], with bolder cats more vulnerable to feline immunodeficiency virus, which has potential health implications for pet cats in the home.

Methodological challenges facing studies of cat personality

A multidimensional examination of personality in cats [ 43 ] served as a foundational study design, which used subjective trait ratings in a personality survey designed for rhesus monkeys ( Macaca mulatta ), which was modified following behavioural observation of cats in a laboratory over three months. Despite the limited generalizability of the sample ( n = 14, female only, and laboratory environment), inter-cat social behaviour was observed and recorded, allowing successful validation of initial subjective assessments. Some of the methodological limitations or weaknesses of domestic cat personality studies conducted to date include: (1) lack of information on length of familiarity between cats and the people rating them [ 41 ]; (2) lack of acknowledgement of possible influence that researchers may have on cat behaviour when observing them in their homes [ 25 ]; (3) small sample sizes, such as 14 cats [ 43 ] or 40 cats [ 25 , 41 ], with 440 cats representing the largest sample rated by their owners following recruitment over the Internet [ 38 ]; and (4) insufficient reporting of reliability assessments with inter-rater/observer reliability coefficients the most commonly reported values, conveying varying levels of agreement between raters/observers in their impressions of cat personality trait expression [ 25 , 37 , 41 , 43 ].

Another problem concerns the inconsistent nomenclature or labelling used for cat personality trait and factors. The dimension reduction technique principal components analysis (PCA), coupled with exploratory factor analysis (EFA), is recommended to analyse statistical correlations among personality traits that cluster together creating overall components [ 34 ]. For example, the highest factor loadings on traits such as anxious, insecure, tense, and so on, have resulted in labelling the factor as Neuroticism [ 37 ]. The labelling of a factor as Feeding in one study [ 25 ], with its behavioural inclination makes its applicability to personality questionable [ 16 ]. Moreover, a lack of standardised personality factor labels and their corresponding traits is obstructing replication studies and consequently progress in cat personality research [ 38 ]. Thus, a standardised use of terms guided by previous research [ 44 ], such as the well-established human FFM nomenclature is recommended.

Following identification of personality factors, researchers should assess consistency of these factors across situations [ 45 ], with evaluation of validity to determine whether the assessment tool actually measures personality [ 46 ]. For example, the two-step process [ 25 , 43 ] allows subjective ratings to be converged with coded behaviours, in order to identify and validate personality factors with some respectable coefficients (e.g. > .70), thereby strengthening accuracy of findings. Content validity was likely compromised in one application of human personality traits to cats [ 38 ] since not all domains of cat personality may have been captured in their use of only the human FFM.

Application of personality assessment for improved cat management and welfare

Previous research on captive wild felids has led to suggestions of potential application of personality assessment to captive animal management and welfare, such as socially compatible enclosure groupings created for Scottish wildcats ( Felis silvestris ) [ 19 ] and provision of ample hiding places for highly tense-fearful cheetahs ( Acinonyx jubatus ) [ 20 ]. These applications may also be relevant to domestic pet cat management, particularly for grouping of compatible cats in multi-cat households, or providing neurotic pet cats with additional hiding places. To facilitate the use of personality assessment as a means of improving pet cat management and welfare, the methodological challenges facing research in this area, including small sample sizes, must be addressed.

Research aims

As an exploratory study, this study aimed to analyse personality in a large sample of pet cats, utilising a personality inventory completed by cat owners about their cats. The study followed on from previous research on felid personality, which has typically found between three [ 37 ] and five factors [ 38 ], with the personality dimensions Sociable, Dominant and Curious emerging with high validity across felid species [ 46 ]. The research question that this study sought to answer was: how many reliable and interpretable factors depict personality in pet cats and what traits do they represent?

Materials and methods

Ethics statement and study subjects.

The study was conducted in New Zealand and South Australia. The methods and materials used in this study were approved by the University of South Australia’s Human Research Ethics Committee (#33220) and the Victoria University of Wellington Human Research Ethics Committee (#21064) with all cat owners consenting to participate in the study. All issues related to participant recruitment and consent were approved by Human Research Ethics Committees. Data were collected through an online survey instrument. Participants voluntarily self-selected to complete the survey and they were provided with information regarding the project and consent at the beginning of the online survey. It was a requirement for participants to confirm that they consented to being involved in the project and that they met inclusion criteria (being at least 16 years old and currently living in South Australia, for the Australian survey; being at least 18 years old and currently living in New Zealand, for the New Zealand survey) before being able to undertake the survey. As an example, a copy of the South Australian participant information for the online survey is provided ( S1 Appendix ).

The Human Research Ethics Committee approved 16 years and older as a participation criterion for the South Australian research as 16 year olds were able to register cats in some local council areas in South Australia and it was considered important to understand their attitudes to cat management. The Human Research Ethics Committee agreed that a 16 year old could reasonably decide to participate in the social survey and answer the questions.

Subjects included 2,802 domestic cats of varying breeds from private homes in South Australia (SA; n = 1,687) and New Zealand (NZ; n = 1,115). This included 1,377 male, 1,387 female and 38 cats of unknown sex, ranging in age from 1–20 years, with a median age of 5 years. Cats under one year of age were excluded and only surveys where respondents had answered all personality items needed for analyses were included.

Design, measures and materials

The current study utilised a 52-item (variable/trait) survey based on a recent comprehensive Scottish wildcat personality survey [ 19 ], which pooled items from previous personality assessments on felids [ 20 , 43 , 47 ] and chimpanzees ( Pan troglodytes ) [ 48 ]. Minor changes to wording were made in our survey to minimise confusion and increase applicability to pet cats. ‘Friendly to conspecifics’, ‘aggressive to conspecifics’ and ‘stingy’ were replaced with ‘friendly to other cats’, ‘aggressive to other cats’ and ‘greedy’, respectively. The survey included specific definitions alongside each item, such as insecure (‘seems scared easily, jumpy and fearful in general’) to ensure a uniform understanding of the terms among participants, who were asked to rate the extent to which their cat demonstrated each personality item/trait along a seven-point Likert scale ranging from ‘not at all’ to ‘very much so’ ( S1 Appendix ).

This personality survey was part of a larger online survey (using SurveyMonkey [ 49 ]) created for a citizen science project called Cat Tracker, a collaborative initiative to further the understanding of domestic cats and community views on their ownership and management. Cat Tracker also tracked pet cats utilising small Global Positioning System (GPS) tracking units. The results of the larger study will be used to inform cat management. Only the cat personality component of the online survey is provided here for analysis. The other parts of the survey are not included, nor are details about the tracking of cat movement, as they are relevant to other parts of the larger study. For reference, the larger survey included sections related to cat ownership, attachment to cats, participant demographics (such as age and sex), general opinions on cats, and cat stories (provided by the participant). To be involved in the larger survey, individuals could be either cat owners or non-cat owners (although many questions including the cat personality questions, the focus of this study, were only asked of cat owners). Participants were recruited through an open invitation to participate in the project, which was promoted by project leaders in SA and NZ through partner organisations (including academic, government and animal-focused organisations), social, and traditional media. Although the project was promoted in multiple ways (including e-newsletters to organisational mailing lists), no participants were recruited directly. The media release that was used in SA is provided as an example of promotional material ( S2 Appendix ). In addition, the project’s popularity saw it promoted by other individuals, companies, organisations, government departments, and media outlets in various formats and locations (including those outside the study areas). The study areas of SA and NZ were selected for convenience and due to funding conditions. Project funders were based in SA and NZ, which saw research limited to these locations. The survey was open in SA from February 2015 until September 2016 (a total of 20 months), whilst in NZ data collection began at the end of 2014 and continued until the end of 2015 (a total of 12 months). Although it was suggested that participants completed the survey online, it was also available in paper format and was posted (reply paid) to potential participants upon request. Any completed paper-based surveys received by the research team were transcribed into the online survey instrument.

Survey data from both SA and NZ ( n = 4,842) were downloaded from SurveyMonkey [ 49 ] and initial data screening was conducted in Microsoft Excel. Many survey respondents had not answered all of the cat personality items ( n = 1,780) as they did not have a cat, had a cat but decided not to complete the (optional) cat personality section, or had neglected to answer some of the items. Data from those respondents were removed. The survey responses were also checked for unengaged participants (no variation in their responses to the 52 cat personality items as indicated by standard deviations of < .50), which resulted in two being excluded from analysis. The remaining sample ( n = 3,060) was analysed in IBM SPSS Statistics V21.0. A filter was applied to conservatively exclude cats less than one year old ( n = 258), as personalities may still be developing prior to a minimum age of four months [ 35 ]. The relationship between the length of the personality survey and the number of missed responses to survey items was investigated using Pearson’s correlation coefficient. The last item ‘eccentric’ was not included as it was found to be an outlier, with 132 missed responses.

Initial exploratory factor analysis (EFA) utilising principal axis factoring was conducted on two independent samples of personality ratings in pet cats from SA ( n = 1,687) and NZ ( n = 1,115). Initial results ( Table 1 ) show the orthogonally (varimax) rotated four-factor solutions, initial and rotated eigenvalues, and percentages of variance revealing moderate-high internal consistencies, and highly similar factor extractions between both data sets. For SA, the items that clustered on factor 1 represent Neuroticism; factor 2, Dominance; factor 3, Extraversion; factor 4, Impulsiveness, and cumulatively explain an initial variance of 41.58%. A similar factor structure was revealed for NZ, cumulatively explaining an initial variance of 41.35%, but the items on factor 3 represent Self-control and factor 4, Extraversion; however, factors 3 and 4 for NZ were presented adjacently to factors 3 and 4 for SA to enable meaningful comparison ( Table 1 ). The high similarity between factor structures cross-validated the results [ 50 ], thus justifying further analysis using a combined sample ( n = 2,802).

https://doi.org/10.1371/journal.pone.0183455.t001

Preliminary analyses involved examination of the Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity to determine if the data were appropriate for factor analysis ( S3 Appendix ). The overall KMO measures of .92 and .91 respectively, confirmed sampling adequacy for both analyses with values exceeding the minimum level of .50 [ 51 ], and Bartlett’s Tests of Sphericity [ 52 ] were significant ( p < .001), thus the data were considered appropriately correlated for factor analysis. The KMO values for individual items were over .72 ( S3 Appendix ), which is greater than the accepted level of .50 [ 50 ].

Initial analyses were run to obtain eigenvalues for each factor in the data. Ten factors had eigenvalues greater than Kaiser’s criterion of one that cumulatively explained a variance of 56.95% for SA and 57.22% for NZ. For SA and NZ as independent samples, the scree plots display the eigenvalues associated with the components in descending order against the number of the components revealed inflexion points that justified retaining four factors for each data set ( S4 Appendix ; [ 53 ]), whereas parallel analysis [ 54 ] justified retaining up to seven factors for SA and eight for NZ ( S4 Appendix ).

The factorability of the 52 cat personality survey items was also examined using several well-known criteria ( S3 Appendix ), and based on these preliminary analyses the 52 cat personality survey items were subjected to EFA. Exploratory factor analysis was conducted ( S5 Appendix ). Principal components analysis (PCA) was initially used to determine the potential number of factors within the data set, and as a result of this process four items (independent, individualistic, eccentric and vocal) were excluded ( S5 Appendix ). Finally, a principal axis factor analysis (PAF) was conducted on the final 48-item sample. The justifications used for each step of the preliminary and final analysis processes are outlined in detail in this section, the results section, or are provided in S3 Appendix , S4 Appendix , and S5 Appendix .

Cat owner participants

The 2,802 cats (study subjects) were owned by 2,291 survey participants, members of the general public of SA and NZ. Participants were male ( n = 308), female ( n = 1,850) or did not answer ( n = 133). About 60% of participants were aged between 21 and 50 years, with approximately 16% of participants on either side of this age range ( Table 2 ).

https://doi.org/10.1371/journal.pone.0183455.t002

The Pearson’s correlation coefficient showed there was a strong correlation between the number of missed survey items and the length of the cat personality survey using the combined SA and NZ data, r = .73, n = 51, p < .01. A final PAF was conducted on the 48-item cat personality dataset with five factors cumulatively explaining a variance of 47.43%. Orthogonal (varimax) and oblique (direct oblimin) rotations were explored and resulted in similar factor extractions, as minimal correlations (< .30) were present between factors ranging from .01 to .28 ( S5 Appendix ). Consistent with previous research [ 19 , 37 ], other recommendations [ 55 – 57 ] and for ease of interpretation and understanding [ 58 ], the orthogonally rotated (varimax) solutions are presented, cumulatively explaining a variance of 41.53%. All items had salient loadings over .30 and some cross-loadings, but following previous studies [ 19 , 37 , 59 ], items with multiple salient loadings were assigned to the factor that had the highest corresponding loading. We refer to this final five-factor solution as the Feline Five. The factor-loading matrix after rotation, the initial and rotated eigenvalues, and percentages of variance explained by the factors determining their importance are presented below ( Table 3 ).

https://doi.org/10.1371/journal.pone.0183455.t003

In order to choose suitable labels for each of the five personality factors, a comparison of labels used in other animal and particularly felid studies was undertaken ( S6 Appendix ). Thus, factor 1 represents Neuroticism, factor 2 represents Extraversion, factor 3 represents Dominance, factor 4 represents Impulsiveness, and factor 5 represents Agreeableness. The initial eigenvalues showed that the factors explained a variance of 18.24%, 12.03%, 6.97%, 6.51% and 3.67% respectively, which levels out on rotation ( Table 3 ). The internal consistencies of each of the five factors was assessed using Cronbach’s alpha to determine their reliabilities and resulted in a high coefficient of .90 for Neuroticism and acceptable/moderate [ 60 ] coefficients of .80 for Extraversion and Dominance, and .72, and .78 for Impulsiveness and Agreeableness, respectively. No considerable increases in alpha levels could have been accomplished by further item removal.

In summary, following the removal of four of the 52 cat personality survey items, the final analysis yielded five distinct factors that depict personality in pet cats from the combined SA and NZ sample and determined the factors were moderately to highly consistent.

Factor scores

Weighted sum factor scores were created in Microsoft Excel. The salient item loadings in each factor were multiplied by the Likert scale score for each corresponding survey item. The resulting values were then summed together to create factor scores for individual cats on all five factors [ 61 ]. These results were then provided to cat owners, who had opted to receive the results, through individual reports that illustrated their cats’ score on each of the five factors, and how this compared to other cats using a low, typical, and high format. The report also contained a brief description of what the personality results may mean in terms of application to cat management, particularly if their cat had extreme scores on any of the personality factors.

This study sought to determine the number of reliable and interpretable factors that depict personality in pet cats and analyse what traits the factors represent. The results suggest that there are five factors of domestic cat personality and that these represent traits related to Neuroticism, Dominance, Impulsiveness, Agreeableness, and Extraversion. This study is the first of its kind to utlilise a transnational sample of this magnitude ( n = 2,802) and build on previous research to fill in a number of methodological gaps. The cross-validation technique analysing data from two independent samples (SA and NZ) strengthened the research providing a reliable and valid measure of pet cat personality upon which future animal personality research can build. Management and welfare implications for pet cats in the home have been largely neglected, and how this research can be used within this field is discussed subsequently. However, there are some limitations of the study which can be addressed in future research. Survey length may affect rater accuracy and engagement if they are too long [ 62 , 63 ]. We found that the final item ‘eccentric’ was missed often enough to become an outlier. However, this was the only item that had an additional open-ended response option (to explain any unusual behaviour in their cat), which may have confused raters or as the final question in a list of 52 items, the survey may have been too burdensome to complete. This item is still worth including, as unusual or idiosyncratic aberrant behaviour may be an indicator of stress, but may be better placed elsewhere in the measure. The elimination of four items during statistical data reduction has the potential to address the limitation of length. Next, the majority of survey respondents were female, which is consistent with some previous research finding females typically complete surveys more often than males [ 64 ], and some argue that adult women are generally ‘predestined’ to be the main human companion in human-cat dyads [ 65 ]. Future research could aim to have a more representative sample by setting target sex/gender ratios aligned with population data. Finally, not asking raters how long they had known the cats prior to rating their personalities may also be a limitation to the validity of the measure. However, this issue is most likely to be prevalent for animals in zoos or in shelter environments, where keepers or staff may rotate frequently, or spend little time with individual animals, rather than with pets who have the opportunity to behave more naturally in their environment [ 46 ].

Domestic pet cat personality structure: The feline five

Pet cat personality ratings from SA and NZ revealed five factors with acceptable-high internal consistency: Neuroticism, Extraversion, Dominance, Impulsiveness and Agreeableness. Based on the clusters of traits, previous research and the FFM nomenclature, the labelling of four out of the five cat personality factors was clear, namely: (1) Neuroticism- reflects strongest levels of traits, such as insecure, anxious, fearful of people, suspicious and shy; (2) Dominance- reflects bullying, dominant and aggressive to other cats; (3) Impulsiveness- reflects impulsive, erratic and reckless; and (4) Agreeableness- reflects affectionate, friendly to people and gentle. However, our fifth factor Extraversion also revealed traits normally associated with Self-control in Scottish wildcats [ 19 ] including decisive, aimless, persevering and quitting. Our decision to label the factor Extraversion ( S6 Appendix ) was based on the fact that traits reflecting Extraversion, including: active, vigilant, curious, inquisitive, inventive, and smart all loaded onto this factor and have also been reported for other pet cats [ 25 ], captive wild felids [ 20 , 47 , 66 ] and orangutans ( Pongo pygmaeus and Pongo abelii ) [ 59 ].

Our Feline Five factors of Extraversion, Neuroticism and Agreeableness appear generally consistent with the Big Five human personality assessments, along with the addition of Dominance in nonhuman animal personality assessments [ 67 ]. The Feline Five introduces a more comprehensive overall structure of pet domestic cat personality based on the largest sample size to date. The similarities between our factor structures and those reported in other studies of animal and domestic cat personality ( S6 Appendix ), suggest that some factor labels could be standardised, enabling comparison and discussion of their practical implications. Individual cats will exist somewhere along a continuum between low and high scores for each of the five factors. Since cross-species similarities in personality exist between pet cats and captive wild felids [ 19 , 20 ] or other mammals [ 59 ], and personality has played an important role in the health and wellbeing outcomes of captive animals, personality is likely to have similar implications for pet cats. Being able to accurately assess personality of pet cats, and consider possible suggestions for improvements in pet cat management, may help owners manage individual cats in a way that optimizes their welfare.

Applications of feline five personality scores to management of pet cats

The personality profiles of their cats may not only be interesting to cat owners, but may be used to improve welfare, particularly when an individual cat has unusually high or low scores on a factor. Awareness of results being considered extreme on a scale, compared to a sample of 2000+ pet cats, allows the owner to seek advice and consider changes to the environment or management of their cat. Personality profiling may be particularly useful for managing multi-cat households, ideally before obtaining a new cat. The following information provides specific examples.

Cats that score high on Neuroticism (shy) may be stressed and benefit from an assessment of social stress [ 8 ] by observing any interactions between the neurotic cat and others (human or non-human animals). These cats may benefit from additional hiding places around the home or access to quiet areas (like cheetahs with high tense-fearful scores [ 20 ]). All cats that are allowed to roam outside are at greater risk of disease transmission [ 42 ] or injury (road deaths or fights). Cats with low scores for Neuroticism (i.e., they are bold) may travel further (if not confined), compounding this risk.

Cats with high scores for Extraversion (smart, curious, inventive) may need additional stimulation and more complex environmental enrichment to avoid boredom [ 9 ], such as extra room to play, additional sensory items or toys, and social interactions with humans and/or other animals [ 3 ]. Low scores for Extraversion (clumsiness, aimlessness) may indicate age-related health issues, such as cognitive dysfunction [ 68 ], or other health problems, thus requiring further individual assessment from a veterinarian.

High scores on Impulsiveness (erratic, reckless) may also indicate a stressful environment [ 8 , 69 ], with negative effects on a cat’s health and welfare [ 70 , 71 ], and owners may need to seek advice from an animal behaviourist to locate the source of stress. Low scores for Impulsiveness are likely to be indicative of cats that are well adjusted to their environment and enjoy routine.

High scores for Agreeableness (friendly) are likely to represent cats that are well adjusted and ‘happy’, potentially serving as a source of enrichment for other cats (see captive elephants ( Loxodonta africana ) [ 72 ] and tigers ( Panthera tigris tigris ) [ 73 ]). Owners typically desire friendly cats [ 30 ] for their therapeutic benefits [ 69 ], and as a result friendly cats are more likely to be adopted from shelters [ 2 ]. Low scores for Agreeableness (irritable/aggressive towards people) may reflect poor socialisation [ 30 ], frustration [ 71 ], or underlying pain or illness [ 74 ].

Further research is needed to understand how extreme scores for the Feline Five factor of Dominance may be used for improving cat welfare. Most of our knowledge about sociality of domestic cats comes from behavioural observations of free-living (feral) colonies, where related adult females cooperate in raising kittens, while there is competition between adult males [ 8 ]. High scores for Dominance reflect a cat that is likely to bully other cats in the household, potentially causing stress, aggression or injury [ 75 ], with object (food) and social (inter-cat) dominance behaviours observed in situations with forced grouping of cats [ 29 ]. This particular personality factor may have a biological basis as oxytocin (a neuropeptide) has been associated with Roughness (consisting of traits irritable, dominant, forceful & moody) in cats [ 76 ].

In summary, our Feline Five factors were: Neuroticism, Extraversion, Dominance, Impulsiveness and Agreeableness. Accurate assessment of pet cat personality may help owners manage their cats in a way that optimises cat welfare. Additionally, a greater understanding of cat personality may help owners notice changes in their cat and seek professional assessment by a veterinarian and/or animal behaviour specialist [ 8 , 17 , 77 ].

Supporting information

S1 appendix. relevant survey information..

https://doi.org/10.1371/journal.pone.0183455.s001

S2 Appendix. Media release for SA cat tracker project.

https://doi.org/10.1371/journal.pone.0183455.s002

S3 Appendix. Preliminary analyses.

https://doi.org/10.1371/journal.pone.0183455.s003

S4 Appendix. South Australia and New Zealand as independent samples.

https://doi.org/10.1371/journal.pone.0183455.s004

S5 Appendix. Exploratory factor analysis.

https://doi.org/10.1371/journal.pone.0183455.s005

S6 Appendix. Comparison of factor labels used in other animal and particularly felid studies compared with the current study.

https://doi.org/10.1371/journal.pone.0183455.s006

Acknowledgments

We are grateful to the cat owners for their participation in this study. The Cat Tracker project commenced in North Carolina, led by staff from the North Carolina Museum of Natural Sciences and the North Carolina State University, where it was primarily focussed on the tracking of pet cats ( http://cats.yourwildlife.org/ ). We duplicated the cat tracking project in South Australia and New Zealand and added the social and cat personality research. We thank Karina Burgess for help with the early survey design. Supporting organisations in South Australia were: Dog and Cat Management Board; University of South Australia; City of Marion; City of Salisbury; City of Mitcham; Adelaide and Mount Lofty Ranges Natural Resources Management Board; and the Department of Environment, Water and Natural Resources. Supporting organisations in New Zealand were: Wellington City Council, Victoria University of Wellington; and World Wildlife Fund. We are grateful to the Academic Editor and two anonymous reviewers for their time, thoroughness and useful comments.

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A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section " Companion Animals ".

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Dear Colleagues,

The number of cats sharing space and experiences with people has been increasing in the last decades, playing an important role in human life. However, compared to other species such as the dog, the study of domestic cats has not yet been well developed.

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MINI REVIEW article

The mechanics of social interactions between cats and their owners.

• 1 Institute for Applied Ethology and Animal Psychology, I.E.A.P./I.E.T., Horgen, Switzerland
• 2 Vetsuisse Faculty, University of Zurich, Zurich, Switzerland

This is a mini review that summarizes what is known from quantitative observational studies of social interactions between domestic cats and humans in both laboratory colonies and the home setting. Only results from data that have been statistically analyzed are included; hypotheses still to be tested will be declared as such. In some cases, the observational data have been combined with independently collected subjective assessments by the owners of the animals' character and owner personality traits to help interpret the data. Further some relevant experimental studies are also included. All social interactions between cats and humans that are discussed below assume that the animals were socialized to people as kittens, the first topic of this review. Such socialized cats show what might be called “friendliness to humans,” which in turn affects human attachment to the cat. The visual and acoustic behavioral elements used to communicate and interact with other cats can be perceived by people and are also employed by the cats when interacting with them. The initiation, and the initiator of social interactions between cats and humans have been shown to influence both the duration of the interaction bout and total interaction time in the relationship. Compliance with the interactional “wishes” of the partner is positively correlated between the cats and the humans over all human-cat dyads examined. Cats do not spontaneously prefer one gender or age cohort of people, but the humans in those cohorts behave differently to the cats causing the latter to react differentially. The dyadic interaction structure has also been shown to differ between women and men and between older and younger adults. Nevertheless, cats—merely their presence but of course their behavior—can affect human moods and human mood differences have been shown to affect the behavior of the cats. Finally, differences have been found between interactions with purebred and non-purebred cats and between younger and older cats.

Socialization and Other Factors Affecting Establishment of a New Relationship

Eileen Karsh was the first researcher to experimentally determine the sensitive phase of kittens for socialization to humans and this was supported by further data from cat colonies in Zurich and Cambridge ( 1 – 3 ). Kittens handled frequently by humans during their second to mid-seventh week of age become friendly and trusting of people and remain so throughout their later lives [tested to at least 3 years of age, ( 4 )]. The duration and frequency of handling and number of handlers required for this effect have also been examined ( 5 ). Much behavior toward conspecifics is still to be learned. Schaer ( 6 ) suggested that conspecific “socialization” occurs by about 10 weeks and Hediger ( 2 ) confirmed experimentally that socialization to conspecifics and to humans can occur simultaneously. Therefore, most experts recommend not placing kittens before 10 or 12 weeks of age ( 7 ) 1 .

Although original socialization status to people is of paramount importance for future cat-human relationships, other parameters have also been shown to influence the establishment of a new relationship [summarized in a model by ( 1 , 8 )]: genes of the father ( 9 ); presence and behavior of the mother ( 10 ); curiosity (exploratory behavior, see below); stroking the cat; and the act of feeding the animal ( 11 ). The model by Turner predicts differential outcomes of later positive and negative experiences with people depending on the quality of original socialization to humans. For a cat well-socialized to humans as a kitten it takes many negative experiences with other people to become wary of such contacts and very few positive experiences with a new owner to become friendly and trusting of that person. A cat poorly socialized to people as a kitten requires a great deal of positive experience to accept a new person, but very little negative experience with a person to confirm its wariness and fear of people. Most shelter employees will inform that a poorly socialized and/or mishandled cat requires a great deal of patience and understanding by the new owner after being rehomed, while a well-socialized individual will take only 1–2 weeks to adapt to the new owner and home. This has enormous welfare implications for the cats involved in that poorly socialized cats take up limited space in the shelter for longer while waiting for the personnel to find such a patient new owner, and well-socialized cats can be rehomed more easily and quickly.

Friendliness to Humans

Turner et al. ( 9 ) reported a father effect on the behavioral patterns of kittens associated with what one might call “friendliness to humans.” Since cat males have nothing to do with raising their kittens, this effect had to be genetic. At the time the authors cautioned that they were not talking about a “gene for friendliness” and later, McCune ( 3 ) proposed that the genetic father effect was on “boldness” of his kittens, which in turn, increased or decreased their exploratory behavior and the chances of their contact with new humans, appearing as friendliness or, if lower, shyness.

Turner and Stammbach-Geering ( 12 ) asked women living at home to subjectively assess their cats and relationships to them along 31 traits, once for their current cat and once for the “ideal” cat and relationship. The effects of civil status, housing condition (indoor or with outdoor access), and number of cats kept on the trait ratings were also examined.

Significant positive correlations were found between the ratings of “cat affection to the owner” and “owner affection for the cat.” The former was positively correlated with ratings for “predictability,” “proximity to the owner,” “enjoyment of physical contact,” “cleanliness” and “likeness to humans.” The keepers of cats with outdoor access rated their animals as being less curious than those of indoor cats. The authors hypothesized that cats kept exclusively indoors were compensating for their less animate environment by initiating more contacts with objects inside than the outdoor cats did. However, it is important to remember that correlational results are not necessarily causal, and still need to be tested experimentally. Turner's ( 13 ) observational data on human contact initiation by indoor cats do however support the hypothesized interpretation.

Communication Between Cats and With Humans

Cat-cat visual, olfactory and auditory communication have been fairly well deciphered beginning with Leyhausen's ( 14 ) original work on the body and facial signals used [expanded by ( 15 – 17 )]. Cats often use some of the same visual and vocal signals when interacting with people. When they approach another familiar cat and greet their keepers after a short absence, they raise their tails upright, presumably as a sign of friendly intentions. Only domesticated cats use this signal and it has been suggested that there was selective pressure for such a signal in the dense temple colonies of ancient Egypt ( 18 ). To get our attention, they flank-rub on our legs (which might also mark us) and head-rub—forehead to forehead—with cats they know well, presumably marking each other (and us) with a scent ( 1 , 19 ). Bernstein and Friedmann [( 20 ), also citing ( 21 )] reported that cats preferred certain places on their bodies, particularly the head region, for being stroked, modified their postures to promote access to those preferred regions, and even led their keepers to preferred places in the home for petting episodes. Ellis et al. ( 22 ) determined that both handler familiarity and body region stroked significantly influenced negative behavioral responses. Bernstein and Friedmann (op cit.) also mentioned the cat's closing of the eyes in this relaxed situation (sometimes called the “slow blink”). This slow-blink has received more attention recently and when previously unfamiliar persons initiate such blinking, cats tend to approach them more often ( 23 ).

Auditory communication by cats has been and continues to be examined [reviewed by ( 17 )], most recently by Schötz et al. ( 24 ) using phonetic analyses of cat-to-human vocalizations. It is generally known that cats vocalize more frequently with their human companions than with other cats ( 1 ). Yeon et al. ( 25 ) found that meows are attention-seeking vocalizations in interspecific situations and higher pitched (subjectively more pleasant) than in feral cats and wild ancestors. They also modify their purrs when actively soliciting food (more urgent and less pleasant than when just resting as perceived by the human raters) and people are capable of distinguishing these ( 26 ), both behaviors probably learned over time in interactions. Ellis et al. ( 27 ) reported that 40% of their human participants identified the correct contexts of cat vocalizations more often than by chance when the vocalizations belonged to their own cat, but did not perform above chance when the calls belonged to an unfamiliar cat. Interestingly, Saito et al. ( 28 ) demonstrated with the habituation-dishabituation method that privately owned cats can discriminate their own names from other words, which leads now to other studies in the area of social cognition in cats.

Recent work on social cognition in cats also has relevance to cat-human communication. Vitale Shreve and Udell ( 29 ) provided a first review of what was known and still to be discovered and a number of studies have since been published. Pongracz and his colleagues in Hungary have been particularly active this this area. Even though Miklosi et al. ( 30 ) had already shown differences between dogs and cats in their ability to use human pointing gestures, especially that cats lacked some components of attention-getting behavior compared with dogs, Pongracz et al. ( 31 ) demonstrated that cats were indeed able to read and follow human gaze for referential information. Galvan and Vonk ( 32 ) found that cats were only modestly sensitive to emotions as indicated by human postural and vocal cues, but particularly when displayed by their owner. Quaranta et al. ( 33 ) demonstrated experimentally that cats are indeed capable to cross-modally match pictures of emotional faces with their related vocalizations in both conspecifics and humans, especially for high intensity emotions. These authors concluded that cats have a general mental representation for the emotions of their social partners, both conspecific and human.

The Initiation of Social Interactions and Goal Meshing

As mentioned above, the results from Turner and Stammbach-Geering ( 12 ) prompted a more detailed investigation of social contact initiation by household cats and their humans. Turner's ( 13 ) team observed the mechanics of social interactions in 158 cat-owning households over three consecutive days, recording which partner, the cat or the person, tried to initiate the interaction (precisely defined), the reaction of the partner (accepting or declining), the duration of each interaction as well as total interaction time observed in that cat-human relationship. The goal of the project was to determine a potential measure of relationship success or quality. Firstly, Turner looked at the proportion of “intents” to interact that were successful - separately for the cat and the person (in this study, the woman of the household) - and attempted to correlate these values with total interaction time in the relationship over all cat-human dyads observed. There was no significant correlation for the cat data, but a significant negative one for the humans. The more successful the person was in initiating interactions, the shorter the total interaction time with the cat. This means that it is the cat that determines how long the interaction lasts. The next measure combined the data for the cats and humans into one number, namely, the proportion of all successful attempts to interact that were due to the cat. Over all person-cat pairs, this measure was indeed positively correlated with total interaction time in a relationship. That is the higher the proportion of all successful intents to interact that were due to the cat, the more time spent overall interacting in the relationship.

In Mertens' ( 34 ) observational study in other households, she found that the human partner was generally more active than the cat in distance regulation, especially in reducing distance between the two, but that single bouts of staying close to each other were longer when initiated by the cat. Further, Mertens reported a higher degree of reciprocity in distance regulation in cat-human dyads with adults than in those with children and juveniles, indicating a better “meshing” of close contact. “Goal meshing,” i.e., whether the goals of each partner are aligned with the ongoing goals of the other, is one important quality of any relationship ( 35 ).

Turner ( 13 ) continued the analysis of his data and calculated the proportion of “start interactions” (a defined and recorded element) due to the cat whenever the person had shown an intent to interact (also precisely defined), i.e., the individual cat's willingness to comply with the woman's “wish” to interact. Operationally, the “wish” to interact was defined for both the human and the cat as an approach to the partner and/or a directed vocalization. Also for each pair, whenever the cat had shown an intent to interact, he calculated the proportion of “starts” due to the woman, or, the woman's willingness to comply with the cat's “wish” to interact. These two values over all observed human-cat pairs were positively and significantly correlated. In other words, if the woman complies with the cat's wishes to interact, then the cat complies with the woman's wishes at other times; if the woman doesn't comply with the cat's wishes, then neither does the cat, with the woman's wishes. Therefore, a symmetry exists in the relationships at all levels of compliance, high to low, which might explain the popularity of cats, but also differences in the level of interactivity between relationships. In some relationships there is a high level of interactivity, in others, low, and the cat apparently accepts this, as indicated by staying on as the household pet (even when allowed outside) and lowering its own rate of initiation of interactions, when the owner shows less interactivity.

Wedl et al. ( 36 ) used a relatively new tool to analyze the structure of human-cat interactions observed in the home setting, namely Theme® (Noldus bv, The Netherlands). Strings of video recorded owner and cat behaviors were analyzed during four visits to each of 40 cat-owning households. The Theme® algorithm detects sets of events which follow each other non-randomly in the temporal sequence. Two actions that occur repeatedly and regularly in alternation form a basic “t-pattern.” Hierarchically structured t-patterns emerge via the detection of relationships of these previously detected patterns by repeated use of the algorithm scanning the strings of behaviors. Wedl and her co-workers found that owner and cat personality and gender and cat age of the partners (see below) had significant effects on t-patterning of dyadic behavior. In dyads with a female owner, the number of patterns per minute tended to be higher than in dyads with a male owner. Further, cat sex did not have any effect on the temporal patterning of dyadic behavior. These results are consistent with results found by Mertens [( 34 ), see above] and Turner ( 1 ).

Differences Related to Human Gender and Age

Mertens and Turner ( 37 ) reported differences found between the behavior of men, women, boys and girls in an experimental study of their colony cats. When the human volunteers were not allowed to interact in any way with the cats they were meeting for the first time in an encounter room (they had to look at an age-appropriate book during the first 5 min), the cats entering the room showed no preference for gender or age of the partner in their approach behavior. However, during the following 5 min when the persons were allowed to interact as they pleased with the cats and the authors recorded the human's behavior, the cats reacted to differences in behavior between men, women and children. Men tended to remain seated while women and girls moved down onto the floor, to the level of the cats. Children, especially the boys, tried to approach the cats immediately to which the cats usually reacted negatively by fleeing from them, even though they were all well-socialized. Women and girls spoke to the cats more often and the cats vocalized more often with them than with the men or boys.

These results were supported by later observations by Mertens ( 34 ) during 504 h in 51 cat-owning households with 162 persons and 72 cats. When at home, women spoke and interacted more with the cats than men did. Children were especially active with respect to motor activity, while adults spoke more often to the cats. She also found that interactions with women had a higher reciprocity and therefore probably both the person and the cat enjoyed high-quality relationships. In a more recent study, Wedl et al. ( 36 ) found that female owners entertained a more structured interaction with their cats than male owners and that extraverted owners have relatively varied interaction patterns with their animals. From a PCA analysis of answers to a questionnaire by Hungarian cat owners, Pongracz and Szapu ( 38 ) reported that women considered their cats to be more communicative and empathetic than men did and that emotional matching of the cat was more commonly reported by elderly owners than young owners.

Turner ( 39 ) compared the interactions of younger adults and elderly persons (65+) with their cats and found no difference in total interaction time between the two groups, but two differences in the structure of those interactions: Younger adults interacted significantly more often with their cats, but when older people interacted, they did so for significantly longer (Presumably the elderly waited until the cat came to them to interact, but this was not tested.). The younger owners also interacted more often from a distance and spoke more often to the cat than the elderly did.

All of the above findings have allowed recommendations to psychotherapists and pedagogues working with cats to help people in texts ( 40 , 41 ) and courses in animal-assisted intervention, as well as to the general public to promote harmonious cat-human relationships.

The Effects of Cats on Human Moods

Rieger and Turner ( 42 ) and Turner and Rieger ( 43 ) discovered that not only the mere presence of a cat in the household, but also interactions with the cat reduce measureable negative moods in the person, e.g., anxiety, depression, and introversion. The depressive owner initiates fewer interactions with the cat, but when the cat approaches that person, s/he accepts the intent of the cat to interact, which affects the human's mood. The cat also changes its behavior in response to depressiveness of the human when close to the person (but not at a distance), vocalizing more frequently with the person and head- and flank-rubbing more often on that person. More mood subscales in women than in men are affected by the cat, and they are more strongly affected than in men. Turner et al. ( 44 ) concluded that only the partner, but not the cat, enhances positive moods, while the cats alleviate negative moods. This effect was comparable to the effect of a human partner.

Effects of Cat Breed and Age on Cat Behavior and Cat-human Interactions

Surprisingly, given the large number of popular cat breed books, there have been relatively few research studies of breed differences in behavior or behavior toward people. Turner ( 8 , 39 ) reported on the only ethological study that compared the two oldest purebreds, Persian and Siamese cats, with non-pedigree cats and combined observational data with subjective trait ratings by the owners. He found few differences between the two breeds - reportedly at the extreme ends of cat personality - presumably due to convergent human selection, but those expected from the popular literature: The Persians were less active and less vocal than the Siamese, while the latter were more playful but demanding of their owners. Relative to the non-purebred cats, the purebreds were often closer to their owners and friendlier to strangers, which might be related to differences in handling (pampering) during upbringing or to artificial selection (genetic differences).

Hart and Hart ( 45 ) interviewed some 80 US-veterinarians in feline practices considered to be unbiased authorities on breed differences in cats. They ranked a random selection of five breeds and domestic short- and long-haired cats out of 15 cat breeds along 12 behavioral traits. Three traits had high predictive value to distinguish the breeds, seven traits with moderate and two traits with low predictive value. However, Turner ( 46 ) stated that confirmation of these subjective rankings is still needed from comparative ethological observations. The same criticism can be made of two more recent, but otherwise promising studies for future work, namely by Wilhelmy et al. ( 47 ) and Salonen et al. ( 48 ). Using a well-known questionnaire to generate standardized behavioral profiles, the former study found behavioral characteristics in purebred cats associated with breed, coat color and coat pattern. The latter study also gathered a large data set from a health and behavior questionnaire completed by owners and determined behavioral differences between 19 breeds and breed groups along 10 different behavior traits. A moderate level of heritability in three breeds for seven traits was found but the authors reported that substantial genetic variation exists within breed populations.

There are even fewer studies of the effect of cat age on cat-human interactions. Wedl et al. ( 36 ) employed the Theme® algorithm to their observational data and determined that the older the cat, the lower the dyadic event type complexity, meaning that the strings of cat behavior in interaction with their owners are shorter in old cats than young ones. This probably reflects decreased activity levels and playfulness with age in cats.

Concluding Remarks

This mini-review has shown that we have discovered much about the mechanics of social interactions between cats and their owners, but that more remains to be discovered when researchers apply new techniques, e.g., phonetic analysis of cat vocalizations, or by applying the Theme® algorithm to analyze such interactions. More observational studies comparing the behavior of different cat breeds and animals of different coat characteristics would be welcomed to substantiate and compliment the owners' qualitative assessments of personality traits. Further, it is hoped that an ethically acceptable method to test the prediction of Turner's ( 1 , 8 ) model on the effects of later positive and negative experiences with people on friendliness to people can be found.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: owners, socialization, communication, mood, cats, interactions, breed

Citation: Turner DC (2021) The Mechanics of Social Interactions Between Cats and Their Owners. Front. Vet. Sci. 8:650143. doi: 10.3389/fvets.2021.650143

Received: 06 January 2021; Accepted: 08 March 2021; Published: 31 March 2021.

Reviewed by:

Copyright © 2021 Turner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dennis C. Turner, dennis@turner-iet.ch

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

October 1, 2018

12 min read

The Inner Life of Cats

If you have ever wondered why your cat behaves the way it does, wonder no more

Mark Allen Miller

Ask me another

Interview by Kate Wong

As anyone who has spent time with cats knows, our feline companions are mysterious—much more so than those other furry family members. Here John Bradshaw, author of Cat Sense (Basic Books, 2013), fields a selection of questions submitted by Scientific American editors and Twitter followers about the cat’s many quirks. Bradshaw is a visiting fellow at the University of Bristol School of Veterinary Sciences in England, where he studies the behavior and welfare of cats and dogs, as well as their interactions with people.

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Are cats less domesticated than dogs? Are they becoming more domesticated over time?

Cats are far more similar to their wild ancestors than dogs are to wolves, so dogs are in that sense the more domesticated of the two species. As they adapted to living alongside humans, cats became more sociable with one another and much more accepting of people, but there is no evidence that they have changed much more than that over the past few thousand years.

Will cats, which require meat, eventually evolve to eat a broader array of foods as dogs do?

Cats and dogs belong to a group of mammals known as Carnivora, and the wild ancestors of both species dined primarily on meat. Recent DNA analyses indicate that over the course of their evolution, dogs have acquired more copies of the so-called amylase gene, which makes an enzyme that helps to break down starch. Having more copies of this gene has allowed dogs to eat a more omnivorous diet. In contrast, the cat family, known as Felidae, lost the genes that encode several key enzymes—including those that manufacture vitamin A, prostaglandins and the amino acid taurine—early in its evolution. Whereas dogs (and humans) can synthesize these substances from plant-based precursors, cats have to obtain them from meat. To expand their diet, cats would have to evolve physiological traits that allow them to synthesize these and other key nutrients from plant foods. This capacity has not emerged during the 10 million years of felid evolution, so it seems unlikely to arise spontaneously in our domestic cats.

Why do cats purr?

Cats purr because they have something to say, which roughly translated is “please keep still and pay attention to me.” Kittens purr to persuade their mothers to keep on nursing them, and pet cats purr when they want to be stroked. The vibrations emanating from the purr certainly have a calming effect on people. Yet sick cats will also purr as a cry for help. So purring doesn’t always mean “I’m happy.” Some researchers have claimed that the vibrations from purring might help heal bone damage in an injured cat.

Credit: Mark Allen Miller

How do they purr?

The purr is an unusual vocalization, made by rattling the vocal cords together rather than vibrating them by pushing air past them, which is how cats—and humans—generate all their other vocal sounds. That’s why cats can purr when they’re breathing in and breathing out. Most species of wildcats can purr, including the cheetah. The exceptions are the big cats—lion, tiger, jaguar and leopard—whose voice boxes are modified so that they can roar.

Why do house cats have so many vocalizations compared with wildcats?

House cats are much noisier than feral cats, although they have fewer vocalizations than some other species. The jungle cat from Asia, for example, has a couple more that are not in the house cat’s repertoire, namely the “ow” and the “gurgle.” The house cat’s characteristic sound, the meow, is hardly ever heard in feral cat colonies, except occasionally when mother cats are communicating with their kittens. Feral cats diligently monitor one another’s comings and goings, so they don’t need to announce their presence vocally. Cats that live with humans, however, learn that meowing is a good way of getting our attention: our pet felines often find that we have our noses buried in a book or a screen, so they meow to get us to acknowledge them. Some pets develop a “private language” of meows that only their owners understand, each signifying something different that the cat needs. Also, certain breeds are notoriously chatty, the Siamese in particular.

Do cats think of their owners as parents? Siblings? Friends?

By rubbing around our legs when they greet us, cats show that they regard us as friendly but at the same time slightly superior to them. When living in a family group, kittens rub on their mothers, females rub on males and smaller cats rub on bigger cats. The reverse rarely occurs—an indicator of the small imbalance of power in each of these relationships.

How can you get a cat to love you?

Cats naturally feel affection for those who feed, look after and play with them, although they don’t always make that obvious. Trust can be harder to win because some cats are just nervous. One trick is to always allow the cat to approach you rather than forcing yourself on the cat when you feel like it. When the cat does approach, reward it with a few treats and let it leave as soon as it wants to. You should find that it comes to you a little sooner, and stays for a little longer, each time.

Why do cats sometimes suddenly bite or scratch the person who is petting them?

You have probably missed the warning signs that the cat has had enough. Some cats, even if they enjoy being petted, also tire of it very quickly. Flattened ears and a slight twitching of the tail can signal that they have had their fill. If you have such a cat, try breaking off from stroking it every few seconds and allow it to move away if it wants to.

Can cats get jealous?

Research has demonstrated that dogs can get jealous, so cats probably can, too. For both species jealousy is an in-the-moment emotion they experience when they see that another individual is getting the attention they are craving: they don’t remember being jealous once the moment has passed. As with dogs, cats have a limited concept of time: they can learn to distinguish between different time intervals but only when these intervals are limited to a few seconds. Thinking back into the more distant past is probably beyond their abilities.

Do cats remember different people or just the people they see on a daily basis?

Scientists have not studied this specific trait directly, but cats do seem to forget other cats once they have been separated for a long time, so the same probably applies to people as well. Dogs, for whom humans are much more important, can remember people by their scent for months, probably years.

Do coat colors and patterns correspond to certain personality traits in cats?

Cat owners commonly believe that coat color can predict the personality of their pet, but there’s no evidence that this is universally true. Locally, a very successful male can produce a lot of kittens that both look like him and behave like him because of their shared genes, which might explain how people have come to associate coat color with personality. Among pedigree cats, the major breed types do have characteristic personalities: the so-called oriental breeds tend to be particularly active, the exotics more laid-back. Cats that are hybrids of domesticated cats and wildcats exhibit even stronger breed-specific personality traits. For example, Bengals, which are a cross between domestic cats and the Asian leopard cat, tend to be extremely adventurous and sleep for only about half the time that a typical domestic cat does.

Why do cats bring their kills to their humans?

It is tempting to see these kills as presents from our feline companions, but they are not intended that way. Cats take their kills somewhere safe before they eat them. If this location happens to be in or near the kitchen, they are automatically reminded that commercial cat food is much tastier than mouse, and hence they abandon their catch then and there.

Why do they knock objects off tables and shelves?

Some cats are just clumsier than others, and material possessions mean nothing to them. Others will bat items over the edge simply to get their owner’s attention. Sometimes they seem to do it for their own entertainment or because they have learned that this is a game that their owner seems to enjoy.

Do indoor cats suffer from not being able to go outside?

If a cat has lived indoors all its life, it probably doesn’t “miss” what it has never experienced. Outdoor cats that are suddenly confined do get stressed, however. All indoor cats should be given plenty of stimulation—hunting games using preylike toys are ideal.

Why do cats sometimes suddenly decide to run around like crazy for no apparent reason?

Cats can get quite frustrated if they are bored or if they are stressed, perhaps because they have just seen a rival cat through the window. Under such circumstances, the slightest movement, perhaps just a speck of dust caught in a shaft of light, can set them off. Regular play sessions can help dissipate all that energy and relieve any stress.

Why do cats love to climb into boxes, including ones that seem too small?

Cats like to feel well protected, especially when they intend to sleep, and cardboard cartons can be ideal for a secure nap. Why some seem to prefer too small boxes over just right ones is a mystery, though.

Why do cats climb to heights that they are subsequently afraid to jump down from?

Because cats’ claws face backward, they’re much more useful for running up trees than for climbing down. (The margay, a tree-dwelling South American cat, has double-jointed ankles and wrists that allow it to descend as easily as it ascends.) Inexperienced or frightened cats may go higher than they should. But they usually succeed in descending eventually because they have a reflex that enables them to fall safely, even from quite considerable heights. They stretch all four legs out sideways, so that the loose skin on their belly forms a kind of parachute. Then, a split second before reaching the ground, they push their feet downward and arch their back to minimize the shock of landing.

Why do some cats go crazy for catnip and others show no response at all?

The catnip response is a bit of an enigma because it consists of seemingly random segments of play (pouncing, clutching with the claws), socializing (cheek rubbing) and female sexual behavior (rolling onto the back, for example). Many cat species have this reaction to the herb, including lion, tiger, leopard and lynx. Yet not all individuals in these species show the response because it is controlled by a single gene that only about two thirds of cats carry [see “Catnip’s Magic,” below]. There is no evidence to suggest that cats that ignore catnip are deficient where play, socializing and sex are concerned, however. The catnip response may simply be an evolutionary accident.

Why do cats lick and chew plastic bags, power cords and other nonfood items?

Veterinarians have noted an unhealthy taste for plastics in some cats but have never convincingly explained it. Cats may be trying to alleviate stress. Among oriental cat breeds, adults commonly suck, chew and eat fabrics, especially wool, in response to stress. Also, kittens of all kinds that are weaned too early may develop the habit of suckling on a blanket, usually purring and kneading with their front paws at the same time, as if the blanket were their mother.

Why do they chew their fur?

There are two possible reasons for this behavior. One is itchy skin, possibly caused by fleas or other parasites, or an allergy. But persistent overgrooming on one spot or pulling out clumps of fur can also be signs of chronic stress, perhaps because of conflict with another cat in the house or in the neighborhood.

Why do cat feet sometimes smell like tortilla chips?

A common minor fungal skin infection, similar to athlete’s foot in humans, often produces this smell. Dogs may carry this fungus, too.

Why can’t domestic cats go without food for long, like their big cat cousins can? They get fatty liver disease in a couple of days.

All cats, big and small, can suffer from fatty liver disease, in which fat released from other parts of the body accumulates in the liver, blocking its ability to carry out key functions such as recycling red blood cells. This condition can occur as a result of cancer, diabetes or kidney disease, but it can also be triggered by the cat not eating for some reason (stress is a common cause in pet cats). It comes on much more quickly in obese cats, so it’s probably less common in big cat species simply because living in the wild they’re unlikely to be overweight.

Why are cats so finicky about food and their litter boxes? Why are they so sensitive to any change in routine?

Cats have much more exacting nutritional requirements than dogs do, so they have to be careful about what they eat. Also, as solitary hunters, their ancestors could afford to eat in their own good time. Dogs, in contrast, are descended from pack-hunting wolves, which evolved to eat quickly so they could grab their share of the kill. Cats also prefer to hide their feces, which would otherwise betray their whereabouts to prey and predators alike, hence their reluctance to use a dirty litter box. Cats are much more sensitive than dogs are to any change in their environment because they are territorial animals that get their sense of security from the place where they live. That’s why cats often try to get back to their owner’s previous home in the first few weeks following a house move.

Will cats really consume their deceased owners in some situations?

If they are literally dying of hunger, cats (and dogs) will eat anything available. I’m sure that they don’t mean to cause offense!

CATNIP’S MAGIC

Have you wondered why some cats exhibit strange behavior when exposed to catnip? They act much like a female cat in heat: They may rub their head and body on the herb or jump, roll around, vocalize and salivate. This response lasts for about 10 minutes, after which the cat becomes refractory to catnip’s effects for roughly 30 minutes. Response to catnip is hereditary; about 70 percent of cats exhibit this behavior in the plant’s presence, although it does not affect kittens until they are about six months old and begin to reach sexual maturity.

The genetically susceptible cats are responding to a volatile oil in the stems and leaves of the plant Nepeta cataria and other Nepeta species, which are members of the mint family. When the oil, named nepetalactone, enters the cat’s nasal tissue, it apparently binds to proteins that stimulate sensory neurons. These nerve cells, in turn, provoke a response in neurons in the brain’s olfactory bulb, which project to several regions of the brain, including the amygdala (two neuronal clusters deep in the temporal lobes that mediate emotional responses to stimuli) and the hypothalamus (the brain’s “master gland”), which is known to play a role in regulating everything from hunger to emotions.

The hypothalamus, acting through the pituitary gland, alters hormonal levels to spur the sexual-like response. In other words, the cat essentially responds to the oil as if it were a pheromone emitted by another cat. Catnip is considered to be nonaddictive and completely harmless to cats. — Ramona Turner

Ramona Turner is a veterinarian practicing in Fresno, Calif.

BE A CAT RESEARCHER

Cat lovers around the globe can help researchers better understand the feline mind while helping cats lead happier and healthier lives. Check out the following citizen science projects. In some cases, you do not need to live with a cat to participate.

Track Your Cat

NC State University, Movebank.org and North Carolina Museum of Natural Sciences

http://cattracker.org

Live with a cat that goes outside? Investigators know very little about where cats go when they leave the home and even less about why some travel the open road and others stay nearby. In this project, set up a GPS cat-tracking device and find out where your cat goes.

Cat Tracking Down Under

Cat Tracker Australia, a Discovery Circle project

www.discoverycircle.org.au/projects/cat-tracker

Do cats around the world show similar movement patterns? Does the local environment affect how cats roam? This cat-tracking project in several states in Australia—open to anyone in the region, not just cat owners—will provide a cross-continental comparison of cat behavior. It includes GPS cat tracking, a cat personality test and a general survey.

The Feline Biobank: Genetic and Genomic Resources for the Cat

Cornell Feline Health Center, Cornell University College of Veterinary Medicine

https://www2.vet.cornell.edu

What role do genes play in common feline diseases? By comparing DNA from healthy cats with DNA from cats with one or more common diseases, scientists can better understand the role that genes may play in these conditions. This project could assist veterinarians in their ability to diagnose and treat common diseases. The project is looking for DNA samples from healthy cats older than 10 years old.

Investigating a New Class of Anticancer Drugs for Mammary Cancer in Cats

www2.vet.cornell.edu

Is there a way to stop mammary cancer in its tracks? Help reveal how feline mammary cancer develops and lay the groundwork for new ways to treat it. Investigators are accepting both normal and cancerous feline mammary gland tissue samples from female cats.

Understanding Cat Pain

University of Lincoln School of Life Sciences, England

http://ucpproject.co.uk

Pain is an unpleasant sensory and emotional experience that involves a physical response as well as behavioral change. This project explores the facial, postural and vocal expressions of pain in cats—information that gives owners and veterinarians a leg up on recognizing this emotion earlier so cats can be treated before their welfare is compromised. If you own a cat that you think is in pain, contribute a video or picture. You can also take a quick quiz and check your ability to detect pain in these animals. — Julie Hecht

Julie Hecht is a Ph.D. student studying dog behavior and author of the Dog Spies blog at ScientificAmerican.com

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Putting Cats on the Spectrum: A Scoping Review of the Role of Cats in Therapy and Companionship for Autistic Adults and Children

Affiliations.

• 1 School of Nursing, Midwifery & Social Sciences, Central Queensland University, Sydney, NSW, Australia.
• 2 Nepal Public Health Research and Development Center, Kathmandu, Nepal.
• 3 School of Nursing, University of Tasmania, Sydney, NSW, Australia.
• PMID: 37075312
• DOI: 10.1080/01612840.2023.2195509

This scoping review brings together existing studies on the use of cats in animal-assisted interventions, as assistance animals and as companion animals for autistic people. A systematic search across PubMed, CINAHL and Scopus in September 2022 identified 13 articles from 12 studies meeting the selection criteria with analysis revealing two key findings, cat-assisted therapeutic interventions, and cats as companion animals. There were five themes that emerged: the characteristics and behaviours of cats that make them suitable for inclusion in homes with autistic people; the bond between the cat and the autistic person; the use of cats as human-substitutes; the multiple ways in which cats improved the lives and social functioning of autistic people; and, some noted drawbacks or considerations of cat ownership. The review generates a comprehensive knowledge base upon which to promote feline therapy in autism and to advocate for further targeted research.

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By Michael Marshall

7 March 2023 , updated 16 March 2023

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Effects of the characteristic temperament of cats on the emotions and hemodynamic responses of humans

Takumi nagasawa.

1 Graduate School of Agriculture Studies Tokyo University of Agriculture, Atsugi, Kanagawa, Japan

Mitsuaki Ohta

Hidehiko uchiyama.

2 Faculty of Agriculture, Tokyo University of Agriculture, Atsugi, Kanagawa, Japan

Associated Data

All relevant data are within the paper and its Supporting Information files.

Cats positive effects on their owners’ physiological and psychological health, including improved mood and activation of the human prefrontal cortex and inferior frontal gyrus in the brain. However, the association between the health benefits provided by cat ownership and the characteristic behaviors and reactions of cats is unclear. We recruited 29 participants to measure human prefrontal cortex activity, using functional near-infrared spectroscopy, during interactions with a cat. After the experiments, participants subjectively responded to a questionnaire regarding success rates for interactions with the cat, and completed the Self-assessment Manikin—a scale used to measure emotion. Interactions comprised eight types in four categories (touch, play, train, and feed). This study showed that interactions with a cat significantly activated the prefrontal cortex, regardless of interaction type. During training, the integral values of oxygenated hemoglobin in the left inferior frontal gyrus were the highest in all the interaction categories; however, success rates were lower than in the touch and feed interactions. Regarding the Self-assessment Manikin scores, all interaction categories showed a positive correlation between success rate and valence score, especially in the train and play interactions than in the touch and feed interactions. These results indicate that interactions with a cat activate the prefrontal cortex in humans, including the inferior frontal gyrus region. Moreover, cats’ autonomous behaviors and reactions positively influenced the participants. The characteristic temperament of cats may be a key factor influencing the health benefits of owning cats.

Introduction

Humans throughout the world have pets, especially dogs ( Canis familiaris ) and cats ( Felis silvestris catus ), which have positive effects on the health of their owners. Growth from knowledge reported that 57% of people internationally have at least one pet (dogs: 33%, cats: 23%) [ 1 ], and 64% of such people regularly spend time with pets to maintain their physical health [ 2 ]. Previous studies also showed that companion animals can promote people’s physiological, psychological, and social health [ 3 ]. Recently, the number of pet cats has grown to exceed that of dogs in some countries [ 1 ], including Japan (dogs: 8,903,000, cats: 9,649,000) [ 4 ]. Cats have adapted to co-exist with humans and become the most popular companion animal.

Some studies have reported that owning a cat provides beneficial health effects for the owner. Cat ownership has been linked to a reduced risk for minor health problems, such as headaches and hay fever [ 5 ]. Petting a cat can decrease blood pressure and heart rate [ 6 ], and cat ownership is associated with a decreased risk of death due to myocardial infarction or cardiovascular disease [ 7 ]. Furthermore, cats can be sources of emotional support for their owners [ 8 ], and reduce negative emotions [ 9 ]. These findings indicate that cats can provide both physiological and psychological health benefits.

Recent studies have shown an association between owning pets and improved executive functions [ 10 ], such as working memory [ 11 ], which is controlled by the brain’s prefrontal cortex (PFC) [ 12 ]. Studies have shown that the PFC can be activated by petting [ 13 ] or hearing a cat [ 14 ]. In one study, Kobayashi and colleagues primarily focused on the inferior frontal gyrus (IFG) region of the PFC [ 13 ], which controls functions related to nonverbal communication, such as theory of mind [ 15 ], processing others’ facial expressions [ 16 ], and empathy [ 17 ]. Moreover, individuals with autism spectrum autism spectrum disorder (ASD) often have impaired function in the IFG region and a deficit in the mirror neuron system [ 18 ]; therefore, owning a cat could help improve nonverbal communication skills of individuals with ASD. However, previous research only focused on one source of sensory stimulation at a time (e.g., tactile [ 13 ] or auditory [ 14 ]). Moreover, these studies were not designed to focus on the everyday interactions between cats and their owners.

In households with cats, feeding, playing, and physically interacting with cats is common [ 19 ], as well as essential for building good relationships between cats and their owners. Recently, positive reinforcement training, such as clicker training, has been shown to be an effective method to improve the welfare of cats in animal shelters [ 20 ]. However, it is unclear whether there is an association between these interactions with cats and the health benefits people can experience through cat ownership.

Compared to cat ownership, the health benefits of dog ownership are more apparent. Walking a dog, one of the everyday interactions between dogs and their owners, could have various health benefits for dog owners. Walking a dog activated parasympathetic nerve activity [ 21 ], and created a habit of engaging in physical activity [ 22 ]. Moreover, walking a dog could increase one’s opportunities for social interactions with others [ 23 ]. Dog ownership has also been associated with a reduction in the risk of cardiovascular disease [ 24 ] and its associated mortality rate. Additionally, in several studies, researchers focused on dogs’ behavioral reactions during experiments. For example, Nagasawa and colleagues reported that gazing behavior from dogs increased urinary oxytocin concentrations in owner [ 25 ][ 26 ]. Another study reported that interaction with dogs could affect the concentration of hormones such as cortisol and oxytocin in their owners’ blood [ 27 ].

Through the process of domestication, dogs have learned to display obedient behaviors toward humans. The purpose of domesticating dogs was to allow them to work with humans (e.g., guarding and hunting [ 28 ]); thus, more submissive traits were chosen by artificial selection. On the contrary, cats do not typically display obedient behaviors toward humans. As cats were originally utilized for their instinctual hunting ability to decrease rodent populations [ 29 ], cats may have been domesticated by natural selection, not selective breeding [ 28 ]. Marinelli and colleagues found the factors that affect the quality of dog–owner relationships differ from those for cat–owner relationships [ 30 ], and stated the tools used to study human–animal bonds need to be customized by species. Therefore, in the study of human–cat relationships, the behaviors and reactions of a cat should be the point of focus to determine if it is the factor that leads to health benefits for cat owners.

We designed this study based on various everyday cat–human interactions, focusing on the characteristic temperament of cats during regular interactions, and examined whether the characteristic temperament of cats affects human physiological and psychological health by assessing a cat’s behavioral reactions. We hypothesized that everyday interactions with a cat activates the PFC of the human brain, including the IFG region, and affects human’s moods. Particularly, the characteristic behaviors and reactions of cats could positively influence these effects.

Materials and methods

Ethics statement.

The experiments in this study were approved by the Human Research Ethics Committee (approval no. 1134) and Animal Experiment Ethics Committee (approval no. 1301312) at the Tokyo University of Agriculture in accordance with the World Medical Association’s Declaration of Helsinki.

Participants and the test animal

We recruited 29 participants (10 men and 19 women) from the Tokyo University of Agriculture. Participants’ mean age was 21.17 ± 0.65 years. Sixteen participants had experience owning cats. All participants provided verbal informed consent before the experiment. No participants were allergic to cats. One spayed female cat (ragdoll breed; nine-years-old) was used in this study. The cat had always lived in the laboratory and was already trained to perform some behaviors by using positive reinforcement (e.g., raising her paws, touching humans’ hands, sitting down, turning around, lying down, etc.). Before the experiment, all participants encountered the cat while receiving an explanation of the experimental protocol; there was no person who had a specific and close relationship with the cat.

Functional near-infrared spectroscopy (fNIRS)

During all experimental tasks, oxygenated hemoglobin (oxy-Hb) concentrations were recorded using the fNIRS method. We used an OEG-SpO2 (Spectratech, Inc., Kanagawa, Japan). Fig 1 shows the arrangement of the apparatus. Using the basis of the International 10/20 System, the center of the probe holder was placed on Fpz, the bottom left of the corner was placed on F7, and the bottom right of the corner was placed on F8 [ 31 ]. The temporal resolution was set at 0.08 s. This fNIRS method has approximately 770 and 840 nm wavelengths as near-infrared light. This instrument consists of six illuminator probes and six detector probes. The distance between the illuminator and the detector was fixed at 3 cm.

Black circles are illuminators. White circles are detectors. The number is the measurement region of 16 channels.

Self-Assessment Manikin (SAM)

The SAM is a nonverbal pictorial assessment method that directly and quickly measures affective responses in many contexts [ 32 ]. It comprises three dimensions (valence, arousal, and dominance) consisting of 5-panel graphic depictions, rated on a 9-point scale (see Fig 2 ). We focused on the valence and arousal dimensions because they are the two factors related to emotions.

Behavioral reactions of the cat

After their interactions with the cat, participants subjectively assessed (by percentage) whether the interaction with the cat was successful (0%: the cat did not obey to 100%: the cat perfectly obeyed ). Each question was arranged in an order designed to reflect the thoughts of the participants (e.g., touch interaction category: did the cat allow itself to be brushed/petted; see S1 Appendix ).

We divided the experiment into four interaction categories ( Table 1 ). We set two items for each interaction type. Table 1 shows the details of each interaction category: “touch” (brushing with a comb/petting by hand), “play” (with a stick/with a small rubber ball), “train” (tactile/non-tactile), and “feed” (giving food/water). The experiments consisted of one interaction item in each of the four categories.

The experiment was conducted in a 3 × 3m room in the laboratory. The cat was familiar with the experimental space. The protocol of the experiments is shown in Fig 3 . Participants were asked to sit in the middle of the room. For each interaction item, participants interacted with the cat for 30 seconds (task time). Before and after each interaction, participants rested for 30 seconds (pre- and post-times). During the pre- and post-times, the participants stared at a cross mark written on a paper on the wall, and repeated the Japanese vowels (/a/,/i/,/u/,/e/,/o/) in their head to stabilize their prefrontal Oxy-Hb concentrations [ 13 ]. When the task started, the experimenter put the cat in front of the participant. During all the stages of the experiment, participants were not allowed to speak to the experimenter, bow their head, or stand. However, participants were allowed to talk to the cat, but only with words related to the interaction (e.g., call the cat’s name). After the experiment, participants completed the SAM to identify their emotions and the behavior of the cat during the interactions.

fNIRS analysis

NIRS enables us to measure signal changes in Oxy-Hb and Deoxy-Hb. The direction of changes in Oxy-Hb is always the same as that of the change in regional cerebral blood flow; however, the direction of changes in Deoxy-Hb is influenced by other factors (venous blood oxygenation and volume) [ 33 ]. Therefore, we focused only on Oxy-Hb signals. We removed the physiological fluctuations, caused by body motion and posture change, from Oxy-Hb signals using the hemodynamic modality separation system [ 34 ].To analyze participants’ Oxy-Hb signals of PFC over time during the experiments, we calculated the average value from all participants combined using the BRainAnalyzer (B.R.Systems Inc., Kanagawa, Japan). We then averaged all 16 channels in the data set.

Based on previous study [ 13 ], to compare the degrees of activation in each interaction category, integral values of Oxy-Hb were calculated using the BRainAnalyzer ( Fig 4 ). When we compared interaction types, we adjusted the start of the integral values to baseline. We focused on the right and left IFG regions of the brain. These regions placed F7 and F8; thus, channel 1, 2, 3, and 4 channels reflect right IFG, and 13, 14, 15, and 16 channels reflect left IFG [ 35 ].

Statistical analysis

We determined the difference of the mean Oxy-Hb signal during rest and task time by performing an analysis of variance and a Bonferroni test as post-hoc analyses. Using the Friedman test and Scheffe’s method as post-hoc analysis, we examined the difference among four interaction categories: the integral values of the left and right IFG, participants’ mean success rate for interaction with the cat, and participants’ mean SAM score. Additionally, we compared the differences of the integral values of the left and right IFG between participants who had experience owning cats and those who did not (Welch’s t -tests). We assessed the correlation between success rates and the integral values of the left and right IFG and SAM score using Spearman’s rank correlation coefficient. All statistical analyses were performed using BellCurve for Excel (Social Survey Research Information Co., Ltd., Japan).

Data were analyzed from 27 participants. Two participants were excluded from analysis because one participant did not complete the experiment, and we failed to measure channel 1 of another participant.

Sequential change of the Oxy-Hb signal

The sequential change of the Oxy-Hb signal is shown in Table 2 . Significant differences in the mean Oxy-Hb concentration were observed among pre-time, task-time, and post-time (touch: F = 483.63, p < .01; play: F = 372.39, p < .01; Train: F = 509.48 p < .01; feed: F = 363.27, p < .01). A post-hoc analysis using a Bonferroni comparison indicated there were significant differences in all interaction categories: touch: pre vs. task (t = 26.82, p < .01), pre vs. post (t = 27.06, p < .01); play: pre vs. task (t = 21.76, p < .01), pre vs. post (t = 25.14, p < .01), task vs. post (t = 3.38, p < .01); train: pre vs. task (t = 23.82, p < .01), pre vs. post (t = 30.32, p < .01), task vs. post (t = 6.51, p < .01); feed: pre vs. task (t = 22.85, p < .01), pre vs. post (t = 23.81, p < .01).

Integral values of the IFG

Fig 5 shows the mean integral values of the right and left IFG. For both the right and left IFG, significant differences were observed among interaction categories (right IFG: X 2 = 41.68, p < .01; left IFG: X 2 = 73.11, p < .01).

Mean integral values of the left (a) and right (b) IFG in each interaction categories. Error bars indicate SE. Different letters indicate significant differences by Scheffe’s method ( p < .05).

For the right IFG, a post-hoc analysis using Scheffe’s method indicated that the feed interaction (1.88 ± 0.40) had smaller integral values than did the touch (4.84 ± 0.55, X 2 = 16.23, p < .01), play (4.60 ± 0.48, X 2 = 10.05, p < .05), and train (6.59 ± 0.65, X 2 = 40.69, p < .01) interactions. The train interaction had higher integral values than did the play interaction (X 2 = 10.29, p < .05).

For the left IFG, the feed interaction (1.52 ± 0.37) had smaller integral values than did the touch (4.58 ± 0.53, X 2 = 27.27, p < .01), play (4.68 ± 0.52, X 2 = 24.61, p < .01), and train (7.14 ± 0.69, X 2 = 71.70, p < .01) interactions. The train interaction had higher integral values than did the touch (X 2 = 10.53, p < .05) and play (X 2 = 12.30, p < .01) interactions.

According to Welch’s t -tests, in all interaction categories, there were no significant differences in the integral values of the left and right IFG between participants who had experience owning cats and those who did not.

Valence scores showed significant differences among interaction categories (X 2 = 9.85, p < .05, Fig 6A ). A post-hoc analysis using Scheffe’s method indicated that the train interaction (5.06 ± 0.33) had a lower score than did the feed (6.46 ± 0.21 X 2 = 9.34, p < .05) interaction.

The mean SAM score among interaction categories for (a) pleasure and (b) arousal. Different letters indicate significant differences by Scheffe’s method ( p < .05).

Arousal scores also showed significant differences among interaction categories (X 2 = 29.37, p < .01, Fig 6B ). The play interaction (4.61 ± 0.28) had a significantly higher score than did the touch (3.15 ± 0.24, X 2 = 22.45, p < .01) and feed (3.37 ± 0.29, X 2 = 15.64, p < .01) interactions. The train interaction (4.50 ± 0.28) had a significantly higher score than did the touch (X 2 = 24.12, p < .01) and feed (X 2 = 17.05, p < .01) interactions.

Success rate

Significant differences were observed in the success rates among interaction categories (X 2 = 36.36, p < .05, Fig 7 ). A post-hoc analysis using Scheffe’s method indicated that the touch interaction (62.78 ± 3.93%) had a higher success rate than did the play (36.02 ± 4.55%, X 2 = 8.56, p < .05) and train (35.46 ± 5.03%, X 2 = 11.48, p < .01) interactions. The feed interaction (75.65 ± 5.48%) had a higher success rate than did the play (X 2 = 24.29, p < .01) and train (X 2 = 29.05, p < .01) interactions.

Error bars indicate SE. Different letters indicate significant differences by Scheffe’s method ( p < .05).

Correlation with success rate

We analyzed the correlation between the success rate scores and the integral values of the IFG (right and left side), as well as between success rate and SAM scores individually ( Table 3 ). The correlation between success rate and the integral values of the IFG was not significant in all interaction categories. However, SAM scores significantly correlated with success rates. For valence scores, there was positive correlation among all interaction categories (touch: r = 0.53, p < .05; play: r = 0.61, p < .01; train: r = 0.68, p < .01; feed: r = 0.54, p < .01). For arousal scores, only the play interaction positively correlated with success rate (r = 0.51, p < .01).

* p < .05,

** p < .01.

Sequential change in Oxy-Hb signal of the PFC

Interactions with a cat activated participants’ PFC, regardless of interaction type. The experiment protocol consisted of interactions typical in cat owners’ homes; therefore, this result suggested that owning a cat enhances the function of the owners’ PFC. Furthermore, PFC controls executive function [ 12 ]; therefore, interactions with a cat may improve executive function. This result is consistent with previous studies [ 10 ][ 11 ]. To our knowledge, this study may be one of the first to explain the mechanism that everyday interaction with cats enhance PFC function.

Integral values of the IFG for the train and play interactions

We focused on the integral values of Oxy-Hb signals in left and right IFG regions. In all interactions, the integral values did not correlate with each success rate individually. However, there was a significant difference among interaction categories. The integral values of the train interaction were larger than those of the other interaction categories. Notably, in the left IFG region, the integral values of the train interaction were significantly larger than those of the other categories. The left IFG controls the mirror neuron system [ 36 ] and empathy [ 37 ]. This study suggested that performing training interactions with a cat would be an effective way to help develop these brain functions.

There are several possible reasons for significant activation of the IFG during the train interaction. First, participants might have not been accustomed to training a cat; thus, it is possible that unnatural interaction situation promoted Oxy-Hb activation for participants. Training is still not a typical interaction between a cat and its owner in general households. Nevertheless, training using clicker has recently become a standard method to improve cats’ welfare [ 20 ] and develop effective relationships between cats and humans [ 38 ]. Training a cat should be recognized as a common interaction between cats and their owners.

Second, the characteristic temperament of cats (i.e., not typically displaying obedient behavior) might have been the reason for activation of the IFG. For the train interaction, participants reported significantly lower success rates than for the feed and touch interactions. The cat frequently showed autonomous behaviors and reactions to participants owing to the independent nature of cats. Participants might try to anticipate the cat’s next action and determine how to succeed. This thinking process might have been the reason for the activation of the IFG.

The play interaction also showed a lower success rate than did the feed and touch interactions. Further, the integral values of the IFG during playing with the cat were larger than those during feeding. In the play interaction, it was difficult for participants to attract the cat to play. As with the train interaction, participants might think about the way to succeed in this interaction.

The thinking processes used during the train and play interactions related to fundamental nonverbal communication skills necessary not only for interactions between people and animals but also for interpersonal interactions. Therefore, the train and play interactions with a cat, which induce the activation of the IFG, have potential to treat individuals with ASD, which have impaired function in the IFG region [ 18 ]. Previous studies showed that interactions with an animal can improve the social communication skills of children with ASD [ 39 ][ 40 ]. Although much of the previous research has been performed using therapy dogs [ 41 ], a few studies have posited that cats can also be useful for therapy with people with ASD [ 42 ][ 43 ]. However, the mechanism was still unclear.

It is frequently difficult to speculate on cats’ behaviors, even for their owners. The behaviors and temperament of cats, such as independence, is a unique trait compared to dogs. As cat domestication was shorter than for dogs, and may not even be complete [ 28 ], the genes of domestic cats are not distinct from those of wild cats [ 44 ]; thus, even household cats frequently display autonomous behaviors like wild animals. The present results suggest that cats’ unique behaviors and reactions are the key factors explaining the mechanism underlying the health benefits that cats can provide to individuals with ASD. However, this study targeted healthy participants, not those with ASD; therefore, further studies are needed to determine whether cats positively effects the treatment of individuals with ASD.

Integral values of the IFG for the feed and touch interactions

During the feed interaction, the integral values were significantly less compared to the other interaction types; however, the success rate was higher than in the train and play interactions. Since feeding is the most fundamental interaction between a human and an animal, the cat relatively obeyed participants during the feed interaction. Participants may have felt it was easy to speculate on the cat’s behavioral reactions during the feed interaction; therefore, the IFG region was not activated.

As with feeding, tactile communication with a cat is a central interaction between a cat and its owner. In this study, the touch interaction showed a higher success rate than either the train or the play interaction; however, the integral values of the IFG were larger than during the feed interaction. This could be the result of tactile stimulation. A previous study showed that the IFG region was activated by touching a cat [ 13 ], which is consistent with the findings of this study. Therefore, the current results might show that tactile stimuli, which occur through interaction with a cat, affect IFG activation.

Valence scores from the SAM significantly positively correlated with success rates. The valence dimension in the SAM is the measurement of emotions, such as happiness and satisfaction [ 32 ]. In this study, participants felt positive emotion when the cat obeyed them. Moreover, the train and play interactions, which had a significantly lower success rate than the feed and touch interactions, showed a relatively higher correlation coefficient than the feed and touch interactions. Therefore, the present result indicates that the lower the success rate of interaction with a cat, the more likely positive emotions of the participants occurred when the interaction succeeds. As mentioned above, cats and dogs have different temperaments, and cats frequently showed autonomous behavior and reaction for their owners. These characteristic temperaments of cats may be the key factor to enhance human psychological status.

During the play interaction, only the arousal score for the SAM significantly positively correlated with success rates. The arousal dimension in the SAM is the measurement of emotions such as excitement [ 32 ]. In the play interaction, the success meant the cat responded to the cat toys using its paws. It is possible that the movement of the cat increased the arousal of the participants. Previous studies claimed that the arousal response is related to enhanced cognitive function [ 45 ]. Additionally, exercise, which increases arousal, also improves executive function [ 46 ][ 47 ]. Therefore, playing with a cat may promote s the development of human cognitive function. Furthermore, 90% of cat owners play with their cats at least once per day [ 48 ]; thus, play with cats is a common interaction for their owners. The results of the current study may show the mechanism of an association between owning pets and improved executive functions.

Limitations

This study had several limitations. First, we used a laboratory cat, not a house cat. This was because of the difficulty of conducting this experiment in cat owners’ homes. Domestic cats are territorial animals [ 49 ], and would not behave typically with their owner if an unfamiliar person and apparatus were to be in their territory. Thus, we utilized a laboratory cat. However, the cat had been raised in the laboratory like as a house cat; therefore, the cat had the characteristic temperament of a house cat.

Second, during the experiments, only participants could initiate an interaction, not the cat. Specifically, in the touch interactions, we requested that participants pet the cat. However, cats often display allogrooming (i.e., groom other cats using their tongue) and allorubbing (i.e., rubbing their head and tail toward other cats) behaviors toward humans [ 50 ]. If interactions between participants and the cat had been mutual, the results may have varied. In future studies, researchers should design a protocol that allows for free and mutual interactions between cats and participants.

Third, we used Bonferroni’s and Scheffe’s methods for post-hoc analyses; although, we did not use a false discovery rate approach. Therefore, further studies should use false discovery rate to control the proportion of false positives among channels that are significantly detected.

Conclusions

Our findings indicated that everyday interaction with a cat can activate a person’s PFC, including the IFG region, regardless of the type of interaction. Moreover, during training interactions, the cat often disobeyed the participants, which elicited significant IFG activation. Valence scores of participants positively correlated with the success rates for interactions with the cat; especially, the train and play interactions, which had significantly lower success rates than did the touch and feed interactions, and a high correlation coefficient.

This study showed that the autonomous behaviors and reactions of a cat influenced the physiological and psychological states of people; therefore, the characteristic temperament of a cat may be the key factor to the mechanism underlying the positive health effects gained through cat ownership.

Supporting information

S1 appendix, acknowledgments.

We thank Editage ( www.editage.com ) for English-language editing.

Funding Statement

The authors received no specific funding for this work.

Data Availability

Essays About Cats: Top 5 Examples Plus Prompts

Cats are some of the most beloved animals to humankind; this article contains writing prompts and essay examples to help you write essays about cats. 

When you think of animals, two things come to mind: cats and dogs. Cats are some of the most popular pets, as they are, for the most part, relatively independent, low-maintenance, and easy to care for. The word “cat” most often describes domesticated house cats but also refers to some of the most vicious predators on the planet, such as lions, cheetahs, and leopards. Nevertheless, they make great companions for people who enjoy staying home and spending time sitting down and petting them, which reduces stress and anxiety. 

If you want to start writing essays about cats, start by reading some essay examples.

For help with your essays, check out our round-up of the best essay checkers

1. Short Essay on “Cat” by Kirti Daga

2. life of stray cats by nathaniel bridges, 3. do cats understand mirrors by christine o’brien, 4.  why cats are bad pets by shannon cain.

• 5. ​​Why Are Cats So Incredibly Rude? by Julie Davidson

5 Writing Prompts For Essays About Cats

1. should you own a cat, 2. why are cats so loved, 3. my experience with cats, 4. cats vs. dogs, 5. my favorite breed of cat.

“If your cat has given birth to kittens, make sure that your house is quiet because a lot of noise and activity can scare a small kitten and a cat lover would never wish to scare a kitten for sure. Cats can be shy in nature and can even take time while adjusting with the environment. One needs to be patient and deal with the animal with a lot of love and care.”

Daga gives a basic description of cats’ physical features, personalities, and misconceptions about them. They are gentle and playful yet, to an extent, selfish. Many believe that cats are related to black magic and bad luck; however, this is entirely false. Daga ends the essay by briefly discussing how to tame a cat and care for one that has given birth to kittens.

“Although it’s impossible for us to adopt every stray cat on the street, but imagine if every family manage to keep a pet cat in their home. That can actually save a lot of their lives. Some might have allergies towards animals but you can still help by providing clean water and some food outside of your house for the cats. This can avoid them from eating poisonous or unhygienic foods and also let them have a healthier life.”

In his essay, Bridges implores readers to be more sympathetic to the plight of stray cats. They have difficulty finding food and are involved in many accidents, particularly with cars. Bridges suggest leaving out food and water for stray cats, so they eat healthier food than whatever they scavenge for. In addition, he encourages people to adopt stray cats, although this is not for everyone, as some may have allergic reactions. 

Looking for more? Check out these essays about dogs .

“the extent of cat self-awareness is still a mystery. Despite all of the wisdom contained in her all-knowing eyes, when your cat’s pacing back and forth in front of mirror, she’s probably not admiring the sleekness of her coat or the smoothness of her freshly-trimmed nails.

More than likely, she’s investigating the stranger that is too close for comfort.”

O’Brien writes about the phenomenon in which cats look at themselves intently in the mirror. Based on research, cats do not recognize themselves and continue to look into the mirror to assess possible threats. As animal brains are less developed, they do not understand that they see themselves and instead see their reflections as other animals. They are not looking at themselves as people claim but trying to perceive the presence of another cat. 

“How many people do you see taking their cat with them on car rides? Or having a nice walk in the park? Absolutely no one. If you’ve ever brought your cat in the car, you know how loud, annoying and horrible it is, not only for them but you as well. The whole time, all you hear is their pitiful meow from the carrier, which is in the very back, covered in blankets to drown out the ear-splitting screeches.”

Cain’s essay explores the more negative aspects of cats, particularly compared to owning a dog. She starts by recalling ancient Egyptian traditions by which cats were associated with divinity and protectors from evil spirits, demons, and hell. She also discusses several bad qualities of cats; they are “a bit messy,” “filthy,” “annoying,” and “horrible.” While Cain does not hate cats, she believes dogs are preferable. 

5. ​​ Why Are Cats So Incredibly Rude? by Julie Davidson

“Cats hold a grudge. When a cat is mad, she wants you to acknowledge it. Some will act out doing such things as clearing the books off the coffee table, sumo wrestling a feline roommate, or emptying her water dish out onto the floor—all to get your attention. But, just when she has pushed us to our absolute limit, a cat flashes those big kitten eyes (picture Puss in Boots from Shrek), and we melt like a snowman in the Sahara.”

Davidson writes about some of the cats’ bad habits, particularly their “rudeness.” They demand attention, put up a bad attitude when it is not given, and do things considered “adorable” to win back the favor of their owners. Cats are lovable yet manipulative; however, this is part of their nature, and cat owners must deal with it. For more, you can also see these articles about cats .

In this essay, research and list the advantages and disadvantages of owning a cat- what positive and negative traits do they have? Then, conclude whether you would recommend getting a cat as a pet to others. Of course, this would be easier if you own or have a cat, but ample research will suffice. This is an excellent topic for an argumentative essay, as you can find many arguments for and against owning a cat online. 

It is a fact that cats are loved by many. What makes cats so lovable? In your essay, look into some qualities of cats that make them so beloved and ideal as pets. If you do not have a cat,  you can base your essay on interviews with cat owners or information from the internet. 

Think of a memorable occasion when you interacted with a cat, whether with your pet, a family member or friend’s cat, or even a stray cat outside. How did it make you feel- were you stressed, relaxed, or disgusted? Your essay should be a retelling of a personal story; do not include others’ opinions or ideas from online sources. 

For an engaging argumentative essay, decide which animal you prefer: cats or dogs. Research and write about the advantages and disadvantages of having either of them as a pet, then decide which one you would prefer. Be sure to justify your choice; you can use some of the essay examples above as evidence, 

Do you have a favorite breed of cat? How about the species of cat overall? For your essay, write about your favorite type of cat, whether a lion, tiger, or adorable Persian cat. Explain why it is your favorite and, if applicable, any other special meaning the cat has to you. 

If you’d like to learn more, check out our guide on how to write an argumentative essay .

For more help, check out our guide packed full of transition words for essays .

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UK position on EU's Research and Innovation Framework Programme

UK Position Paper published on emerging themes related to the shape and direction of the European Union’s Research and Innovation Framework Programme 10.

PDF , 204 KB , 11 pages

This paper sets out the UK government’s views on the 10th Research and Innovation Framework Programme ( FP10 ).

The UK have been associated to Horizon Europe since the start of 2024, and as such, we are following the developments around its successor programme with interest.

FP10 , the successor programme to Horizon Europe, will be tasked with harnessing excellence-based research and innovation to support delivery of European security, sustainable prosperity and competitiveness. The global nature of the current programme allows likeminded countries with shared goals, such as the UK, to pool resources in support of scientific collaboration with our European partners and other associated countries to tackle pressing societal challenges and directly impact people’s lives.

The UK’s collaboration through EU Framework Programmes is rooted in longstanding ties of friendship, shared expertise, and common values. Scientists and innovators from the UK and EU are working together to tackle some of the most important issues facing our societies – from climate and the clean energy transition, to supporting our common economic security. We are clear that we want to strengthen these ties with our neighbours and allies and explore areas where we can boost our shared prosperity and security through mutually beneficial agreements.

Having recently associated to Horizon Europe, we want UK scientists, innovators, businesses, and institutions to closely collaborate with partners across Europe going forward and hope that the developing proposals for FP10 will support this. The UK is of course interested in potentially associating to FP10 assuming it is open and relevant to our research community. As such, we are keen to support discussions as FP10 shapes up.

• Guidance about who can bid for Horizon funding, how, and what support is available
• UK Association to Horizon Europe and Copernicus programmes: explainer

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New security protocol shields data from attackers during cloud-based computation

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Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.

This reliance on cloud computing poses significant security risks, particularly in areas like health care, where hospitals may be hesitant to use AI tools to analyze confidential patient data due to privacy concerns.

To tackle this pressing issue, MIT researchers have developed a security protocol that leverages the quantum properties of light to guarantee that data sent to and from a cloud server remain secure during deep-learning computations.

By encoding data into the laser light used in fiber optic communications systems, the protocol exploits the fundamental principles of quantum mechanics, making it impossible for attackers to copy or intercept the information without detection.

Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures.

“Deep learning models like GPT-4 have unprecedented capabilities but require massive computational resources. Our protocol enables users to harness these powerful models without compromising the privacy of their data or the proprietary nature of the models themselves,” says Kfir Sulimany, an MIT postdoc in the Research Laboratory for Electronics (RLE) and lead author of a paper on this security protocol .

Sulimany is joined on the paper by Sri Krishna Vadlamani, an MIT postdoc; Ryan Hamerly, a former postdoc now at NTT Research, Inc.; Prahlad Iyengar, an electrical engineering and computer science (EECS) graduate student; and senior author Dirk Englund, a professor in EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE. The research was recently presented at Annual Conference on Quantum Cryptography.

A two-way street for security in deep learning

The cloud-based computation scenario the researchers focused on involves two parties — a client that has confidential data, like medical images, and a central server that controls a deep learning model.

The client wants to use the deep-learning model to make a prediction, such as whether a patient has cancer based on medical images, without revealing information about the patient.

In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.

Also, the server does not want to reveal any parts of the proprietary model that a company like OpenAI spent years and millions of dollars building.

“Both parties have something they want to hide,” adds Vadlamani.

In digital computation, a bad actor could easily copy the data sent from the server or the client.

Quantum information, on the other hand, cannot be perfectly copied. The researchers leverage this property, known as the no-cloning principle, in their security protocol.

For the researchers’ protocol, the server encodes the weights of a deep neural network into an optical field using laser light.

A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.

The server transmits the network’s weights to the client, which implements operations to get a result based on their private data. The data remain shielded from the server.

At the same time, the security protocol allows the client to measure only one result, and it prevents the client from copying the weights because of the quantum nature of light.

Once the client feeds the first result into the next layer, the protocol is designed to cancel out the first layer so the client can’t learn anything else about the model.

“Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks,” Sulimany explains.

Due to the no-cloning theorem, the client unavoidably applies tiny errors to the model while measuring its result. When the server receives the residual light from the client, the server can measure these errors to determine if any information was leaked. Importantly, this residual light is proven to not reveal the client data.

A practical protocol

Modern telecommunications equipment typically relies on optical fibers to transfer information because of the need to support massive bandwidth over long distances. Because this equipment already incorporates optical lasers, the researchers can encode data into light for their security protocol without any special hardware.

When they tested their approach, the researchers found that it could guarantee security for server and client while enabling the deep neural network to achieve 96 percent accuracy.

The tiny bit of information about the model that leaks when the client performs operations amounts to less than 10 percent of what an adversary would need to recover any hidden information. Working in the other direction, a malicious server could only obtain about 1 percent of the information it would need to steal the client’s data.

“You can be guaranteed that it is secure in both ways — from the client to the server and from the server to the client,” Sulimany says.

“A few years ago, when we developed our demonstration of distributed machine learning inference between MIT’s main campus and MIT Lincoln Laboratory, it dawned on me that we could do something entirely new to provide physical-layer security, building on years of quantum cryptography work that had also been shown on that testbed ,” says Englund. “However, there were many deep theoretical challenges that had to be overcome to see if this prospect of privacy-guaranteed distributed machine learning could be realized. This didn’t become possible until Kfir joined our team, as Kfir uniquely understood the experimental as well as theory components to develop the unified framework underpinning this work.”

In the future, the researchers want to study how this protocol could be applied to a technique called federated learning, where multiple parties use their data to train a central deep-learning model. It could also be used in quantum operations, rather than the classical operations they studied for this work, which could provide advantages in both accuracy and security.

“This work combines in a clever and intriguing way techniques drawing from fields that do not usually meet, in particular, deep learning and quantum key distribution. By using methods from the latter, it adds a security layer to the former, while also allowing for what appears to be a realistic implementation. This can be interesting for preserving privacy in distributed architectures. I am looking forward to seeing how the protocol behaves under experimental imperfections and its practical realization,” says Eleni Diamanti, a CNRS research director at Sorbonne University in Paris, who was not involved with this work.

This work was supported, in part, by the Israeli Council for Higher Education and the Zuckerman STEM Leadership Program.

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