‘There was no overall sense of how the whole brain works and why it works that way’

I recently had the heads-up on a new textbook called How the Brain Works: What Psychology Students Need to Know (Sage) written by colleagues at Birkbeck, Michael Thomas and Simon Green. Having found the systems overview and the evolutionary perspective particularly useful from a philosophical point of view, even in a book aimed at 18–20-year-old psychology undergrads, I had the chance to ask them about it.

You've written an introductory textbook for psychology students on 'how the brain works' (henceforth HTBW) that places evolution front and centre. Why did you think this book was needed, why did you emphasise evolution so much, and what's new about your approach?

Our memories of being introduced to neuroanatomy as psychology undergraduates were of a cloud of strange Latin names for parts of the brain and a focus on methodology, such as how brain scanners work. There was no overall sense of how the whole brain works and why it works that way. As one psychology A-level student told us recently, 'Whenever I ask a 'why' question in class – such as why the left side of the brain should control the right side of the body and vice versa – my teacher keeps saying, "just learn it, it's in the textbook"'. Our aim with this book was to give that overall gist of how the brain works and why it works that way.

Although neuroscience is complex at the cutting edge, we do know enough to give an overall picture, as a foundation to build a deeper understanding as state-of-the-art research pushes back the boundaries of knowledge. We now also know, thanks to artificial intelligence and robotics, that there are other ways the cognitive system could have worked. Our cognitive systems work the way they do because they are delivered by brains. Brains work the way they do because of biology. Biology works the way it does because of evolution, which led us to place evolution at the heart of our book. The contra-lateral brain organisation, by the way, is probably an evolutionary story involving fish… so quite a distant ancestor.

Hasn't psychology appealed to evolution before?

Indeed it has. But the evolutionary psychology of the 1990s and 2000s was arguably concerned with 'just-so' stories generated by psychologists for how evolution might have selected innate high-level cognitive modules, such as for grammar learning or cheater detection. The contemporary view is one founded in developmental neurobiology and comparative study across related species. Why do you need a brain? Not all species have a single central brain. Jelly fish don't have them. Trees don't have them. Some species have species-specific behaviours – echolocation in bats, syntactic language in humans. Do you find a special new part of the brain for these special behaviours in these species? No. There is still a role for 'evolutionary psychology'. The most popular area is sex differences in mate selection – females are choosy, males just want to spread their genes around. But without an underpinning in our evolutionary history and, eventually, neurobiology, these kinds of accounts still end up as speculation.

In the book, you say how hard things are for psychologists, because most of their key concepts – perception, memory, attention, learning and so on – don't map nicely to how the brain works.

That's right. One of the themes that emerges in the book is how the operation of multiple systems in the brain contribute to the kinds of concepts we use in psychology or education. Take 'learning', for example. For a teacher, this is what occurs in the classroom, such that a student leaves a class with new knowledge or competencies. But in the brain, there may be perhaps eight different neural systems contributing to learning, each of which has a preferred diet of experience to help it acquire and consolidate knowledge or skills, and each of which has a different forgetting curve. We can forget the name of the capital city of Bolivia for example, or who invented the lightbulb, but not that we are scared of spiders. Why would that be?

Of course, psychology and education have the concepts they do because of what they are aiming to explain and predict – behaviour, educational outcomes. But as we understand more about brain function, the challenge of fitting explanations at different levels of description becomes more apparent. And we may begin to see a reason why human behaviour appears so context sensitive – because of the range of mechanisms involved in delivering behaviour, different mechanisms may drive behaviour in different situations.

You say the brain 'doesn't have built-in banana detectors' – what do you mean by that and why does it matter?

It's one of the light-hearted examples we use to underscore principles of brain development. Another is the Brain-Builder-5000, a notional machine for growing brains, which illustrates the idea that most mammalian brains are differently scaled versions of the same growth plan.

Banana detectors illustrate the idea that when it comes to central nervous systems, evolution does not commit to specific solutions; it remains general and lets the experience of an environment produce adaptations. If you were a monkey living in the jungle, it might seem to be a selective advantage for evolution to furnish you with visual banana detectors. You'd be born ready to detect bananas and feed yourself. But this is not the way evolution has gone, because specific pre-programmed solutions render species vulnerable to changes in their environments – if the banana trees all died from disease your built-in banana detectors would not allow you to perceive the oranges hanging right next to them. Instead, the central nervous system (constructed by genetically specified developmental processes) remains a generalist and then uses plasticity; organisms are born to develop visual detectors for whatever foods (or other relevant items) they encounter in their environments.

Perhaps the main point we use this example to illustrate, though, is that evolutionary explanations must be testable. What's the evidence? We discuss work where a mouse is genetically engineered to see a new colour (red) that its species has never previously encountered in its evolutionary history (mice being yellow-blue dichromatic). But mice born with the new ability to see red can nevertheless develop to use red information in their visual systems.

You use sleep as an example to show that sometimes it's not useful to think of the brain as a computer. But, after all, many artificial systems need downtime to do backup. Is sleep really so non-computational?

'Sleep' sounds deceptively simple. But consider the 24-hour sleep/waking cycle, the oscillation between REM sleep and slow-wave sleep, the incredibly complex neurotransmitter control systems, associated hormonal changes during sleep and waking, such as blood cortisol levels, the more recent study of sleep and waste removal from the brain – and all this is before we look at ecological and lifestyle factors that have been shown to influence sleep patterns throughout evolution and the animal kingdom. And we haven't mentioned the memory consolidation functions. 'Sleep' is only a downtime in terms of awareness, otherwise it is a fabulously active set of brain states. What the brain does is physical, driven by the chemical and electrical activity within and between biological cells, causing changes in the body. The idea of computation, while useful, sometimes feels too clunky to capture the diversity of the ways these physical interactions can lead to behaviour. Even the idea of the 'on-off' nerve impulse, which looks a bit digital, has had to be modified as graded potentials have been shown to be important in neuronal conduction. It is notable that more research is now focusing on studying networks of real neurons growing in petri dishes to gain some idea of now the nervous system works. Every period of human curiosity has used the current technology to model the brain and behaviour, and we may be closer with the digital computer than we were with, say, hydraulics and humours, but it's still nowhere near the actuality of brain function.

You spend much of the book describing similarities in how the brain works across different species, and only at the end do you really go to town with what might be human differences. Is the human brain really that similar to other species? What about language? What about human culture, what about art?

Quite right. There is a point in the book where we just lose it with evolutionary theories that stress biological continuity between species, because humans look so different. And we spend time considering what brain features and aspects of historical circumstance may have produced a step-change in the cultural sophistication of our species. Language and tool use are important, since they allow us to work together to shape our environments, but the invention of information tools is probably essential, to enable transmittable and cumulative cultural practices. Then add education, to allow cultures to shape the minds of children raised in those cultures. Humans needed a big enough brain to support this skill acquisition, but here the geological recording is interesting – hominin brain sizes have been steadily increasing for two million years, yet there were periods of 100,000s of years at a time where, as far as we can tell from the fossil record of tool use, cultural complexity had plateaued. And then, bang, 50,000 years ago, cultural complexity explodes, and accelerates: there is only a couple of hundred years between the invention of the idea of computers and the iPhone. Cultural complexity is enabled by but not tightly linked to brain size. Something else clearly happened to trigger the explosion.

My background is in philosophy. I think that HTBW will be really useful for philosophers trying to get into the brain, as well as budding mainstream neuroscientists. Do you think knowledge of how the brain works can be useful for, say, philosophers of mind or philosophers of cognitive science?

We do think the systems view of the brain we offer in this book – how it works rather than a focus on identifying individual structures or the field's methodologies – will be helpful in identifying links between levels of description. We are now a long way from functionalism: believing that the role of psychology was to work out the mind's 'software' and of neuroscience to figure out how that software is implemented in the 'hardware' of the brain. We now understand much more about how the fields of adult psychology, developmental psychology, neuroscience, biology, and evolutionary theory must be linked. We know that although you can study these topics independently, they are not independent but must be mutually constraining. You can't have any old cognitive theory, it has to be one that can be linked to developmental theory; you can't have any old developmental theory, it must be one that can be delivered by the neurobiological processes in the brain; you can't have any old developmental neurobiological theory, it must align with the mechanisms that have been highly conserved across evolution. And so forth. We think HTBW contributes to debates around how scientific theories should be formulated and how fields should relate.

How does the existence of psychiatric disorders – where risk can sometimes run in families – fit with an evolutionary explanation of the brain? Could psychiatric disorders ever offer a benefit, perhaps for group selection?

Fifty years ago genetic approaches to disorders such as schizophrenia and depression focused on single key genes and their mutations. As disorders are normally associated with reduced fertility (fewer offspring), the classical model of evolution would predict that the schizophrenia gene, for example, would gradually disappear from the human genome.  Given the astonishing pace of research, we know that all complex behaviours, including disorders, involve hundreds if not thousands of genetic factors. So, a simple but popular idea is that if you inherit the full set of genetic factors, you may develop the schizophrenia syndrome, along with reduced fertility; however, a given subset may be associated with characteristics that were adaptive at a given stage of human evolution. The favourite is usually the hunter-gatherer phase or 'the environment of evolutionary adaptation'. Examples might include magical and divergent thinking, or group leadership (the 'Shaman' model). For depression it might be withdrawal from conflict in the group, avoiding possibly fatal encounters.

All this is pretty speculative, and the full understanding will wait until we can link relevant genetic factors to neurodevelopment and brain organisation – watch this space...

What have drugs for conditions like schizophrenia and depression told us about how the brain works?

Classic drugs for schizophrenia (antipsychotics) and depression (antidepressants) were first used in the 1950s on a trial and error basis – neurotransmitter pathways in the human brain were then unmapped. As brain mapping of pathways and the pharmacology of clinical drugs progressed in the 1960s and 1970s, so we developed the dopamine overactivity model of schizophrenia and the serotonin underactivity model of depression – both based on the effectiveness and neuropharmacology of drug therapy. Nowadays, in parallel with an awareness that drug therapy is nowhere near perfect – around 40-50 per cent do not respond to antipsychotics, 30-40 per cent do not respond to antidepressants – there is now a more critical attitude not just to the pharmacology of these disorders, but also to their diagnosis and classification. We have been prodded to consider the complex behaviours and experiences linked to psychiatric disorders as the result of the interaction of dynamic brain systems, and how a variety of neurotransmitters, in combination with many other neural factors and histories of life experience, can push the system into different stable or unstable states. It is frustrating that drug companies still search for the 'magic bullet' for schizophrenia or depression, when our current knowledge of the complexity of brain systems, circuitry and neuropharmacology suggest such a search is futile. But we should acknowledge that research into clinically used drugs has been a real catalyst for the study of neurotransmitters and their pathways.

The book offers a plausible explanation of how the brain generates consciousness – but the 'hard problem' with consciousness seems to be a question for physics, not biology. Would you agree?

Maybe eventually physicists will be needed… don't ring us, we'll ring you… but before then, we need to figure out how consciousness works and what it is for. That's a job for philosophers, psychologists and neuroscientists. We did want to put something in the book about how the brain generates consciousness but there's far from a consensus of how the 'hard' problem can be solved right now. The so-called hard problem is how physical processes can lead to phenomenal experience, why it should feel like anything to be alive. For example, some theorists argue everything is conscious (panpsychism) while others argue that nothing is conscious, it's just a delusion we have (eliminativism). We decided instead to offer an example theory just to show what a final explanation might look like; just to show how it might work and that it doesn't need, indeed must not have, a little person in your head looking out of your eyes.

We based the example theory on the following observations: most of the brain's neurons don't contribute to consciousness (e.g. the 80 per cent of the brain's neurons sitting in the cerebellum); the brain is isolated from both its body and the external world, only receiving information from sensory neurons; when you stub your toe, it hurts out in your toe, not in your brain; and recent work in cognitive neuroscience suggests that the prefrontal cortex isn't involved in consciousness either. We ended up with a sensory theory of consciousness: it's a 3D model of the body-in-the-world generated by the brain to drive actions. We think the example works to show that ultimately the hard problem is soluble, given the right (re)conceptualisation, even if we don't yet know what that solution will be.

Your penultimate chapter focuses on climate change and sustainability – why did you pick that topic? What has it got to do with the science of the brain?

We wanted a chapter that pulled together what we've learned about how the brain works and applied it to something relevant and topical, so we chose climate change and sustainability. Although many of us now think that the climate is changing rapidly due to human behaviour, the way the brain works means that even those who hold such beliefs don't necessarily change their behaviour. Why not? Behaviour is driven as much by emotions and by histories of rewarding and aversive experiences. The brain is interested in immediate, personally relevant consequences (jumping in my car to drive to the shops), while climate change often involves abstract ideas far in the future (the living conditions of distant generations). And when governments induce anxiety in their populations to alter behaviour ('it's a crisis', 'we're at a tipping point'), it may produce very different effects on behaviour depending on people's personalities. Climate change and sustainability is of course a very big topic, but we believe neuroscience has a contribution to make.

What was the single most surprising thing you found out about the brain when you were researching and writing this book?

Michael: That you can't predict what a species' particular behaviours are going to be just by looking at its brain structure; or what a person's particular abilities will be by just looking at a structural scan of their brain (other than, in both cases, some weak correlation between size and intelligence). You need more than structure; you need to know how it works.

Simon: Methods… in animal models, we can now turn neurons on and off at will (optogenetics!), study how genes are turned on and off at key points in development, use advanced brain scanning to unpack interactions between brain networks rather than looking at individual structures (i.e., work at the systems level), and use a variety of methods to unpack genetic contributions to behaviour. I have been teaching and writing in the area of brain and behaviour for 50 years. It was fairly routine for 30 years, but technological advances over the last 20 years have been literally astonishing. Reversing the question, the least surprising thing that is emerging is that the brain is just as complex as we thought it was! The systems approach adopted by the book is an attempt to provide an accessible model for understanding some of this complexity.

What is the gist of how the brain works, then?

Basically, it's just some content-specific hierarchical sensory and motor systems modulated by a control system, some appetitive and spatial memory systems bothered with survival-relevant behaviour, an action-selection system bothered about rewards, a motor-smoothing system and some bodily homeostasis systems. But in the book, we say that all in a slightly funnier way. With cartoons.

• For more on the book, see the accompanying website.