Some members of my family probably still don’t believe that, but well: you can now make livng out of studying animal behavior. There is a non-zero chance that you will get funding for your research on social behavior of mice, song development of songbirds or decision making in sponges.
But before you will pack your instruments and go to Australia to study neurobiology of koala’s sleep, please take a while to recall the harsh history of our trade. Józef Piłsudski, one of the fathers of Polish independence and a grandfather of its fall, said once that the nation that forgets about its history is doomed. And the history of our field is full of misery.
Take Wallace Craig as an example. If your knowledge of ethology is limited to some textbook cliches, you might think that the first person who ever attemted to study instincts was a friendly – looking Nazi that liked to be followed by geese. This is obviously not true; and Craig was one of the persons who came before. He was a student of Charles Otis Williams, another important guy that you most probably don’t know. Craig started his career as a zoologist, yet soon observed that his manual skills were too deficient for a work that required a lot of manual operations. He proceeded to study behavior of pigeons in it’s entirety – from sexual behavior to vocalizations.
He authored a famous paper in which he made an important distinction between apetitive and consumative behavior and criticized tha claim according to which instinctive behaviors are composed of series of chain reflexs. His theories were an important influence for Lorenz.
But his career was a harsh one. At some point he was unable to get a funding for his research; he had to devise a strategy that would allow him to satisfy his research interests and not to die of hunger. Idea was simply – animals whose behavior is intreresting are sometimes also edible. As he wrote to his friend:
“We must keep hens; while I watch their behavior we can eat their eggs, and later we can put the specimens themselves in the pot. I must keep large pigeons as well as doves; we can eat the squabs.”
If you ever played Stardew Valley during your PhD, you know that farming and research are very hard to combine. After a short time Craig wrote again:
“Probably I could maintain a bird farm and make it pay, but it would take every minute of my time […] and I am, after all, more in need of time than I am of money.”
William Craig to C. Adams (Burkhardt, 2005)
He was very unlucky. At some point his main occupations were teaching and studying pigeon vocalizations. Both of them turned out to be impossible to conduct after he started to become deaf. He left the University of Maine in 1922 and died in obscurity in 1954.
His work was immortalized by its incorporation in Lorenz’s theory. What is interesting, Craig himself managed to immortalize an object of his study, a passenger pigeon. This bird, whose population is estimated to reach billions of specimens (historical accounts depict flocks of passenger pigeons so big that their flight over the town was taking a week) got extinct in 1914; the reason being an excessive hunting that should be most accurately described as a massacre. Craig was studying its calls and produced a note transcription of a pigeon’s vocalizations that you might use to recreate the voice of something that do not exist anymore, a feeling similar to one you might have during listening to those reconstructions of ancient greek music that are so unpleasant to listen.
Well, it seems that we are now in a much better position, right? No need to eat your rats; grant agencies will be happy to ensure your survival while you spend your precious time doing research. You are no longer a part of obscure community; neuroscience needs you.
You can now pack your neuropixels, openEphys boxes, hard drives full of open software and go to study your koalas in the wild. Or can you?
Well, it is definitely worth it. You will probably join Wallace Craig and the growing group of scientists whose object of studies became extinct. But in one hundred years your recording of koala’s brain activity might be for next generation just like the notes above: a proof that something you know only from drawings or museum specimens was someday alive and behaving.
Burghardt, G. M., & Burkhardt, R. W. (2018). Wallace Craig’s Appetites and Aversions as Constituents of Instincts: A Centennial appreciation. Journal of Comparative Psychology, 132(4), 361–372. https://doi.org/10.1037/com0000155
Burkhardt, R. W., Jr. (2005). Patterns of behavior: Konrad Lorenz, Niko Tinbergen, and the founding of ethology. Chicago, IL: University of Chicago Press.
Some stories we tell about the world are just untrue.
Others, much more dangerous ones, are untrue, but well-told. They resonate with our intuitions and preconceptions, and they tend to stick with us like mischievous ghosts.
We do all we can to get rid of them. We ritually distance ourselves from them in the introductions to our papers and books, just to blindly fall prey to them a few paragraphs later. They hide in the language – in metaphors, analogies, and cliche phrases that we use without much thinking.
Let’s take one of the most common ghosts of this type in biology: scala naturae, an ancient concept, according to which all beings can be arranged in a hierarchical structure. God is at the top, of course; then come angels, then humans. Animals (non-human animals, as we would say today) are one level lower, themselves arranged in a hierarchy: lions higher than goats, goats higher than fishes, that are above insects and sea invertebrates, and snakes – this sneaky, malicious creature that cheated first humans – at the bottom. And lower still plants, with minerals at the absolute bottom.
The development of the theory of evolution in the XIX century (and later changes in our view of life) has put this concept to rest*. The tree of life is no longer hierarchical: it’s circular, and all beings are arranged at the top branches. There are no higher beings, nor lower ones. Everybody knows that, it’s a textbook truism: scala naturae is dead.
Only it isn’t, as Paul Katz observed recently. Every time you see someone mentioning a gene or a mechanism that is conserved from flies to humans – you see a well-hidden example of a scala naturae ghost lurking from behind the words. Lower and higher animals can be still spotted in scientific papers. But it also survives in our theorizing about brain evolution.
As you might have noticed from my previous notes, I’m preoccupied with the topic of instinct. My focus of interest changes with time, though: years ago I was interested in the neural implementations of instinctive behaviours; then I moved to various critiques of the concept itself. Now, as I realized recently, I am mostly interested in why this concept is so sticky, and despite being shown to be empty, it is still haunting papers, books and folk wisdom about behaviour in neuroscience.
What I would like to blame is exactly our scala naturae view of nature, and deeply rooted preconceptions about the progressive nature of evolution in general, and the evolution of learning and behavioural plasticity in particular. In this series of posts, I hope to show you how this concept lays at the core of our thinking about instincts and innate behaviour, and how it makes us blind to facts discovered multiple times – and how we can replace it.
And the main message of my posts will be: there is no hierarchy of behaviours, going from primitive and instinctive to complex, learned, and flexible, that hierarchically evolved one after another. Rather, learning is an intrinsic property of nervous systems that were there from the beginning, and instincts – or, as I prefer to call them, robust behaviours – are rather a specific exception from the rule, an exception that can be achieved in multiple ways, and not – as it is widely believed – by hardwiring those behaviours in the brain.
In this first post, I will set the stage for the deconstruction – I will characterize the old view, discuss its assumption and show that it is indeed still influencing the way we think about behaviour.
Scala naturae, brain and behaviour – the hierarchical view of brain evolution
There is probably a no better example of scala naturae thinking about the brain and behaviour than Paul MacLean’s triune concept of brain evolution. As you all probably know, MacLean divided the vertebrate brain into three parts. At the bottom, we can find so-called reptilian brain (Biblical snake winks) – a set of structures responsible for governing basic behaviours necessary for survival – mating, foraging, fleeing, and fighting. Those behaviours are supposed to be ones that evolved early, and – as they are crucial – are supposed to be instinctive and hardwired. Then, on the top of the reptilian brain there lies the mammalian brain, a set of structures that introduced feelings to mammalian successors of emotionless, automatic reptiles – feelings related to parental care and social behaviours, for example. At the top of the tops resides the neocortex, that equipped higher mammals with the ability to learn a complex task, think and develop language in the human branch of evolution.
[…] most frameworks of brain organization are heavily centred on the cortex. These descriptions view ‘newer’ cortex as controlling subcortical regions, which are assumed to be (relatively) unchanged throughout evolution.
Pessoa, Medina and Desfilis, 2021
Ghost of MacLean’s theory is also easy to spot in many recent studies in psychology and neuroscience: in studies on social behaviors, attention, personality, decision making and many more.
MacLean’s theory assumes not only the existence of the hierarchy of brain structures. According to this view, we can create also a hierarchy of behaviours – from evolutionary primitive ones – ones that are crucial for survival, e.g. fighting or feeding, and which are hardwired into lower parts of the brain, through more complex ones (e.g. social behaviors or maternal care) that may be partially learned, up to complex behaviours of humans – the use of language, for example.
This view is so much uncontested that it is sometimes barely perceptible, but you can easily find it in papers especially clearly in the moments of surprise when people’s findings do not align well with this view. Here’s an example of a study that discovered experience-related plasticity in the ventrolateral subdivision of the hypothalamus (VMHvl), a region engaged in behaviors such as fighting and mate recognition. As the authors write:
More generally, [these observations] reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a “hard-wired” system.
Remedios and Kennedy, 2017
Another great example is a recent paper on social behaviours; and even the title is telling – Neural circuits of social behaviors: Innate yet flexible. The abstract starts with a claim:
Social behaviors, such as mating, fighting, and parenting, are fundamental for survival of any vertebrate species. All members of a species express social behaviors in a stereotypical and species-specific way without training because of developmentally hardwired neural circuits dedicated to these behaviors.
Wei, Talwar and Lin, 2021
Interestingly, the next sentence is Despite being innate, social behaviors are flexible. We will come back to this interesting contradiction in the next post of this series, for now, it is just important to notice this automatic assumption that behaviours crucial to survival must be innate, hardwired in lower brain regions.
MacLean’s theory focuses on the vertebrate brain. But the hierarchical view of evolution is evident in thinking about the brains of animals in general. There is often an assumption expressed directly or indirectly by many scholars that learned and flexible behaviours are the domain of higher, or more complex animals. Up to the seventies (and the seminal 1976 study of William Quinn, who has show learning ability in Drosophila) insects were viewed as unable to learn; Seymour Benzer was known to display during a lecture a photo from an article in the Washington Post with a face of the fly and a caption Can’t learn anything.
Insects have rather a small number of constituent neurons of the central nervous system (CNS) [..] and eventually display rather simple patterned movements; a so-called ‘instinctive behavior’, which principally does occur without memory and learning
Yet another twenty years later, with an overwhelming amount of data that shows the richness of learned and flexible behaviours in invertebrates, we can encounter claims in a similar spirit (2019!):
Indeed, most of the behavioral repertoire of insects and other short-lived animals is innate.
(Strangely, the author forgot to give us a single reference to support this claim!)
Of course, every single neuroscientist asked directly about this scala naturae view of brain and behaviour would not admit adherence to it – and yet it still haunts us in the words and metaphors that are used in textbooks and papers.
Let me create a strawman – I will call it the hierarchical view of brain evolution. My strawman combines different aspects of scala naturae-based viewing of the evolution of brain and behaviour as a gradual increase of complexity, which started with simple, hardwired behaviours and then, with an emergence of more complex nervous systems, allowed for learned and flexible behaviours. There are four claims that I would like to ascribe to this view:
Learned and plastic behaviours are costly and require complex neural structures, and thus appeared only later in evolutionary history
Behaviours of “evolutionary primitive” animals are largely innate, and genetically determined.
“Ancient” behaviours” crucial for survival retain their innate nature even in more “complex” animals
This hierarchy is reflected in the brain architecture
As I said, there is probably not a single scientist who would – when asked – admit to holding all those views. But in my opinion, it well captures an idealized summary of ideas permeating our thinking. And it is hard to get rid of, as it adheres nicely to our deeply-held belief about the nature of nature – but doesn’t able us to reshape our thinking about the brain and behaviour, so that it may fit much better to the knowledge we gathered during last few decades. My aim in this post series is to prove that all those points are not viable – this will be the deconstructive part of my story. But I would also like – constructively – to propose to you another view if evolution, that I call the parallel view of behavioural evolution. The claims of this view are:
Plasticity is an intrinsic property of the nervous system
Learning and other forms of plasticity are ubiquitous, even in the case of “instinctive” behaviours
Behaviours can be canalized, and canalization can be achieved in many different ways
Learning is not more costly than canalization (at least not by default)
Environment is a reliable source of information
In the next post, I will debunk the first three claims of the hierarchical view. Then I will proceed to the last one (especially interesting); just to finish with my attempt at developing the aforementioned parallel view, describing its experimental predictions and implications for our study of behaviour. Stay tuned!
I am very bad at remembering and recognizing melodies. It doesn’t hurt too much: I am so amusical that I rarely notice this deficiency. It was a much bigger problem, though, when I was a child – and it’s because I was a birdwatching child.
Recognizing birds by their songs was a dramatically hard thing for me. The mnemotechnic that I found the most useful was learning folk transcriptions of melodies. Take a yellowhammer as an example – song of this yellow beauty was traditionally transcribed in Polish as nie będzie suchej kobyle niiic (in a loose translation – the skinny marewill be fiiine). In English, the same song is transcribed as little bit of bread and no cheeese . The absolute absurdity of a bird comforting an owner of an underfed horse was bold enough to make me remember this song well – I can now recognize a yellowhammer’s song wherever I go.
But this song motif is not exactly the same everywhere – yellowhammers have their dialects. Czech scientists initiated even a citizen science project – you can record a yellowhammer in your neighbourhood and send them your recording. Authors use this data to make a map of dialects – you can find the interactive version here; click on a coloured point to hear the dialect.
Songs of birds can also differ in time. Yellowhammers in my hometown might sing a song that is slightly different from the one that my grandmother could hear in her childhood – this phenomenon of cultural evolution was observed in savannah sparrows living on an island. They were recorded for 30 years; it turned out that their song changed – they added some new elements and modified others.
Both phenomena – dialects and cultural evolution – are caused by the fact that yellowhammers and savannah sparrows, just like 4000 other species of songbirds, learn their song from other individuals, more or less like we learn our language. And learning, as you may know, is never perfect; some notes might not be copied properly by some birds and, in effect, a song will slowly change in time and space, as some birds migrate.
What is interesting, though, is that even though yellowhammers have different dialects and their song may change in time, it is anyway always clearly recognizable as a yellowhammer song. Birdsong, though learned, is very often surprisingly stable and universal.
Its robustness was shown in many studies. If you isolate a young songbird from its peers, making it unable to learn the song, it will usually develop a chaotic, noisy isolate song. But there is an order in this chaos – isolate song has some characteristics of a species-typical song – e.g. song duration, numbers of notes in a song and the duration of intervals between notes.
Much more spectacular proof of the stability of birdsong is a study done in 2009 by Olga Feher and others. They isolated young zebrafinches and let them develop an isolate song. After it was developed, they used those isolated singers as tutors for the next generation of birds. Afterwards, zebrafinches from the second generation became tutors for the next generation. And well, results were astonishing – birds that learned isolate songs were making it more species-typical in every generation; and after three generations of learning the song was pretty similar to the normal song of a zebrafinch. It happened even though none of those birds had ever heard a normal zebrafinch song!
In my last post I wrote that we should call such stable behaviors robust behaviors and avoid terms like instinctive or innate. This time I would like to show what are mechanisms that ensure the robustness and stability of such behaviors.
In a search for the template
As I wrote previously, whenever we face an aspect of animal behavior that seems to be independent of learning, universal and developmentally stable, people usually assume that it has to be somehow hardwired in the brain, with a scheme for this wiring being encoded in the genome. Aforementioned stable aspects of birdsong are no exception; as Douglas Nelson and Peter Marler wrote in their paper on species universals in birdsong:
“[Our model] postulates extensive pre-encoding of information about species-specific song structure, embodied in innately-specified brain circuitry.”
Marler, P., & Nelson, D. (1992)
Authors mention innately-specified brain circuitry for an obvious reason: as we all know, the brain controls behavior; if genes encode some aspects of behavior, they must do it by encoding how neurons connect with each other. Genes influence behavior by their direct or indirect influence on the brain development, neuron properties and wiring – that is how the story goes. As another example of this conviction let me cite Kevin Mitchell about human behavior in his book Innate:
“Somehow, in the molecules of DNA in a fertilized egg from any of these species is a code or program of development that will produce an organism with its species-typical nature. Most importantly, that entails the specification of how the brain develops in such a way that wires in these behavioral tendencies and capacities. Human nature, thus defined, is encoded in our genomes and wired into our brains in just the same way”.
Kevin Mitchell, Innate: How the Wiring of Our Brains Shapes Who We Are
And it is obviously true. Genes can have an influence on a bird’s brain to make some of its parameters universal and stable in many ways.
First of all, genes may influence the properties of neurons that take part in song generation. By changing e.g. expression or structure of different ion channels you can easily obtain neurons that might spike with a different frequency. A good example is the case of fruit flies’ courtship song. Two species of Drosophila – Drosophila simulans and Drosophila mauritiana – produce courtship song (whichis produced by wing vibrations) that differs in carrier frequency by 9.7 Hz. It turns out that this difference can be explained by an intronic retroelement present in the gene coding for slowpoke calcium-activated potassium channel in D. simulans, which in some way changes properties of neurons that are involved in song generation.
In the case of birds, by influencing properties of neurons that take part in the generation of a song, genes may limit the scope of songs that are possible for a bird to sing. But they can also influence the properties of neurons that process sensory information – cells in auditory areas might be so tuned that they respond preferentially to species – specific sound frequencies. In this way a bird would have a limited scope of songs that it can learn from a tutor. Birds prefer to learn songs of their own species. Even if they learn a song of another species, they will make it sound similar to what they would normally sing. Some authors postulated the existence of innate perceptual filters, that would just filter out songs of other species. You can make an auditory region of the brain in a way that will respond much more strongly to your species song – and it seems to be the case.
In those – and many other ways – genes, by influencing properties of the nervous system, channel the development of a song to ensure it’s stability. But can we say that a yellowhammer’s song is really encoded in those genes? As I want to show you, we probably cannot, and it’s because there is much more to a song generation and learning than just the brain.
Bird has a body
If you were not born on another planet, or – just like poor birds in the aforementioned experiments – were not kept in isolation, you must have seen the picture of Darwin’s finches’ beaks. Yes, they differ, and thanks to their differences birds can occupy different niches in a harsh environment of the Galapagos islands. But what is important in our case is that the size of the beak influences also the bird’s song.
Birds actively change their beak gape rapidly during singing in a way dependent on the frequency of a song, increasing the gape when the song’s frequency is higher and vice versa. The function of those rapid adjustments is filtering – the beak acts as an acoustic filter and the change of its gape allows it to serve its filtering function over a wide range of song frequencies. If you add an extra mass to a canary’s beak, limiting bird’s ability to move its beak rapidly and precisely, the result will be a song of lower tonal purity.
As you might expect, having a naturally heavier beaks should also influence the song – and it does; as Jeffrey Podos has shown, Darwin finches with larger beaks produce songs with lower repetition rates of syllables and narrower frequency bandwidth than birds with smaller beaks. The same is true for neotropical woodcreepers; in blue cardinalids a length of a beak is negatively correlated with a note rate.
A bird’s song repertoire may thus me restrained by the size and shape of its beak. But the beak is not the only morphological trait that may constrain a song of a bird.
A bird’s vocal organ is the syrinx. It is located near the base of the trachea; the sound is generated when airflow causes vibratory tissues – membranes or labia – to vibrate. Morphology of syrinx differs between different groups of birds. It is especially elaborate in songbirds – their syrinx is a duplex voice organ, with two pairs of labia in each bronchus that can be controlled independently.
Sound can be shaped by muscles that change the tension of the tissues in the syrinx. It seems, though, that the properties of the song depend only on the commands received from the neuromuscular system. Some authors suggest that vibrating membranes or labia in bird’s syrinx may act like coupled nonlinear oscillators, producing a range of phenomena typical for such systems, such as sudden frequency jumps. Carel ten Cate and Gabriel Beckers claimed that song of doves from genus Streptopelia exhibits acoustic phenomena that are caused by such nonlinear dynamics intrinsic to their syrinx and may not be a result of direct muscle control. What is interesting, they speculate that inter-species differences in vocalizations may be caused by minor changes in e.g. the morphology of the syrinx.
One of the key features of nonlinear systems is that small and gradual changes in control parameters can cause large, sudden, and qualitative changes in dynamics. If true in bird song, the intrinsic dynamics of the sound production organ itself would provide a source of major and qualitative acoustic variation. […] Seemingly strong interspecific differences, e.g. between the tonal coos of S. risoria and the noisy ones of S. orientalis or S. tranquebarica, might thus have resulted from minor changes in underlying mechanisms of vocalization, without the need for large changes in syringeal structure or control mechanisms.
Beckers, G. J. ., & Cate, C. (2006)
A similar phenomenon was observed in case of calls of Xenopus frogs. These calls frogs are produced by the larynx built of two apposed discs. Fast separation of those discs excites the larynx and the tissues around, resulting in the generation of harmonic frequencies. In a Xenopus male mating call, each sound pulse is composed of two frequency bands produced simultaneously, called dyads. One band, called DF2, has a higher dominant frequency and the other – DF1 – lower. DF2/DF1 frequency ratio is different in different Xenopus clades and conserved within a clade. What is interesting, the properties of those dyads are intrinsic to the larynx and not dependent on the neural control: you can obtain dyads identical to natural ones by stimulating isolated larynges. As authors write:
Both species-specific individual DFs and the clade-specific dyad ratio are thus intrinsic to the larynx rather than the result of laryngeal or respiratory muscle modulation by neural circuitry. Which, as yet unidentified, characteristics of laryngeal tissue geometry and properties result in species-specific DFs and their ratios remain to be determined, but are likely to reflect a common tuning mechanism in descendants of ancestral Xenopus species
Kelley, D. B., Hall, I. C., Tobias, M. L., Elias, D. O., Kwong-Brown, U., & Elemans, C. P. (2019)
It might be thus possible that the aspects of a bird’s song that we label as innate may stem not from the way its brain is hardwired, but from the way its syrinx is shaped.
There are more ways in which the bird’s body can shape its song. The most obvious one is the body size – small birds are unable to produce songs of a very low frequency, as the production of such sounds requires a bigger sound generating mechanism and is more costly energetically. Small birds also have smaller lungs and, as vocal production and respiration are coupled, they are not able to sing longer sounds without a break.
Many aspects of a bird’s song that are labelled as innate in isolation studies may not in fact stem from hardwired templates, but be a result of the way the bird’s body is built. But there are also other factors – outside of a bird’s body – that may ensure the stability of the song in a natural environment.
Bird has an environment
As I mentioned, there are perceptual filters that help birds to learn only their species-typical song. But it can be said that there are also filters that are external to the animal – its environment can also act as a filter. It is because different environments carry – or degrade – different sounds differently.
In forests, for example, sounds can be reflected off canopy or echoed off the trunks of trees. Such reverberations are especially degrading sounds with rapid frequency modulations, while pure whistles are usually much less influenced by them. Environmental factors, such as temperature and humidity, also influence sound transmission – humid environments may enhance sound transmission, and temperature gradients (e.g. when air nearby the ground is warmer) may lead to distortions of sound transmission (e.g sound shadows). Position of the animal also matters – the transmission low-frequency sounds is disrupted when a bird is positioned closer to the ground due to ground interference.
The environment in which a bird is living will thus to some extent filter sounds that arrive at its ears, again limiting the set of songs that it may learn and later produce. The effect of such environmental filtering might be strengthened by a bird’s ‘hardwired‘ preference to learn song less degraded by the environment, as was shown by Stephen Nowicki in swamp sparrow. I would speculate that birds might also be able to hear how song elements produced during the learning phase are transmitted. They may then choose the elements that e.g. do not produce echoes.
Social interactions may also shape the song more directly. In many songbird species males initially produce many different songs and retain only some of them in their adult life, discarding the rest. One criterion for inclusion of a song element might be how well it matches the song of a tutor that was memorized by a bird. But it is only a part of the story – birds construct their song repertoire also during interactions with other birds, males and females.
In brown-headed cowbirds, male selects song elements that it will include in his final repertoire based on the reaction of a female. Females produce subtle signs – wing strokes and beak gapes – when a male is singing; males retain those elements of their repertoire that elicited the response of a female and discard those that did not.
In the case of male field sparrows, it is other males’ behavior that matters. When young birds settle on new territory, they sing a few different song types. They engage in vocal interactions with other males around and, after some time, retain only the song that is most similar to the male that is most active in their neighbourhood. Similarly, song sparrows initially overproduce song types, only to end up with a repertoire of songs that are also sung by many other males around.
As a result of this bias, songs sung by birds in a given area might become more uniform; songs that differ too much from the most popular type are discarded. It is yet another mechanism that may prevent bird’s song from diverging too much from a species-typical pattern.
I think that we tend to view some parts of the natural world as somehow fluid and amorphous. Every time we are confronted with a natural phenomenon that is stable across generations, universal or developmentally robust, we tend to assume that it has to be encoded in some stable structure. This way of thinking might be traced back at least to the famous Schrödinger’s What is life. As Lenny Moss writes on the Schrödinger vision:
Schrödinger begins with a naive notion (but perhaps justifiable for his time) of the cell as a disorganized bag of atoms and argues his way to the need for a solid-state “aperiodic crystal” to serve as that bedrock of order, continuity, and heroic resistance to entropy which makes life possible.
What Genes can’t do, Lenny Moss
This bedrock of order is now usually thought to be DNA, but the way of thinking stays the same. In the case of animal behavior, this bedrock can be hardwired neural circuits. As I wrote above, people who try to explain the stability of bird’s song assume that somewhere in the brain there must be a template, some pre-encoded information that will ensure the development of species-typical song even in the face of developmental perturbances.
And it is to some extent true – it is a truism to say that genes influence the way brain is wired during the development, and depending on the variant of an ion channel you can have neurons that can produce patterns that constrain the range of possible songs that a bird can produce.
But – what I wanted to show you – it is not the whole story. The bird’s song repertoire is also constrained by the way a bird’s body is built. A small bird will not sing a low-frequency song – just because it’s physically impossible. But the shape of the song will be also influenced by its vocal organ morphology and the size of its beak. The songs bird will learn are, on the other hand, constrained by the environment that transmits different sounds better or worse. And even if bird memorized songs that are different from the species-specific canon, interactions with other birds – males and females – will help to prevent any attempt to be original. There may be no single template, hardwired in the brain, but the whole set of filtering mechanisms*, internal or external to the bird, that ensures the stability of the song.
Well, you can say, everything can be anyway traced back togenes, as genes determine the way body is built, the preferences of female, the tendency of a bird to match other bird’s song and it’s preference for a given type of an environment that it will inhabit. It’s just that there genes’ influence on behavior cannot be limited to the genes coding for the wiring of brain parts responsible for song learning and generation.
And if its the message that you will get from why this text, it’s good enough. What are the implications? If you are looking for genes related to different behaviors, you should not limit your search to genes that are directly related to how the brain is wired during development; you may find that there are genes beyond that set that also influence behavior. It may be especially important in studies that aim to find genes that make behavior of two closely related species differ. As in the aforementioned Drosophila study, the reason might be a difference in a gene encoding an ion channel, but in some cases one might find genes related to body shape or organ morphology that are responsible for the difference.
Instead of putting genes as a bedrock of order, we can assume that order, stability and robustness of behaviors can be ensured by interactions of many factors, internal and external to an animal, that recur usually in every generation and that are reliably present in a life of every individual. If one of them fails – for example, when a bird doesn’t have enough tutoring from a conspecific at a young age – others may help it develop a trait properly. A bird can, for example, develop different song types by improvisation and then select only those that are close enough to those sung by other males in a neighbourhood.
A good metaphor for this way of thinking might be tensegrity structures, in which the stability is achieved not by means of building stable support elements, like columns or pillars, but by balancing the counteracting forces of elements with continuous tension and discontinuous compression. There is no bedrock of order in tensegrity structures, just the dynamic interaction of elements that results in a stable structure.
That’s how I view the causes of stability of many animal behaviors. Animal, with its body and environment, can be – at least to some extent – viewed as a system, and only by looking at different parts of this system we can fully understand the genesis of behavior. There is more to it than just genes and brain. There is no single place where the yellowhammer’s song is encoded; the information necessary to ask for a little bit of bread and no cheeese is distributed.
* I really doubt that they can be really named mechanisms, but I did not find a better name for them.
The concept of instinct lays exactly in the center of my scientific interests.
It lays here largely because I was naively assuming that is dead for quite a long time – and is there a bigger pleasure than a dissection of dead ideas? Instinct was declared dead many times and for many reasons – that it lacks a clear definition or doesn’t explain anything. But it always reappears, this way or another, just like a monster from a horror movie. And it happened again, this time in debates on AI.
As you may know, AI is achieving amazing things nowadays – it composes some mediocre pseudo-Baroque music, probably outperforms pigeons in detecting breast cancer and is paving a way towards antyutopian, totalitarian hell where everybody is tracked and controlled, just like in some Orwell’s books that I loved when I was a teenager, before I have fortunately grown up*.
AI achieves all these wonders by learning. You show it thousands of pics, train for a few days, emitting more CO2 than Baltic republics in a year, and it will learn how to recognize a potato. The efficiency of those algorithms has led some people to believe that is just enough to let algorithms learn to achieve the Artificial Intelligence at some point. This hope – or threat – is being criticised with the use of none other, but the instinct, the the zombie of concepts, the undead nightmare of behavioral sciences.
The charge is simple – as Antonhy Zador Anthony Zador wrote recently, there are many things that animals do not need to learn. Examples follow:
A squirrel can jump from tree to tree within months of birth, a colt can walk within hours, and spiders are born ready to hunt. Examples like these suggest that the challenge may exceed the capacities of even the cleverest unsupervised algorithms.
His point is clear – we probably should add some innate constraints to our algorithms to achieve the level of intelligence of a mouse, for example, or just to make them more efficient. Animals are not blank slates, maybe neither should our artificial intelligencies. If it is right or wrong I cannot tell, for the last time I used the neural network the result was the rear part of the mouse got recognized as its head.
But what is interesting for me, a simple-minded behavioral scientist, are terms used in the paper. To be fair, Zador does not use the term instinct. What he does is to mention behaviors that are innate. And as innate he will assume behaviors that are created by innate mechanisms that are encoded in te genome; behaviors that are not learned (colt’s walking); behaviors that are present from birth (spider’s hunting), behaviors that develop without tutoring and that are species-specific (making different burrows different Peromyscus species). All those behaviors are described in ethological literature as instinctive – and, as you will see, this richness of meanings will turn out to be problematic.
A Twitter debate that happened a propos a similar discussion on AI by Gary Marcus and Yoshua Bengio was actually also a debate about the instincts and the role of genes, experiences and learning in the forming of animal behavior. Yes, nature – nurture debate is back, my dears. The existence of instincts is now an argument for developing our algorithms in a specific way.
We understand the concept of instinct almost instinctively, and we usually just assume that it is clear and well defined. But what does it really mean that the behavior is instinctive? Does it really have a clear meaning? And should we use it in our scientific debates, either on animal behavior or AI?
The concept of instinct – in this or another form – is very ancient. I am not a historian of ideas, so I will not naively try to trace its beginnings – but let me just say that the modern concept of instinct was formulated in the XX century by Konrad Lorenz. In his 1932 paper, Methods of Identification of Species-Specific Drive Activities in Birds, he says that the behavior is instinctive if it satisfies one or more of five criteria: 1) if it appears in an animal reared in isolation, without any tutoring, 2) if it is performed in a stereotyped way by all individuals of a specie, 3) if there is a striking mismatch between the typical intellectual abilities of an animal and the abilities that it wouldhave to posses to solve a given problem by insight (e.g. relatively stupid bees are able to show their companions where flowers are by dancing), 4) if the behavior can be elicited in an innapropriate context, suggesting that is not performed consciously, 5) if the behavior is performed in a stereotypical way even in context that is dfferent from the one in which it originally evolved. Niko Tinbergen, who together with Lorenz whole field of ethology, described instinct also as highly stereotyped, coordinated movements, the neuromotor apparatus of which belongs […] to the hereditary constitution of the animal.
Yes, we have seen those different meanings of instinct in Zador’s paper and in the Twitter discussion. And Lorenz’ and Tinbergen’s definitions are not the only ones. Patrick Bateson in his critique of Steven Pinker’s book on innateness lists additional meanings:
“Apart from its colloquial uses, the term instinct has at least nine scientific meanings: present at birth (or at a particular stage of development), not learned, developed before it can be used, unchanged once developed, shared by all members of the species (or at least of the same sex and age), organized into a distinct behavioral system (such as foraging), served by a distinct neural module, adapted during evolution, and differences among individuals that are due to their possession of different gene”
I think we can add a few others. People often say that instinctive behaviors are genetically preprogrammed; that they are somehow hardwired in the brain. Sometimes scientists also automatically assume that behaviors crucial to survival – foraging, mating and fear – are instinctive by definition. Instinctive behaviors are sometimes told to be biological, as contrasted with psychological; some people would also probably say that ancient behaviors, ones present in so called lower animals, are also instinctive.
At this point, you might say: well, and so what? Behaviors that are genetically determined do not require learning and are hardwired; they are also species specific and fulfill other criteria as well. So it is absolutely normal that we have different meanings: instinctive behaviors just have many characteristics! And indeed, people very often use different meanings of instinct almost interchangeably; Steven Pinker, for example, does not define it at any point in his whole book on innateness – he just assumes that we knowwhat he means.
But is it true? Do so called instinctive behaviors posess all those characeristics? Are different definitions compatible with each other? Let’s have a look at a few examples.
Let’s start with an easy example and take two meanings of instinct:present from birth and not learned. The two features seem to fit each other perfectly – but is it so? Can animals learn anything before they are born?
All those behaviors are present at birth – and yet all of them are learned! It seems that not all meanings of instinct always peacefully coexist. But let’s take another example – drinking waterwhen thirsty. It is behavior that is crucial to survival; it is obviously present in all individuals of a given specie and is clearly adaptive. But well, it is apparently learned, at least in rats. Rats must learn the association between dehydration and the relief from dehydration achieved thanks to drinking water – without this experience, they will not seek water when thirsty. If you feed them with a liquid food that does not allow them to develop dehydration and then – at some point – dehydrate them artificially by the injection of salt, they will not increase their consumption of water.
As Bateson mentioned, one of the meanings assumes that in case of instincts differences among individuals are due to their possession of different genes. What is interesting, there are behaviors that differ between animals that are otherwise clones, having an identical set of genes. Among identical genetically pea aphids, you can observe differences in they startle behavior – when presented with a loming stimulus simulating a predator, some of them will jump out of the leaf, while others remain feeding. Similarly, individual fruit flies may differ in their thermal preferences. Those differences are stable within individuals. It is true also for bacteria – they exhibit surprisng phenotypic variability even without variability in genotypes.
What is the source of those differences if they cannot be explained by genes? It can be caused by different experiences, but It seems that the development of an organism is stochastic – it is not an execution of a program and even with a smilar starting condition can give variable results; levels of gene expressions in cells may differ due to purely random processes. Positive feedback – e.g. when a gene’s product might enhance gene’s expression – can amplify those random fluctuations, leading to different outcomes. What do we have here are behaviors that are present from birth, not learned, unchanged once developed – but differences between individuals cannot be explained by the difference in genes, though they are for sure biological in origin.
Aforementioned variability brings us to another set of definitions that may not be always compatible – hardwired and highly stereotyped.
I am never entirely sure what does it mean when it comes to neural circuits, but the hardwired circuit is probably circuit that is not variable, that is set very precisely during development by some genetic instructions and that controls behavior. The problem is that neural circuits that produce invariant, highly stereotyped outcome are very often themselves variable. The trength of connections between neurons that control heartbeat in leech vary between individuals, even though the outcome is identical. As authors write in the introduction, each animal arrives at a unique solution for how the network produces functional output.
What shouldn’t be surprising. Individuals are different: they differ in size, strength; they may develop in different environments. The nervous system cannot have one pre-specified, rigid solutions to all problems; it must find a solution that will work in specific circumstances: it must be robust. It reacts to what happens during the development: if you artificially enhance spike production in a neuron in a developing embryo, the cell will respond by decreasing the expression of excitatory and increasing the expression of inhibitory transmitters to keep the proper level of excitation. The resulting variability in connections or ion channel expression is probably a way to achieve consistency in behavior. You can read more about the topic in a paper that Robin Hiesinger and Bassem Hassam wrote on variability and robustness.
Another example will show us that behaviors that are fundamental to survival, ancient and controlled by highly conserved brain region may show unexpected plasticity at the neural level.
In a 2017 article by Ryan Remedios and Ann Kennedy authors imaged neurons in the ventrolateral subdivision of the hypothalamus (VMHvl), a structure that is related to mating and fighting, behaviors expected to be instinctive, at least by the authors of the paper. But they show an interesting thing: when an experienced male mouse interacts with an intruder that is either male or female, separate neuronal populations are activated depending on the sex of the intruder. But in case of an inexperienced male, those populations are overlapping and separate only gradually with sexual and social experience. As the claim at the end of their summary:
More generally, [these observations] reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a “hard-wired” system.
Finally, let’s look at behaviors that are present in all individuals – so called universals. The universality of a given behavior is often taken as an indication of its innateness; they are viewed as genetically determined and not learned – how can you expect all animals to develop a behavior if its development would depend on experiences that may differ?
Well, there are experiences that are universal, things that happen to all individual of specie which can be reliably used as a source of information that will guide the development of a behavior that is also universal.
Small mallard ducks follow the calls of their mothers just after hatching – they seem to have an innate recognition of a species – typical call, as they prefer it over calls of other species. But it turns out thet it is learned. Before hatching, when a duck breaks into an air bubble within an egg, it starts to produce its own vocalisations. Gilbet Gottlieb has shown that they learn to recognize calls typical for their species’ mothers by listening to their own vocalisations that share some similarities. If you devocalize ducklings hile they are still in an egg, they will be unable to recognize it.
Listening to one’s own voice is also an experience, experience that is universal and will reliably occur in an every generation – just like a possession of a certain gene.
As you may see at this point, different meanings of instinct may not really compatible with each other; closer inspection shows that it is an incoherent whole. And this incoherence may lead us astray, it may make us surprised by discoveries that are not at all surprising.
This is exactly what happened to the authors of the aforementioned hypothalamus paper. They start with a sentence: all animals possess a repertoire of innate (or instinctive) behaviors, which can be performed without training. And a moment later they write: here we report that hypothalamic neural ensemble representations underlying innate social behaviors are shaped by social experience, just to finish their summary with already quoted fragment, that their results reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a “hard-wired” system.
Authors seem to be puzzled by those inconsistencies – we have aninstinctive behavior that is not learned by definition, and yet an ancient structure that controls this behavior is shaped by experience! They openly ask: how can these findings be reconciled with the “innate” nature of mating and aggression?
They try to give some answers – maybe here are downstream areas that are truly hardwired! We just need to find them!
But maybe we don’t really need to? Maybe this whole result is shocking just because we never thought about our hidden assumptions? That an innate behavior must be unlearned and hardwired? Well, in light of what I wanted to show youit absolutely doesn’t need to. We are again misled by our concepts, by labels that have huge historical luggage of meaning and that are not really useful now.
But what should we do? Is really the concept of instinct not clear enough to be useful? Should we drop it? I think yes – it was around for too long, acquiring too many meanings on the way through centuries. Even if we would cleary define it whenever we use it, other meanings may appear anyway in the minds of our readers. When we say unlearned, they will read inborn or universal, what may not be a case.
Should we then coin a new term? New terms can be fun, but they are rarely adopted by people. Biology and other science are full of terms that nobody uses, or uses them in a way completely diferent from intended, just like Dawkins’ memes.
Fortunately, don’t need to do it. We have a term that already exists and is often used to describe many of behaviors we mentioned here. What is more important, this term is free of all assumptions that we associate with instinct – this term is robust behavior. People use it when speaking of behaviors that are stable, occur reliably in a laboratory environment, that may not require too much learning to develop.
If I would like to specify the meaning a little bit more, I would say that robust behaviors are the ones that you can find in most of the individuals of a given specie. They develop in spite of developmental perturbancies; they are usually highly stereotyped. But they can be shaped by experience; they are controlled by specific neural circuits, that can anyway vary between individuals (and sometimes, who knows, might be even controlled by different circuits in different individuals!). Robust behavior does not need to fulfill all those definitions that we attach to the concept of instinct. This concept is just a nice, open and tolerant guy who does not exclude anyone just because they do not obey some artbitrary rules. It gives us freedom, and frees us from artificial surprises that instinct generates, whenever we find unlearned beavior that is not hardwired or vice versa.
This way of thinking is absolutely not new – the whole developmental sytems theory goes more or less on similar lines, although for me it is still slightly too eclectic. There is a wonderful book on the critique of instinct by Mark Blumberg. But as I said at the beginning – instinct is very hard to kill. It will be still hanging around, whatever we do, just because it’s ancient, and this post will of course not change that.
But don’t tell me I didn’t try!
* in fact it was only my wife who convinced me that they are so bad, thanks!
** Wait, wait – you may say. But the world is full of examples of neural circuits that are very invariable! And that’s true – fly eye is an example of a very precise and repeatable wiring. The same for Caenorhabitis elegans , when development seem just like a very precise execution of a program, with all hermaphroditic worms having exactly 302 neurons. But even in those cases invariable outcome is a result of a stochastic process – in both cases during the development cells are going trough a selection process; some will become neurons, some will die – but you cannot predict which ones will survive (look here and here).
When I look through the window of my flat in Warsaw I see a shop. The shop is called Organic; it is full of overpriced linen seeds, nuts and, most interestingly, different kinds of salt. I am not sure in what sense salt can be organic, but I think I know what are the benefits of calling it that way. Organic means biological. Biological, loosely, means natural. And natural means good.
Organic salt is, thereafter, good salt.
Organic salt is just one of the examples of the infamous appeal to nature. For some reason, at least in Western culture, things that are thought to be natural seem automatically good; better than unnatural ones. That belief is the root of natural medicine, some non-vege people arguments (buthumans are carnivores!) and the existence of the Organic shop on the other side of my street.
Things that are natural are also true – that’s why many philosophers believed that only a man in the wild, primitive state is a true human. And that’s why many evolutionary anthropologists study people in tribes isolated from civilisation – because it might tell us something more about the true human nature, one not tainted with the culture that is supposed to run against many of our instincts.
This is also, in my opinion, one of the reasons why people in behavioral neuroscience, including myself, are nowadays becoming fascinated by ethological relevance.
Development of new methods – like miniscopes (for people outside of the field – these are miniaturized microscopes that allow you to image brain activity in a freely-moving animal) – and the constant development and improvement of others (e.g. ephys); methods that are very often open-source and thus cheap, are definitely the main motor of changes in the field. After decades of doing behavioral studies on immobilized, head-fixed animals people are developing paradigms in which the animal is free to move and explore the environment much more eagerly.
But this fascination goes further than just letting the animal go around with a miniature microscope on its head. More and more I hear neuroscientists claim that they are trying to make their paradigms more natural – or, as I said above, ethologically relevant.
Ethological relevance has many faces. It can be as simple a using stimulus that is supposed to mimic a natural one – e.g. a looming black circle that imitates an approaching aerial predator. It evokes an instant escape response in mice; and, even intuitively, reproduces something that probably occurs frequently in the life of wild mice, especially if you compare it to electric shocks that could at best imitate an attack by a very weak electric eel (see figure above).
It can be something more – instead of using a natural stimulus, you can try to create a whole natural environment. You want to study social behaviors? A few years ago you would put a mouse under the mesh cup and let the other one interact with it. If it does, it is social, if it prefers a non-social object (or an empty cup), it is probably not – and maybe even autistic!
Seems silly? Well, it probably is; but now you can do something much fancier – like putting mice for a few weeks in a large, automated cage composed of many chambers connected by tunnels imitating burrows, and study how they interact with each other without even a need of touching them, just like in a device that was developed in our lab.
Even those who are, for methodological reasons, forced to head-fix their animals are trying their best to make their studies a little bit closer to Mother Nature. Andrew Fink and Carl Shoonover developed a virtual burrow. The idea is simple: mice live in burrows and feel safe inside them. Why not put a head – fixed mouse inside of a burrow and do it in a way that will allow the animal to go outside to explore or hide when needed? It can be used to study curiosity, anxiety and who knows what else; and you can still do your two-photon imaging.
As you may have guessed from my title, this essay will be a critique of the ethologically relevant approach. But just to be clear, I want to stress: it certainly has many benefits.
First, there is the question of stress: in the two last examples animals are probably much less stressed than in traditional paradigms, which is always a good thing.
But there is more. Some people argue that paradigms that use unnatural stimuli may evoke unnatural responses in the brain. Mice are not prepared by the evolution to the presentation of white and black stripes on the screen while being immobilized. Their brains might not work as they would if the mice were watching something they usually see in their short lives – and it might be the case (similar problems can be found in fear studies, look here).
Furthermore, behaviors that are instinctive should be possible to see most clearly in natural circumstances in which they evolved. And if many instinctive behaviors are conserved from human to mice (if that order feels weird, read that wonderful paper), you might learn a lot about human behavior studying instinctive behaviors of mice in naturalistic paradigms. And people do that, obviously; all those studies on feeding, fleeing, fighting and mating are here also to help us understand our basic instincts.
These arguments seem to me to be reducible to one simple statement: that by studying more naturalistic behaviors we have bigger chances to study something real. But well, it might not be always true; and here we come to the critique.
We have a strong tendency to believe in a fixed nature of things. Ernst Mayr once claimed that people came up with an idea of evolution so late because of Platonic essentialism – a belief that things have their true essence. As Richard Dawkins put it, if you treat all flesh-and-blood rabbits as imperfect approximations to an ideal Platonic rabbit, it won’t occur to you that rabbits might have evolved from a non-rabbit ancestor, and might evolve into a non-rabbit descendant.
In my opinion, we show the same essentialistic thinking when we describe animal behavior. There is a defined set of behaviors, the natural repertoire, that is specific to mice. Anything outside of this set – like pulling a lever – is un-natural and artificial.; it’s untrue.
In this framework, animals are viewed a little bit like robots, endowed by a designer – in this case, natural selection – with some well-specified functions. If your robot was designed to wash your clothes and make you healthy dishes full of organic salt and linen seeds, you can expect that it will do those things well. You can try to slightly modify it to do some other stuff – like trolling flat Earth supporters on Twitter – but then you cannot expect it do perform perfectly. If it does it, you are lucky, if not – well, life. The most probable result, though, will be some variable, mediocre, unstable performance.
But animals are not robots. They live in an environment that changes, and usually changes quickly. Our beloved laboratory rodents are descendants of animals that adapted to a completely new environment of human settlements, that were itself changing fast. Obviously those animals were evolving; instincts were moulded by the new pressures. But animals also learn how to solve new problems and they are doing it well – just look on the corvids using cars to smash nuts, or blue tits learning how to open milk bottles, or a whole book of such examples. The nature of (many) animals is the lack of a fixed nature.
Animal’s brains are prepared to do new things; they are also prepared to learn how to pull a lever. You can even make a very controversial claim – if an animal is able to learn a task, it is able to learn this task, and it’s using its brain to do that. It means that you can use this task to study how the brain learns – even if at the end of the day you will only know how the brain learns very weird things. Learning very weird things, though, is the stuff that the human brain does most of the time nowadays.
Moreover, the big problem with studying natural behaviors of mice or rats is that we know relatively little about their natural behavior. We have a somewhat cliche image of a wild mouse: it’s small, it lives in burrows, it’s afraid of predators and well, that’s almost all we can say. There are very nice, but quite old books: Mice all over by Crowcroft and, for rats, Rat – a study in behavior by Barnett; we have also recently put some effort to bring together what is known about social behaviors of mice and rats, but we need much more studies on the rodent behavior in the wild to do a really naturalistic neuroscience.
As we discuss in the aforementioned paper, laboratory animals that we use are domesticated animals. And it poses yet another problem. Our lab animals differ in behavior from their wild counterparts. They are less aggressive; their sexual behavior, a supposed pinnacle of instinctiveness, is altered; the way they learn or flee might be also different – natural behaviors of a wild mouse might not be at all natural to a laboratory mouse. You will not learn much about wolf hunting behavior by observing your chihuahua, which is basically a wolf strain, the C57 of Canis lupus*.
Don’t get me wrong: I am a great fan of ethologically relevant studies; moreover, I am trying to do them as well. What I am arguing against is making a dogma of it. I have recently talked with a person who claimed that people doing head-fixed task on rodents are reductionist. They want to reduce en extremely complex phenomenon of learning to the passive process of watching sequences of colorful dots for thousands of times by a poor, immobile mouse.
Nicole C. Nelson in her wonderful book made an interesting point of calling this approach reproductionistic instead of reductionistic. She studied scientists working on mice models of alcohol addiction. According to her observations, they are fully aware of the complexity of the human condition; and they do not want to reduce the socio-psycho-bio-who-know-what-else phenomenon of alcohol addiction to C57 mice drinking ethanol from the bottle. Instead, they want to reproduce some aspects of this phenomenon in mice just to make it open to a scientific investigations. Mouse models of alcoholism have innumerable problems and they will never allow us to study how poverty can make us addicted to alcohol. But they can be to some extent helpful when put in a bigger context of discoveries from other fields.
Those of us who work with immobilized mice watching dots do not usually claim that they will solve the problem of perception. They just reproducing some aspects of perception to make it tractable.
Gustav Flaubert composed a beautiful list of slogans and cliches popular among people of his time. According to it, THE PRESENT AGE should be always denounced vigorously; when somebody talks about ANIMALS, usually mentions that some of them are more intelligent than humans; if you mention PAGANINI, you are supposed to say that his fingers were enormously long.
Flaubert despised cliches. According to him, they are automatic expressions that we say when our thought is lazy; they could have been original or creative when they were said for the first time, but now are dead, they are meaningless. We can have impression that we said something funny, interesting or deep, but it is just a zombie of thought.
An idea of doing ethologically relevant neuroscience, although not new, is still fresh and viable. I am a little bit afraid that by not thinking deeper about what does it mean to be ethologically relevant, when it is advantageous and when not, by designing studies that are naturalistic only superficially, and by dismissing too easily studies that are not, we will turn it into a cliche. Naturalistic could become a mindless label that we will use to easily judge the study. Absolutely absurd paradigm could be then perceived as interesting just because somebody used hiperrealistic robot model of a buzzard instead of an electric shock.
Let’s not go there, let’s think; let’s keep ethological relevance alive as long as possible.
* That’s an obvious overexaggeration; C57 was developed in 1921; dog strains have much longer history. But see Byelayev research on siberian foxes – in this case their behavior and morphology changed very quickly under domestication, so you do not really need many generations to change an animal (but there is some recent criticism of this study, a criticism that made me less eager to tell the story of doglike foxes as my favourite lunch anegdote – look here)