We tested whether a species of fish, unlikely to have experienced any evolutionary pressure for human facial recognition, could learn to discriminate human faces. We found that archerfish could be trained to discriminate a learned face from a large number of other human faces even when some trivial cues had been removed (i.e. brightness, colour and head-shape). While it is impossible to say from our study whether archerfish use the same visual information to discriminate the face images as humans, our results clearly show that some aspects of the facial recognition task can be learnt, even in the absence of a neocortex.

During testing in Experiments 1 and 2, all fish reached peak discrimination accuracy between 77–89%. Archerfish have previously been shown capable of discriminating large numbers of stimuli to a similar degree of accuracy (up to 93% accuracy)41,42. It seems likely that the archerfish did not use trivial features to discriminate the human faces as the fish could distinguish one face from 44 others which varied in similarity. In addition, when brightness, colour and general outline cues were standardized, the fish were still able to complete the task. Our results demonstrate that, like some species of reef fish43, archerfish are adept at fine-detail pattern discrimination and can apply these abilities to unfamiliar stimuli, including human faces.

During training, we observed individual variation in the number of sessions required to learn the task; while some fish learned within a single session (Experiment 1: Fish 3 and 4), others required longer periods of training (up to 17 sessions / 510 trials). The difference in learning rates may simply be due to individual factors such as experience and motivation. However, it is also possible that individuals used different visual information to discriminate the faces and that some features required more time to learn. If individuals do learn to use different visual information for discrimination, it may also explain why some fish achieved a higher degree of accuracy than others in the testing period. When it comes to visually identifying an object, not all visual information is created equal. For example, by learning the combined appearance of the eyes, nose and mouth of a particular human face, it is likely you will be able to easily identify that face from a large pool of other faces. However, learning the appearance of a single spot on the cheek is not likely to be as helpful.

During testing, we saw a similar pattern of behavior. Some fish were immediately highly accurate (Experiment 1: Fish 3 and 4; Experiment 2: Fish 5, 7 and 8), while others improved with experience (Experiment 1: Fish 1 and 2; Experiment 2: Fish 6). These differences in individual performance provide additional evidence that some of the fish were using different features for facial identification from the others and that this visual information differed in its effectiveness for the discrimination task. Future experiments testing which features the fish use to discriminate faces would help shed light on whether individual fish use different features and if these feature were similar to those used by human observers. There are several experimental methods that involve altering facial stimuli in some way (e.g.44,45,46,47) which have previously been used to explore feature use by primates44,45,46,47 and pigeons21 when discriminating human faces and these approaches may be adaptable for future studies with fish.

Understanding the recognition capabilities of different animals can inform us about the evolutionary history of human facial recognition. There are a wide range of animals that use visual cues for conspecific individual recognition including primates e.g.47,48, crayfish49, fiddler crabs50, sheep51,52, damselfish43 and wasps53. With so many examples across such diverse taxa, it is clear that the discrimination of individuals based on facial features is not unique to humans and suggests that perhaps human faces themselves are not a particularly special class of objects. Our evidence that archerfish can discriminate human faces without having any obvious selection pressure for this specific task, suggests that the visual system of distantly related vertebrates is capable of sophisticated discrimination tasks. This is not surprising as so many behaviours fundamental to the survival of a wide range of species rely on accurate vision-based object recognition, including predator detection, mate selection and feeding. Therefore it seems possible that pre-existing circuits for sophisticated visual discrimination evolved into the dedicated face-processing circuitry of primates.

In this experiment we tested discrimination of frontal views; this is a very restricted version of the task humans must perform in order to rapidly and accurately discriminate human faces in real situations. Faces are dynamic and their appearance can be drastically changed by a range of factors including variations in viewing angle, lighting, or facial expression. Unlike the faces of many other vertebrates, primate faces have complex musculature allowing them to form a broad range of facial expressions2. It is possible that the complexity of the neocortex is a requirement for the discrimination of faces under variable conditions. That said, there is evidence that pigeons are able to recognize faces that have changed in viewing angle20 and expression17. This has yet to be tested in animals such as fish that do not live near humans, however, many social animals that recognize conspecific individuals are equally capable of discriminating those individuals under a range of viewing conditions. Fish present an interesting example as they can use colour patterns for recognition which are additionally affected by changes in water quality and lighting. Because different wavelengths are attenuated unequally in water, some colours within a pattern are affected more than others. It is possible that the perceived complexity of human facial recognition may simply be an anthropogenic point of view and in fact other animals must also perform similarly complex pattern discrimination tasks under highly demanding conditions43.