Being able to recognize each other is crucial for social interactions in humans, as well as many other animals. To humans, faces are the most important body part to differentiate between one another. Humans read the face as a whole, rather than look at parts of the face, which is why it is harder to recognise a face when we see it upside-down, but not when we see an upside-down object.

Some other mammals also identify each other by the face and take longer to recognise an upside-down face, but this ability has never been observed in animals other than mammals. Previous research has shown that some fish species can distinguish between individuals. For example, female medaka fish prefer males they have seen before to ‘strangers’. However, until now, it was not known if they can recognize individual faces, nor how they distinguish a specific male from many others.

To see if medaka fish use vision, smell or both cues to recognise mates, Wang and Takeuchi familiarised the fish before the mating test in different settings. In the first group, the male and the female could see each other but were kept in different tanks; in the second group to test odour cues, the male and the female were in the same tank but could not see each other; in the third group, the fish were in the same tank and could see each other; the fish in the fourth group were kept in different tanks and could not see each other. To make sure the fish can recognise and distinguish between fish or objects, Wang and Takeuchi also performed negative conditioning experiments, in which the females had to learn to form an association between a negative stimulus and a specific situation.

Wang and Takeuchi found that medaka fish use both vision and smell to distinguish between other fish, but could recognise each other based on vision alone. More specifically, the fish looked at the faces to tell others apart, and even when spots were added to their faces, the fish could still recognise the other. The mekada fish were also able to discriminate between two fish and two objects, but failed the task when the fish images were presented upside-down. However, when two objects were inverted, they were still able to tell the difference. This suggests that just like humans, faces may be special for fish too.

This is the first study that shows the face inversion effect in animals other than mammals. A next step will be to compare the different mechanisms between species, and identify the underlying genes and nerve cells responsible for face recognition. This will enable us to better understand social interactions in fish, and enhance our knowledge of how our own ability to recognize faces has changed from an evolutionary point of view.

In a social group, the ability to recognise other individuals correctly is essential for maintaining various social interactions in animals, such as pair-bonding, hierarchy, inbreeding avoidance, and recognition of offspring, nest mates, or neighbours (Tibbetts and Dale, 2007; Wiley, 2013). For example, some territorial birds can remember specific neighbours for a long period of time (Godard, 1991), and king penguins can identify their chick from thousands of conspecifics (Aubin and Jouventin, 1998). Receivers associate different types of identity signals, such as odour, sound, tactile, motion, electric or morphological cues, with certain individuals (Sherman et al., 2009) and identify them afterwards when necessary. In addition to looking at how animals recognise conspecifics, their mental representations of specific individuals can also give hints that allow us to judge their cognitive abilities. For example, hamsters have various odours for different body parts, and an unfamiliar hamster will categorise them as multiple individuals, while a previously interacted hamster can associate the odours to the specific individual (Johnston and Bullock, 2001). Animals may have complicated mechanisms to link multiple identity signals to different types of fitness-related tasks, or may use simpler rules to remember an individual. Among all of the individual recognition (IR) systems, face recognition is one of the most specific abilities, and is reported in animals from a number of distinct evolutionary lineages (Kendrick and Baldwin, 1987; McKone et al., 2007; Van der Velden et al., 2008, Coulon et al., 2009; Racca et al., 2010; Sheehan and Tibbetts, 2011). How faces are recognised, and whether the processes involved differ from those used to perceive other objects, is a main topic of interest in the field of cognitive psychology and biology.

In humans and some other mammals, faces are specially processed in cognitive, developmental and functional ways (Calder, 2011). Human infants are hypothesised to be attracted to faces innately (Morton and Johnson, 1991), but also develop face recognition skills and specific brain regions for processing faces during childhood. A familiar face can be individuated in 250 ms (Jacques and Rossion, 2006), and we can possibly remember more faces than other visual stimuli with similar variations in details and features. Studies of a neuropsychological disorder known as prosopagnosia or face blindness, in which individuals are unable to recognise faces but have no difficulty in recognising individuals by other modalities (such as voice) or in discriminating non-face objects (Meadows, 1974; Behrmann et al., 2005), have shown that facial recognition proceeds through specific cognitive and neural pathways (Valentine, 1988; McKone et al., 2007). In addition, the increase in recognition difficulty associated with inversion of faces is greater than that for the inversion of other types of visual stimuli (Yin, 1969). The so-called face-inversion effect indirectly indicates that faces are perceived configurally rather than only by specific features (such as the eyes, nose, or mouth), and that once inverted, such a global configuration is difficult to match and passes through routes which are used for recognising other objects (Bartlett and Searcy, 1993; Haxby et al., 1999; Boutsen et al., 2006). Likewise, the Thatcher illusion found in both humans (Thompson, 1980) and monkeys (Adachi et al., 2009; Dahl et al., 2010), in which the eyes and mouth are inverted relative to the face, becomes difficult to detect when upside down, further demonstrating that configural perception is interrupted when orientation is inverted.

Some other animals, ranging from mammals, birds and fish to invertebrates, have also been reported to use faces for IR (Brown and Dooling, 1992; Kendrick et al., 1995; Bovet and Vauclair, 2000; Van der Velden et al., 2008, Kohda et al., 2015; Parr and Hecht, 2011; Tibbetts, 2002). Scientists have long argued that the face-specific processes are unique to humans or shared only by quite closely related species (Tate et al., 2006). However, such specialised ability may also have evolved in distinct animal taxa when selection force associated with complicated, repeated social interactions strongly favours IR.

The face inversion effect is the method most widely used in animals to test whether faces may be processed specifically, and researchers have identified this ability in some non-human primates (Overman and Doty, 1982; Tomonaga, 1994; Parr et al., 1998; Vermeire and Hamilton, 1998; Neiworth et al., 2007) and in sheep (Kendrick et al., 1996). Some monkeys failed to show such oriented-specific face-processing (Rosenfeld and Van Hoesen, 1979; Bruce, 1982; Dittrich, 1990; Parr et al., 1999; Weiss et al., 2001; Gothard et al., 2004), but many studies lacked the use of non-face signals as controls, making it difficult to interpret the results (Parr et al., 1999). Specialised neural systems for face recognition have been found in some non-human primates and in sheep (Kendrick and Baldwin, 1987; Kanwisher and Yovel, 2006; Tsao et al., 2006), providing great opportunities to interpret how these animals perceive faces perceptually and mechanically for comparative research. Other than the inversion effect, sheep, chimpanzees, and wasps exhibit better discrimination of conspecific faces than of non-face objects (Kendrick et al., 1996; Parr et al., 1998; Sheehan and Tibbetts, 2011). The difference between decision speed and accuracy in discriminating faces and non-facial stimuli is hypothesized to be due to face-specific perception (Sheehan and Tibbetts, 2011).

In the present study, we used a popular freshwater animal model, the medaka fish (Figure 1A), to test IR ability and to examine whether these animals perceive faces differently from non-face stimuli. Researchers have only recently found that fish can use facial pattern to individuate others. Manipulation using digital models demonstrated that two species of cichlid fish use facial patterns, but not body colouration, to recognise familiar individuals (Kohda et al., 2015; Satoh et al., 2016). A species of reef fish uses UV patterns on the face for species recognition, but there is no evidence of IR (Siebeck et al., 2010). Medaka are shoaling fish with diverse social behaviours that has become a popular model in genetic and neural research. Medaka females prefer males with larger body sizes (Howard et al., 1998) and longer fins (Fujimoto et al., 2014), or familiar males. Visual contact for 5 hr can shorten the time to mate for a pair of medaka, and a certain extrahypothalamic neuromodulatory system alters the preference in response to familiarity (Okuyama et al., 2014). Nonetheless, the cues used for medaka IR and the cognitive basis that underlies IR remain unknown. Here, we investigated the identity signals used for medaka IR, and whether the process of recognising other individuals differs from that for other objects. We propose that medaka can become a powerful model for understanding IR systems for many reasons. First, abundant closely related species with different social behaviours are available, allowing us to test the evolutionary background that promotes strict IR. Second, the social behaviours within the species are also variable. Medaka from different geographic regions or different inbred strains behave uniquely (Tsuboko et al., 2014), allowing us to investigate how ecological factors influence the use of identity signals, as well as the mechanisms behind these signals. Moreover, rich genetic techniques such as genome editing and epigenetic methods are available for medaka (Kirchmaier et al., 2015), providing powerful tools with which to solve complex questions.

Figure 1

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The first aim of this study was to identify the cues used for medaka IR. We tested whether visual and odour cues are part of the identity signals, and whether the cues work collaboratively. We also investigated which visual components (such as appearance, motion and different body parts) are necessary for IR, as well as the extent to which the signals can be manipulated (extra pattern added or image inverted) without affecting IR. The second aim was to test whether the mechanism of face recognition differs from that for non-face objects using the classic face-inversion paradigm and the accuracy of discriminating faces and non-face objects. We used both ecologically realistic settings (mating test) and a conditioned test (electric shock two-alternative forced-choice [TAFC] design) to assess strict IR in medaka. Understanding the cues that animals use to recognise others, as well as their cognitive basis, can help us to elucidate how animals connect to each other in their social world.

We demonstrate here that medaka fish are able to perform strict IR in both an ecologically relevant paradigm (mating test) and a conditioning setting (electric shock test). IR is a complex form of recognition and may require strong evolutionary force. For example, nesting penguins use simpler parameters for parent–chick recognition than do non-nesting species (Jouventin and Aubin, 2002). Without nest-site information, non-nesting penguins may face higher selection pressure for specific IR ability. Wild medaka are frequently observed in high-density groups (more than hundreds in one pond, Wang and Takeuchi, personal observation) and without obvious, constant nest site or territory. Also, medaka spawn every day and appear to have complex social interactions such as courtship (Walter and Hamilton, 1970), mate-guarding behaviour (Weir et al., 2011; Yokoi et al., 2015), dynamic group-forming and social learning (Ochiai et al., 2013). Such frequent and repeated social interactions may induce strict IR (Tibbetts, 2004) and sophisticated cognitive/neural adaptation. Medaka and related species provide an excellent model for investigating how the identity signals and recognition ability has evolved, and how animals link multiple identity signals to different social interactions. They are also widely used as genetic and developmental models for social interaction; for example, TN-GnRH3 neurons function as a gateway for activating mate preferences (Okuyama et al., 2014), but we do not yet know whether these neurons regulate sensory perception or the decision-making process after signals are perceived. Here, we tested how medaka fish link identity signals to mating partner or conditioned punishment, both of which are rarely described in animal IR literature. Even though medaka are not monogamous, they still have an astonishing ability to recognise mates, suggesting that IR in mating system may be more common than previously thought.

Medaka can successfully differentiate individuals using visual cues alone. More specifically, they use signals around the face for IR. Few animals other than mammals use faces to discriminate individuals, and these species are considered to use relatively simple mechanisms to encode facial features. Two species of cichlid fish use the face to recognise shoal mates or mating partners, and when the facial patterns are exchanged with those of other individuals using digital models, the recognition was found to be based on facial features alone (Kohda et al., 2015; Satoh et al., 2016). A species of wasp uses a number of facial spots to rank dominance, and this ranking can be artificially altered by adding extra patterns (Tibbetts and Dale, 2004). In our study, even after spots were painted onto the faces of the male medaka, the females still treated the male as a familiar mate. This suggests that medaka are able to tolerant some level of local change during IR. More interestingly, medaka showed the classical face-inversion effect, with fish taking a longer time or failing to recognise the inverted faces, but not the inverted non-face objects. To the best of our knowledge, this is the first study to explore the face-inversion effect in animals other than mammals. The inversion effect is indirect evidence for configural/holistic face processing and has been found in humans (Maurer et al., 2002), chimpanzees (Parr et al., 1998) and sheep (Kendrick et al., 1996). These animals not only perceive faces by using internal features, but also make use of configural cues which combine the sum of a number of parts (Diamond and Carey, 1986; Peirce et al., 2000; Tomonaga, 2007; McKone and Yovel, 2009). When the faces are upside-down, this configural recognition is impaired, and discrimination times and accuracy deteriorate. The configural recognition process is generally unique to faces and does not appear in other stimuli, although a few special cases have been reported (Diamond and Carey, 1986). In addition, specialised neural systems are found to encode faces in humans and some other mammals. In humans, inverted faces delay the neural correlates of faces and increase the activity in object-processing areas (Aguirre et al., 1999; Haxby et al., 1999). We do not know whether medaka have specific face processing pathways, but regardless of whether medaka sharecommon mechanisms with mammals, these fish can be an important comparative model. Dorsal parts of the telencephalon (pallium) in teleost fish are hypothesised to be related to the mammalian cerebral cortex, including the hippocampus and the pallial amygdala (O'Connell and Hofmann, 2012), and thus could be a possible candidate brain region for face-recognition processing.

It is worth noting that the face inversion effect is not direct evidence for holistic processing (Valentine, 1988). Further experiments such as the composite task (Young et al., 1987), the part-whole task (Tanaka and Farah, 1993) and the part-in-spacing-changed-whole task (Tanaka and Sengco, 1997) are necessary to test whether the animal uses holistic cues to process faces. But at least, we show the possibility that the mechanism for detecting faces may be different from that for other stimuli. Other hypotheses should also take into consideration, such as the within-class discrimination (Damasio et al., 1982) or the expertise hypotheses (Diamond and Carey, 1986). The within-class discrimination hypothesis proposes that the special property of faces is due to individual-level discrimination within one type of stimuli, which is a relatively difficult task. For example, discriminating between individual dogs is more difficult than discriminating a dog from a set of mammals such as cats, sheep and monkeys. Although the hypothesis is mostly rejected in humans, it is still possible that it applies to other animals. Another ongoing debate is that humans are face experts and generally use facial stimuli more often than other objects, so the face-specific mechanism is actually expertise-specific. Here, we tested a pair of familiar non-face objects to which the fish had been exposed constantly since they had hatched, in order to control the familiarity level with fish faces. The medaka had a similar level of accuracy when discriminating between familiar objects or medaka faces in the upright orientation, which shows that the familiar objects are sufficient to control for the familiarity and task difficulty in the inversion experiment. The accuracy of discrimination was significantly lower for non-familiar objects compared to that for faces, but we do not know whether the difference was due to task difficulty or familiarity level. In humans, our ability to match unfamiliar faces is surprisingly low. More studies are necessary to understand how familiarity level influences medaka IR, and under which circumstances they can perform IR. One study demonstrated that medaka failed to discriminate fish from their own strain under monochromatic light, whereas they showed strong preference for same-strain mates under normal lighting conditions (Utagawa et al., 2016). The males from both strains were familiar to the females, so colours may be important for identifying between strains. We do not know whether medaka show the face inversion effect for IR under monochromatic light as do humans and monkeys (Dittrich, 1990; Yovel and Duchaine, 2006; McKone and Yovel, 2009).

When being successfully recognised by others is favoured by selection, distinctive traits among individuals may evolve to increase the possibility of being identified (Tibbetts and Dale, 2007). Even though medaka may distinguish each other with visual cues, we cannot find obvious visual traits that vary substantially between individuals. One possible explanation for this is that medaka have eight types of cone opsins, and maximum wavelength absorbance ranging from 356 nm to 562 nm (Matsumoto et al., 2006), whereas human vision has just three types of opsins with absorbance ranging from 430 nm to 560 nm. Although difficult to detect for human eyes, there is some level of individual difference in reflectance spectra from medaka bodies (Figure 1B) and craniofacial morphology (Kimura et al., 2007). On the other hand, even individuals in a group are not especially easy to discriminate; when the signal receivers can benefit from successful individuation, the ability for IR can be favoured by selection (Johnstone, 1997; Dale et al., 2001). Specific identification abilities, such as face recognition, may also evolve under strong selection pressure. Another possible explanation is that medaka do not have to link many individuals with fitness-related tasks, or do not have to remember the individual for long time, which may decrease the resources necessary for IR. Distinguishing faces from other species can also be difficult. For example, sheep can discriminate sheep faces with only 5–10% differences (Tate et al., 2006), which is difficult for non-experienced humans. Thus, arguing that medaka lack individual-level variation from human’s point of view may be inappropriate. Future research should look at whether medaka can link one or more individuals to multiple ecological tasks, and at whether they can connect identity signals to other fitness-related information such as mate quality or health condition. Rich inbreed lines that are available in medaka could also be useful tools for investigating the heritability of identity signal recognition. For example, in human twin studies, face discrimination ability is heritable for upright faces, but not for inverted faces or other objects (Mckone and Coltheart, 2010).

In the mating test, females accepted familiar males after 3 hr of separation, but not after 24 hr. Even after 24 hr, however, females were able to discriminate between males in the electric shock experiment. One possibility is that the females were able to remember the males, but chose not to accept them. This demonstrates that the mate preferences of medaka females are influenced by familiarity level, but with some flexibility. Another explanation is that repeated conditioning strengthens the memory formation, but as yet there is no evidence related to learning ability in either setting. Some other fish species also prefer familiar mates (Korner et al., 1999; Sogabe, 2011; Boyle and Tricas, 2014), and the preference for familiar individuals has been reported to be formed after 4 min (Dugatkin and Alfieri, 1991) or 12 days (Griffiths and Magurran, 1997) and can last for 2 months (Brown and Smith, 1994), depending on the ecological necessity. In medaka, mate-guarding behaviour by males before and after mating has been reported (Weir et al., 2011; Yokoi et al., 2015) and dominant males are able to remain close to the females, which is a possible explanation for such an ephemeral female preference. Medaka females can spawn daily; thus, flexibility in mate preference can ensure mating with the strongest male at the time. In addition, many types of parasitic mating are reported in medaka (Koya et al., 2013), which could facilitate strict IR for recognising the correct mate. Although humans and chimpanzees can differentiate faces immediately (Parr et al., 2000), other animal models generally require a longer period for conditioning identity signals to reward or punishment. For example, sheep require at least 30 to 40 trials to condition an unfamiliar face to food reward (Kendrick et al., 1996). With similar numbers of trials, medaka are able to condition one individual with electric shock punishment. It should be taken into consideration that almost all animal IR experiments are linked with an ecologically relevant task (e.g. mating in this study) or a conditioning test (either positive or negative conditioning). Other unavoidable factors such as the physiological condition or stress level of the animals can influence recognition. For example, mating is a sensitive and bipolar procedure which is influenced by the condition and interaction of both individuals. Multiple paradigms with suitable controls may be useful to assess the evidence of convergent IR in animals.

Overall, our data suggest that medaka can perform strict IR and that, as in humans, specific visual features such as the face may be more important for IR than others. Medaka also show the classic face-inversion effect, which could indicate that specific processes are involved in recognising faces. It is likely that the mechanism underlying medaka face recognition differs from that in mammals. The application of the rich genetic toolset available for this species, which includes genome editing tools (such as CRISPR/Cas9) and epigenetic methods, will allow more detailed investigation of the specialised cognitive abilities (Ansai and Kinoshita, 2014; Nakamura et al., 2014). Even a brain as small as that of the medaka is able to manage such a complex cognitive task. A understanding of how faces are perceptually encoded in simpler models will provide concepts that may be exploited both for the development of new machine face-recognition systems and to explain how the brain processes highly homogenous social information in general. The advantages and limitations of this model compared to mammalian models in face recognition will allow interesting future investigations of convergent systems from phylogenetically distant groups. Other than looking to provide a comparative view on the neurobiology of faces, our future direction will focus on IR in the real world. For example, we would like to study how medaka link individuals to multiple ecological-related topics and how IR shapes their societies and group forming (Wang et al., 2015). The evidence gathered in such studies will indicate the evolutionary background in which such sophisticated cognitive process were formed, which is important for all social animals.

1. 1. Adachi I
   2. Chou DP
   3. Hampton RR

   (2009) Thatcher effect in monkeys demonstrates conservation of face perception across primates

   Current Biology 19:1270–1273.

   https://doi.org/10.1016/j.cub.2009.05.067

   • PubMed
   • Google Scholar
2. 1. Aguirre GK
   2. Singh R
   3. D'Esposito M

   (1999) Stimulus inversion and the responses of face and object-sensitive cortical areas

   NeuroReport 10:189–194.

   https://doi.org/10.1097/00001756-199901180-00036

   • PubMed
   • Google Scholar
3. 1. Ansai S
   2. Kinoshita M

   (2014) Targeted mutagenesis using CRISPR/Cas system in medaka

   Biology Open 3:362–371.

   https://doi.org/10.1242/bio.20148177

   • PubMed
   • Google Scholar
4. 1. Aubin T
   2. Jouventin P

   (1998) Cocktail-party effect in king penguin colonies

   Proceedings of the Royal Society B: Biological Sciences 265:1665–1673.

   https://doi.org/10.1098/rspb.1998.0486

   • Google Scholar
5. 1. Bartlett JC
   2. Searcy J

   (1993) Inversion and configuration of faces

   Cognitive Psychology 25:281–316.

   https://doi.org/10.1006/cogp.1993.1007

   • PubMed
   • Google Scholar
6. 1. Behrmann M
   2. Avidan G
   3. Marotta JJ
   4. Kimchi R

   (2005) Detailed exploration of face-related processing in congenital prosopagnosia: 1. behavioral findings

   Journal of Cognitive Neuroscience 17:1130–1149.

   https://doi.org/10.1162/0898929054475154

   • PubMed
   • Google Scholar
7. 1. Blank M
   2. Guerim LD
   3. Cordeiro RF
   4. Vianna MR

   (2009) A one-trial inhibitory avoidance task to zebrafish: rapid acquisition of an NMDA-dependent long-term memory

   Neurobiology of Learning and Memory 92:529–534.

   https://doi.org/10.1016/j.nlm.2009.07.001

   • PubMed
   • Google Scholar
8. 1. Boutsen L
   2. Humphreys GW
   3. Praamstra P
   4. Warbrick T

   (2006) Comparing neural correlates of configural processing in faces and objects: an ERP study of the Thatcher illusion

   NeuroImage 32:352–367.

   https://doi.org/10.1016/j.neuroimage.2006.03.023

   • PubMed
   • Google Scholar
9. 1. Bovet D
   2. Vauclair J

   (2000) Picture recognition in animals and humans

   Behavioural Brain Research 109:143–165.

   https://doi.org/10.1016/S0166-4328(00)00146-7

   • PubMed
   • Google Scholar
10. 1. Boyle KS
    2. Tricas TC

    (2014) Discrimination of mates and intruders: visual and olfactory cues for a monogamous territorial coral reef butterflyfish

    Animal Behaviour 92:33–43.

    https://doi.org/10.1016/j.anbehav.2014.03.022

    • Google Scholar
11. 1. Brown SD
    2. Dooling RJ

    (1992) Perception of conspecific faces by budgerigars (Melopsittacus undulatus): I. Natural faces

    Journal of Comparative Psychology 106:203–216.

    https://doi.org/10.1037/0735-7036.106.3.203

    • PubMed
    • Google Scholar
12. 1. Brown GE
    2. Smith RJ

    (1994) Fathead minnows use chemical cues to discriminate natural shoalmates from unfamiliar conspecifics

    Journal of Chemical Ecology 20:3051–3061.

    https://doi.org/10.1007/BF02033710

    • PubMed
    • Google Scholar
13. 1. Bruce C

    (1982) Face recognition by monkeys: absence of an inversion effect

    Neuropsychologia 20:515–521.

    https://doi.org/10.1016/0028-3932(82)90025-2

    • PubMed
    • Google Scholar
14. Book

    1. Calder A

    (2011)

    Oxford Handbook of Face Perception

    Oxford University Press.

    • Google Scholar
15. 1. Coulon M
    2. Deputte BL
    3. Heyman Y
    4. Baudoin C

    (2009) Individual recognition in domestic cattle (Bos taurus): evidence from 2D-images of heads from different breeds

    PLoS One 4:e4441.

    https://doi.org/10.1371/journal.pone.0004441

    • PubMed
    • Google Scholar
16. 1. Dahl CD
    2. Logothetis NK
    3. Bülthoff HH
    4. Wallraven C

    (2010) The Thatcher illusion in humans and monkeys

    Proceedings of the Royal Society B: Biological Sciences 277:2973–2981.

    https://doi.org/10.1098/rspb.2010.0438

    • PubMed
    • Google Scholar
17. 1. Dale J
    2. Lank DB
    3. Reeve HK

    (2001) Signaling individual identity versus quality: a model and case studies with ruffs, queleas, and house finches

    The American Naturalist 158:75–86.

    https://doi.org/10.1086/320861

    • PubMed
    • Google Scholar
18. 1. Damasio AR
    2. Damasio H
    3. Van Hoesen GW

    (1982) Prosopagnosia: anatomic basis and behavioral mechanisms

    Neurology 32:331-341.

    https://doi.org/10.1212/WNL.32.4.331

    • PubMed
    • Google Scholar
19. 1. Diamond R
    2. Carey S

    (1986) Why faces are and are not special: an effect of expertise

    Journal of Experimental Psychology: General 115:107–117.

    https://doi.org/10.1037/0096-3445.115.2.107

    • PubMed
    • Google Scholar
20. 1. Dittrich W

    (1990) Representation of faces in Longtailed Macaques (Macaca fascicularis)

    Ethology 85:265–278.

    https://doi.org/10.1111/j.1439-0310.1990.tb00406.x

    • Google Scholar
21. 1. Dugatkin L
    2. Alfieri M

    (1991) Guppies and the TIT FOR TAT strategy: preference based on past interaction

    Behavioral Ecology and Sociobiology 28:243–246.

    https://doi.org/10.1007/BF00175096

    • Google Scholar
22. 1. Fujimoto S
    2. Kawajiri M
    3. Kitano J
    4. Yamahira K

    (2014) Female mate preference for longer fins in medaka

    Zoological Science 31:703–708.

    https://doi.org/10.2108/zs140102

    • PubMed
    • Google Scholar
23. 1. Godard R

    (1991) Long-term memory of individual neighbours in a migratory songbird

    Nature 350:228–229.

    https://doi.org/10.1038/350228a0

    • Google Scholar
24. 1. Gothard KM
    2. Erickson CA
    3. Amaral DG

    (2004) How do rhesus monkeys (Macaca mulatta) scan faces in a visual paired comparison task?

    Animal Cognition 7:25–36.

    https://doi.org/10.1007/s10071-003-0179-6

    • PubMed
    • Google Scholar
25. 1. Griffiths SW
    2. Magurran AE

    (1997) Familiarity in schooling fish: how long does it take to acquire?

    Animal Behaviour 53:945–949.

    https://doi.org/10.1006/anbe.1996.0315

    • Google Scholar
26. 1. Haxby JV
    2. Ungerleider LG
    3. Clark VP
    4. Schouten JL
    5. Hoffman EA
    6. Martin A

    (1999) The effect of face inversion on activity in human neural systems for face and object perception

    Neuron 22:189–199.

    https://doi.org/10.1016/S0896-6273(00)80690-X

    • PubMed
    • Google Scholar
27. 1. Howard RD
    2. Martens RS
    3. Innis SA
    4. Drnevich JM
    5. Hale J

    (1998) Mate choice and mate competition influence male body size in japanese medaka

    Animal Behaviour 55:1151–1163.

    https://doi.org/10.1006/anbe.1997.0682

    • PubMed
    • Google Scholar
28. 1. Jacques C
    2. Rossion B

    (2006) The speed of individual face categorization

    Psychological Science 17:485–492.

    https://doi.org/10.1111/j.1467-9280.2006.01733.x

    • PubMed
    • Google Scholar
29. 1. Johnston RE
    2. Bullock TA

    (2001) Individual recognition by use of odours in golden hamsters: the nature of individual representations

    Animal Behaviour 61:545–557.

    https://doi.org/10.1006/anbe.2000.1637

    • Google Scholar
30. 1. Johnstone RA

    (1997) Recognition and the evolution of distinctive signatures: when does it pay to reveal identity?

    Proceedings of the Royal Society B: Biological Sciences 264:1547–1553.

    https://doi.org/10.1098/rspb.1997.0215

    • Google Scholar
31. 1. Jouventin P
    2. Aubin T

    (2002) Acoustic systems are adapted to breeding ecologies: individual recognition in nesting penguins

    Animal Behaviour 64:747–757.

    https://doi.org/10.1006/anbe.2002.4002

    • Google Scholar
32. 1. Kanwisher N
    2. Yovel G

    (2006) The fusiform face area: a cortical region specialized for the perception of faces

    Philosophical Transactions of the Royal Society B: Biological Sciences 361:2109–2128.

    https://doi.org/10.1098/rstb.2006.1934

    • PubMed
    • Google Scholar
33. 1. Kendrick KM
    2. Baldwin BA

    (1987) Cells in temporal cortex of conscious sheep can respond preferentially to the sight of faces

    Science 236:448–450.

    https://doi.org/10.1126/science.3563521

    • PubMed
    • Google Scholar
34. 1. Kendrick KM
    2. Atkins K
    3. Hinton MR
    4. Broad KD
    5. Fabre-Nys C
    6. Keverne B

    (1995) Facial and vocal discrimination in sheep

    Animal Behaviour 49:1665–1676.

    https://doi.org/10.1016/0003-3472(95)90088-8

    • Google Scholar
35. 1. Kendrick KM
    2. Atkins K
    3. Hinton MR
    4. Heavens P
    5. Keverne B

    (1996) Are faces special for sheep? evidence from facial and object discrimination learning tests showing effects of inversion and social familiarity

    Behavioural Processes 38:19–35.

    https://doi.org/10.1016/0376-6357(96)00006-X

    • PubMed
    • Google Scholar
36. 1. Kimura T
    2. Shimada A
    3. Sakai N
    4. Mitani H
    5. Naruse K
    6. Takeda H
    7. Inoko H
    8. Tamiya G
    9. Shinya M

    (2007) Genetic analysis of craniofacial traits in the medaka

    Genetics 177:2379–2388.

    https://doi.org/10.1534/genetics.106.068460

    • PubMed
    • Google Scholar
37. 1. Kirchmaier S
    2. Naruse K
    3. Wittbrodt J
    4. Loosli F

    (2015) The genomic and genetic toolbox of the teleost medaka (Oryzias latipes)

    Genetics 199:905–918.

    https://doi.org/10.1534/genetics.114.173849

    • PubMed
    • Google Scholar
38. 1. Kohda M
    2. Jordan LA
    3. Hotta T
    4. Kosaka N
    5. Karino K
    6. Tanaka H
    7. Taniyama M
    8. Takeyama T

    (2015) Facial recognition in a Group-Living Cichlid fish

    PLoS One 10:e0142552.

    https://doi.org/10.1371/journal.pone.0142552

    • PubMed
    • Google Scholar
39. 1. Korner K
    2. Schlupp I
    3. Lutjens O
    4. Parzefall J

    (1999) The role of experience in mating preferences of the unisexual Amazon molly

    Behaviour 136:257–268.

    https://doi.org/10.1163/156853999501315

    • Google Scholar
40. 1. Koya Y
    2. Koike Y
    3. Onchi R
    4. Munehara H

    (2013) Two patterns of parasitic male mating behaviors and their reproductive success in japanese medaka, Oryzias latipes

    Zoological Science 30:76–82.

    https://doi.org/10.2108/zsj.30.76

    • PubMed
    • Google Scholar
41. 1. Matsumoto Y
    2. Fukamachi S
    3. Mitani H
    4. Kawamura S

    (2006) Functional characterization of visual opsin repertoire in Medaka (Oryzias latipes)

    Gene 371:268–278.

    https://doi.org/10.1016/j.gene.2005.12.005

    • PubMed
    • Google Scholar
42. 1. Maurer D
    2. Grand RL
    3. Mondloch CJ

    (2002) The many faces of configural processing

    Trends in Cognitive Sciences 6:255–260.

    https://doi.org/10.1016/S1364-6613(02)01903-4

    • PubMed
    • Google Scholar
43. 1. McKone E
    2. Kanwisher N
    3. Duchaine BC

    (2007) Can generic expertise explain special processing for faces?

    Trends in Cognitive Sciences 11:8–15.

    https://doi.org/10.1016/j.tics.2006.11.002

    • PubMed
    • Google Scholar
44. 1. McKone E
    2. Yovel G

    (2009) Why does picture-plane inversion sometimes dissociate perception of features and spacing in faces, and sometimes not? toward a new theory of holistic processing

    Psychonomic Bulletin & Review 16:778–797.

    https://doi.org/10.3758/PBR.16.5.778

    • PubMed
    • Google Scholar
45. 1. Mckone E
    2. Coltheart V

    (2010)

    Tutorials in Visual Cognition

    261–303, Face and object recognition: how do they differ?, Tutorials in Visual Cognition.

    • Google Scholar
46. 1. Meadows JC

    (1974) The anatomical basis of prosopagnosia

    Journal of Neurology, Neurosurgery & Psychiatry 37:489–501.

    https://doi.org/10.1136/jnnp.37.5.489

    • PubMed
    • Google Scholar
47. 1. Morton J
    2. Johnson MH

    (1991) CONSPEC and CONLERN: a two-process theory of infant face recognition

    Psychological Review 98:164.

    https://doi.org/10.1037/0033-295X.98.2.164

    • PubMed
    • Google Scholar
48. 1. Nakamura R
    2. Tsukahara T
    3. Qu W
    4. Ichikawa K
    5. Otsuka T
    6. Ogoshi K
    7. Saito TL
    8. Matsushima K
    9. Sugano S
    10. Hashimoto S
    11. Suzuki Y
    12. Morishita S
    13. Takeda H

    (2014) Large hypomethylated domains serve as strong repressive machinery for key developmental genes in vertebrates

    Development 141:2568–2580.

    https://doi.org/10.1242/dev.108548

    • PubMed
    • Google Scholar
49. 1. Neiworth JJ
    2. Hassett JM
    3. Sylvester CJ

    (2007) Face processing in humans and new world monkeys: the influence of experiential and ecological factors

    Animal Cognition 10:125–134.

    https://doi.org/10.1007/s10071-006-0045-4

    • PubMed
    • Google Scholar
50. 1. O'Connell LA
    2. Hofmann HA

    (2012) Evolution of a vertebrate social decision-making network

    Science 336:1154–1157.

    https://doi.org/10.1126/science.1218889

    • PubMed
    • Google Scholar
51. 1. Ochiai T
    2. Suehiro Y
    3. Nishinari K
    4. Kubo T
    5. Takeuchi H

    (2013) A new data-mining method to search for behavioral properties that induce alignment and their involvement in social learning in medaka fish (Oryzias latipes)

    PLoS One 8:e71685.

    https://doi.org/10.1371/journal.pone.0071685

    • PubMed
    • Google Scholar
52. 1. Okuyama T
    2. Yokoi S
    3. Abe H
    4. Isoe Y
    5. Suehiro Y
    6. Imada H
    7. Tanaka M
    8. Kawasaki T
    9. Yuba S
    10. Taniguchi Y
    11. Kamei Y
    12. Okubo K
    13. Shimada A
    14. Naruse K
    15. Takeda H
    16. Oka Y
    17. Kubo T
    18. Takeuchi H

    (2014) A neural mechanism underlying mating preferences for familiar individuals in medaka fish

    Science 343:91–94.

    https://doi.org/10.1126/science.1244724

    • PubMed
    • Google Scholar
53. 1. Overman WH
    2. Doty RW

    (1982) Hemispheric specialization displayed by man but not macaques for analysis of faces

    Neuropsychologia 20:113–128.

    https://doi.org/10.1016/0028-3932(82)90002-1

    • PubMed
    • Google Scholar
54. 1. Parr LA
    2. Dove T
    3. Hopkins WD

    (1998) Why faces may be special: evidence of the inversion effect in chimpanzees

    Journal of Cognitive Neuroscience 10:615–622.

    https://doi.org/10.1162/089892998563013

    • PubMed
    • Google Scholar
55. 1. Parr LA
    2. Winslow JT
    3. Hopkins WD

    (1999) Is the inversion effect in rhesus monkeys face-specific?

    Animal Cognition 2:123–129.

    https://doi.org/10.1007/s100710050032

    • Google Scholar
56. 1. Parr LA
    2. Winslow JT
    3. Hopkins WD
    4. de Waal FB
    5. Waal, f DE

    (2000) Recognizing facial cues: individual discrimination by chimpanzees (Pan troglodytes) and rhesus monkeys (Macaca mulatta)

    Journal of Comparative Psychology 114:47–60.

    https://doi.org/10.1037/0735-7036.114.1.47

    • PubMed
    • Google Scholar
57. 1. Parr LA
    2. Hecht EE

    (2011)

    The Oxford Handbook of Face Perception

    Face perception in non-human primates, The Oxford Handbook of Face Perception, United States, Oxford University Press.

    • Google Scholar
58. 1. Peirce JW
    2. Leigh AE
    3. Kendrick KM

    (2000) Configurational coding, familiarity and the right hemisphere advantage for face recognition in sheep

    Neuropsychologia 38:475–483.

    https://doi.org/10.1016/S0028-3932(99)00088-3

    • PubMed
    • Google Scholar
59. 1. Racca A
    2. Amadei E
    3. Ligout S
    4. Guo K
    5. Meints K
    6. Mills D

    (2010) Discrimination of human and dog faces and inversion responses in domestic dogs (Canis familiaris)

    Animal Cognition 13:525–533.

    https://doi.org/10.1007/s10071-009-0303-3

    • PubMed
    • Google Scholar
60. 1. Rosenfeld SA
    2. Van Hoesen GW

    (1979) Face recognition in the rhesus monkey

    Neuropsychologia 17:503–509.

    https://doi.org/10.1016/0028-3932(79)90057-5

    • PubMed
    • Google Scholar
61. 1. Satoh S
    2. Tanaka H
    3. Kohda M

    (2016) Facial recognition in a Discus fish (Cichlidae): experimental approach using digital models

    PLoS One 11:e0154543.

    https://doi.org/10.1371/journal.pone.0154543

    • PubMed
    • Google Scholar
62. 1. Sheehan MJ
    2. Tibbetts EA

    (2011) Specialized face learning is associated with individual recognition in paper wasps

    Science 334:1272–1275.

    https://doi.org/10.1126/science.1211334

    • PubMed
    • Google Scholar
63. 1. Sherman P
    2. Reeve H
    3. Pfennig D

    (2009)

    Behavioural Ecology: An Evolutionary Approach

    Recognition systems, Behavioural Ecology: An Evolutionary Approach, John Wiley & Sons.

    • Google Scholar
64. 1. Siebeck UE
    2. Parker AN
    3. Sprenger D
    4. Mäthger LM
    5. Wallis G

    (2010) A species of reef fish that uses ultraviolet patterns for covert face recognition

    Current Biology 20:407–410.

    https://doi.org/10.1016/j.cub.2009.12.047

    • PubMed
    • Google Scholar
65. 1. Sogabe A

    (2011) Partner recognition in a perennially monogamous pipefish, Corythoichthys haematopterus

    Journal of Ethology 29:191–196.

    https://doi.org/10.1007/s10164-010-0236-y

    • Google Scholar
66. 1. Tanaka JW
    2. Farah MJ

    (1993) Parts and wholes in face recognition

    The Quarterly Journal of Experimental Psychology Section A 46:225–245.

    https://doi.org/10.1080/14640749308401045

    • PubMed
    • Google Scholar
67. 1. Tanaka JW
    2. Sengco JA

    (1997) Features and their configuration in face recognition

    Memory & Cognition 25:583–592.

    https://doi.org/10.3758/BF03211301

    • PubMed
    • Google Scholar
68. 1. Tate AJ
    2. Fischer H
    3. Leigh AE
    4. Kendrick KM

    (2006) Behavioural and neurophysiological evidence for face identity and face emotion processing in animals

    Philosophical Transactions of the Royal Society B: Biological Sciences 361:2155–2172.

    https://doi.org/10.1098/rstb.2006.1937

    • PubMed
    • Google Scholar
69. 1. Thompson P

    (1980) Margaret Thatcher: a new illusion

    Perception 9:483–484.

    https://doi.org/10.1068/p090483

    • PubMed
    • Google Scholar
70. 1. Tibbetts EA

    (2002) Visual signals of individual identity in the wasp Polistes fuscatus

    Proceedings of the Royal Society B: Biological Sciences 269:1423–1428.

    https://doi.org/10.1098/rspb.2002.2031

    • PubMed
    • Google Scholar
71. 1. Tibbetts EA

    (2004) Complex social behaviour can select for variability in visual features: a case study in Polistes wasps

    Proceedings of the Royal Society B: Biological Sciences 271:1955–1960.

    https://doi.org/10.1098/rspb.2004.2784

    • PubMed
    • Google Scholar
72. 1. Tibbetts EA
    2. Dale J

    (2004) A socially enforced signal of quality in a paper wasp

    Nature 432:218–222.

    https://doi.org/10.1038/nature02949

    • PubMed
    • Google Scholar
73. 1. Tibbetts EA
    2. Dale J

    (2007) Individual recognition: it is good to be different

    Trends in Ecology & Evolution 22:529–537.

    https://doi.org/10.1016/j.tree.2007.09.001

    • PubMed
    • Google Scholar
74. 1. Tomonaga M

    (1994) How laboratory-raised japanese monkeys (Macaca fuscata) perceive rotated photographs of monkeys: evidence for an inversion effect in face perception

    Primates 35:155–165.

    https://doi.org/10.1007/BF02382051

    • Google Scholar
75. 1. Tomonaga M

    (2007) Visual search for orientation of faces by a chimpanzee (Pan troglodytes): face-specific upright superiority and the role of facial configural properties

    Primates 48:1–12.

    https://doi.org/10.1007/s10329-006-0011-4

    • PubMed
    • Google Scholar
76. 1. Tsao DY
    2. Freiwald WA
    3. Tootell RB
    4. Livingstone MS

    (2006) A cortical region consisting entirely of face-selective cells

    Science 311:670–674.

    https://doi.org/10.1126/science.1119983

    • PubMed
    • Google Scholar
77. 1. Tsuboko S
    2. Kimura T
    3. Shinya M
    4. Suehiro Y
    5. Okuyama T
    6. Shimada A
    7. Takeda H
    8. Naruse K
    9. Kubo T
    10. Takeuchi H

    (2014) Genetic control of startle behavior in medaka fish

    PLoS One 9:e112527.

    https://doi.org/10.1371/journal.pone.0112527

    • PubMed
    • Google Scholar
78. 1. Utagawa U
    2. Higashi S
    3. Kamei Y
    4. Fukamachi S

    (2016) Characterization of assortative mating in medaka: mate discrimination cues and factors that Bias sexual preference

    Hormones and Behavior 84:9–17.

    https://doi.org/10.1016/j.yhbeh.2016.05.022

    • PubMed
    • Google Scholar
79. 1. Valentine T

    (1988) Upside-down faces: a review of the effect of inversion upon face recognition

    British Journal of Psychology 79:471–491.

    https://doi.org/10.1111/j.2044-8295.1988.tb02747.x

    • PubMed
    • Google Scholar
80. 1. Van der Velden J
    2. Zheng Y
    3. Patullo BW
    4. Macmillan DL

    (2008) Crayfish recognize the faces of fight opponents

    PLoS One 3:e1695.

    https://doi.org/10.1371/journal.pone.0001695

    • PubMed
    • Google Scholar
81. 1. Vermeire BA
    2. Hamilton CR

    (1998) Inversion effect for faces in split-brain monkeys

    Neuropsychologia 36:1003–1014.

    https://doi.org/10.1016/S0028-3932(98)00054-2

    • PubMed
    • Google Scholar
82. 1. Walter RO
    2. Hamilton JB

    (1970) Head-up movements as an Indicator of sexual unreceptivity in female medaka, Oryzias latipes

    Animal Behaviour 18:125–127.

    https://doi.org/10.1016/0003-3472(70)90079-5

    • Google Scholar
83. 1. Wang M-Y
    2. Brennan CH
    3. Lachlan RF
    4. Chittka L

    (2015) Speed–accuracy trade-offs and individually consistent decision making by individuals and dyads of zebrafish in a colour discrimination task

    Animal Behaviour 103:277–283.

    https://doi.org/10.1016/j.anbehav.2015.01.022

    • Google Scholar
84. 1. Weir LK
    2. Grant JW
    3. Hutchings JA

    (2011) The influence of operational sex ratio on the intensity of competition for mates

    The American Naturalist 177:167–176.

    https://doi.org/10.1086/657918

    • PubMed
    • Google Scholar
85. 1. Weiss DJ
    2. Kralik JD
    3. Hauser MD

    (2001) Face processing in cotton-top tamarins (Saguinus oedipus)

    Animal Cognition 3:191–205.

    https://doi.org/10.1007/s100710000076

    • Google Scholar
86. 1. Wiley RH

    (2013) Specificity and multiplicity in the recognition of individuals: implications for the evolution of social behaviour

    Biological Reviews 88:179–195.

    https://doi.org/10.1111/j.1469-185X.2012.00246.x

    • PubMed
    • Google Scholar
87. 1. Yin RK

    (1969) Looking at upside-down faces

    Journal of Experimental Psychology 81:141–145.

    https://doi.org/10.1037/h0027474

    • Google Scholar
88. 1. Yokoi S
    2. Okuyama T
    3. Kamei Y
    4. Naruse K
    5. Taniguchi Y
    6. Ansai S
    7. Kinoshita M
    8. Young LJ
    9. Takemori N
    10. Kubo T
    11. Takeuchi H

    (2015) An essential role of the arginine vasotocin system in mate-guarding behaviors in triadic relationships of medaka fish (Oryzias latipes)

    PLOS Genetics 11:e1005009.

    https://doi.org/10.1371/journal.pgen.1005009

    • PubMed
    • Google Scholar
89. 1. Young AW
    2. Hellawell D
    3. Hay DC

    (1987) Configurational information in face perception

    Perception 16:747–759.

    https://doi.org/10.1068/p160747

    • PubMed
    • Google Scholar
90. 1. Yovel G
    2. Duchaine B

    (2006) Specialized face perception mechanisms extract both part and spacing information: evidence from developmental prosopagnosia

    Journal of Cognitive Neuroscience 18:580–593.

    https://doi.org/10.1162/jocn.2006.18.4.580

    • PubMed
    • Google Scholar

Author details

1. Mu-Yun Wang

   1. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
   2. The Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan

   Contribution

   M-YW, Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   muyunwang@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-9533-995X

2. Hideaki Takeuchi

   1. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
   2. The Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan

   Contribution

   HT, Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Investigation, Project administration, Writing—review and editing

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-6610-3895

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