The complex nature of the brain and the tools to help us understand what’s going on inside
The most complex object in the known universe
Science recognizes that the 3 pounds of soft stuff sitting between our ears is likely to be **the most complex object in the known universe**.
In fact, our brain power is so vast that, according to a 2009 study, we are powered by some **86 billion neurons** – that’s approaching half the number of stars in the Milky Way.
Even 2500 years ago, Greek physician Hippocrates, the ‘Father of Medicine,’ argued with his intellectual peers that the mind resides in our heads, not in our hearts.
And yet, until relatively recently, the internal workings of this mysterious organ remained unknown, predicted based on how we react to external stimuli and behave in wide-ranging environments.
Advanced scientific techniques are starting to pull back the curtains, enabling us to peer in at the billions of neurons and begin to understand how they work together to provide complex rational and even irrational thoughts and explore our experience of life itself.
We are finally ready to seriously consider some of the answers to our biggest questions: **What is intelligence? Why do we behave as we do? And what is the nature of consciousness?**
Why explore human cognition?
Cognitive science combines the best tools, ideas, and methods of linguists, philosophers, neuroscientists, computer scientists, and psychologists to explore the inner workings of the human mind and its connection with the environment.
**Cognition** is central to the field, referring to the thinking involved in **perception, decision-making, learning, problem-solving, language, and emotional experience**. Cognitive science ultimately attempts to understand what it means to be human.
And, while it is fascinating and vital to the academic investigating the feeling of life itself, it is also of great importance for the rest of us.
After all, exploring how and why our brain works as it does is central to medicine. Understanding human cognition offers valuable insights and the potential for developing treatments for mental illnesses such as depression and schizophrenia and neurodegenerative diseases that damage the cells and nervous system, including Alzheimer’s, Parkinson’s, and motor neurone disease.
**A healthy brain is crucial if we are to keep extending our life expectancy and live our best lives.**
Cognitive techniques
How does science delve into the seemingly unknowable workings of the mind and approach the slippery subject of thought?
At one time, only philosophers posited what might be happening within our skulls; now, with an increasing focus on unraveling the mysteries of the brain and mind, new approaches, scientific methods, and theories are making giant leaps forward.
**Cognitive psychologists, cognitive neuroscientists, and evolutionary psychologists** are working at the cutting edge of this wave of new research, attempting to understand our thinking, biases, and even mental disorders.
Their challenge is to learn more about our brain’s internal processes, how we make sense of the overwhelming environmental stimuli available to us, and how we decide how to react and behave.
**Cognitive psychologists observe our behavior** to understand the processes involved in attention, perception, memory, learning, problem-solving, and much more. Contrastingly, **cognitive neuroscientists rely on the latest brain-imaging techniques** to peer in at the once incomprehensible. Finally, we have **evolutionary psychologists, looking deep into our animal past** while comparing who we are now with our closest and most distant relatives.
Cognitive psychology
For much of cognitive psychology’s relatively short history, researchers have identified with the **‘information processing approach’** – drawing analogies between the workings of the brain and the computer.
Internal cognitive processes spin away in the background in response to environmental stimuli or tasks presented in the psychologist’s lab (inputs), resulting in behaviors and responses (outputs).
And yet, there is much more to it than that. As well as **‘bottom-up’ processing**, most problem-solving also involves a **‘top-down’ element**, engaging with the individual’s beliefs, knowledge, and expectations.
Moreover, processes occur simultaneously, known as **‘parallel processing,’** rather than serially, in a series or sequence. And this happens more frequently when we are well-practiced, which is why learning to drive, and balancing multiple demands, is, at least initially, so tricky.
While cognitive psychology has contributed significantly to understanding human cognition, it has also come under fire.
As much of the testing occurs in artificial environments such as labs, testing may lack **‘ecological validity’** – meaning that findings may not apply to real-world complexity.
Cognitive neuropsychology
Cognitive neuropsychology involves examining the patterns of cognitive performance and deficits faced by brain-damaged patients. Its results are prized, telling us much about what we typically consider ‘normal’ human cognition.
In fact, during the 1970s, extensive research into a head trauma patient, referred to as ‘KF’ to preserve their anonymity, who had a severely reduced short-term memory yet intact long-term memory, significantly challenged existing theories of human memory.
Findings suggested that **the brain had a degree of modularity**. Some cognitive systems appeared to operate relatively independently, providing **domain-specific functions**, meaning ones applicable to a single area of cognition, such as face recognition.
Damage to such an area of the brain can lead to an inability to recognize individuals while still leaving object recognition relatively intact. This means we can reasonably assume that recognizing faces and objects happens in different and distinct parts of the brain.
And yet, that’s not the whole story. **Neuroimaging also suggests a high degree of connectivity**, supporting the idea of **parallel processing**, neurons firing at the same time, rather than serial processing, one after another.
Undoubtedly, cognitive neuropsychology continues to shape our understanding of the workings of the mind, particularly specialist functions such as memory, language, and problem-solving, and yet must account for the flexibility and extensive interactions throughout the brain.
Cognitive neuroscience
**86 billion interconnected neurons** are spread throughout the brain – that’s unimaginable. Cognitive neuroscience attempts to understand the brain in action by imaging small areas of tightly clustered connections between such neurons, known as ‘**modules**,’ and between regions with vast numbers of connections to other regions, known as ‘**hubs**.’
By asking subjects to perform various tasks while imaging the brain, it is possible to determine the areas that become active and the order in which they occur.
With multiple techniques available, including ‘**positron emission tomography**’ to track radioactively labeled tracers injected into the body and ‘**functional magnetic resonance imaging**’ to trace tiny changes in blood flow, the cognitive neuroscientist can provide detailed accounts of brain functioning and more general activity.
Imaging methods are wide-ranging and imaginative. ‘**Electroencephalography**’ includes attaching electrodes to the scalp to measure tiny changes in electrical activity in the brain.
At the same time, ‘**transcranial magnetic stimulation**’ is more radical, inserting a small coil into the brain, delivering a magnetic pulse of current, and observing the inhibited processing that results. As a result, we are able to find out which areas of the brain certain bits of thinking happen in.
Computational neuroscience
**Artificial intelligence** aims to produce intelligent outcomes from computer systems that may not resemble the human brain – modeling the ‘what,’ not the ‘how.’
Computational neuroscience also uses computers, but attempts to model the **‘what’ and the ‘how’** of the brain’s workings to better understand human cognition. They are both **descriptive and predictive** and offer powerful modeling tools for checking for imprecise terms and hidden assumptions in cognitive theories.
**Often computational models are domain-specific**, meaning that they are only applicable to certain cognitive functions, such as the ability to read aloud, yet some, ambitiously, attempt more general architectures that can be applied widely.
While there are many models, **connectionist** models are the most popular. They consist of **interconnected networks of simple units aiming to emulate cognitive performance without explicitly defining rules**.
Such artificial neural networks typically involve input, representational, and output layers, and are used to model aspects of human cognition such as perception, learning, and memory.
Other approaches for modeling human thinking, known as **‘production’ systems**, are more explicit, capturing decisions and choices through a series of ‘if … then’ statements.
Indeed, human thinking can often be thought of as a series of productions. “If I cross the road now, I can get across before the car reaches me.” And yet, the approach caters less well to much of our intuitive thinking, where decisions are implicit rather than explicit.
What are the challenges?
Understanding and modeling the brain is far from straightforward. Indeed, with an estimated **100 trillion connections** and a set of explorative tools that will one day seem archaic and elementary, the challenge may seem insurmountable.
And yet, by observing brain activity, studies have even been able to identify which objects participants are looking at with a reasonable degree of accuracy. However, such research has been performed with limited stimuli and could in no way be described as ‘mind-reading.’
We must also remain cautious in mapping individual brain areas to specific cognitive processes or even down to individual beliefs and factual knowledge. Sometimes referred to, unflatteringly so, as ‘blobology’ due to degrees of localized activity being pictured as blobs of color based on statistical significance – increasing from red, to orange, and then white.
And yet, **there is unlikely to be one single region** involved in jealousy or a small set of discrete neurons holding the date of our loved one’s birthday due to their interrelationships with other thoughts, memories, and beliefs. This is reflected in findings of different labs identifying differing blobs of activity based on the nature of their testing procedures.
While each approach to understanding human cognition makes its own distinctive contribution, they all have limitations. Yet, comparing findings from each and combining techniques will undoubtedly continue to lead to new, previously obscured understandings.
The mechanical brain?
**There is much we still have to learn about the brain**. When American boxer Julian Jackson swung a powerful right hook at Englishman Herol Graham in 1990, in a battle for the WBC world middleweight title in Spain, it connected with his head, leaving him unconscious before he had even hit the ground.
And yet, why should a primal, mechanical act have such a grave impact on the electrical or biochemical signals of the brain? According to brain researcher William Tyler at Arizona State University, **neurons may be hooked up in a ‘mechanical network,’** like cogs in an antique watch.
And Tyler is not alone. Other researchers are confirming that it’s not only chemicals and electricity that allow brain cells to communicate; it appears mechanical forces also have a part to play.
While seemingly at odds with conventional thinking, it may explain the reasons behind certain types of brain injury and why Graham’s gears may have been knocked out of alignment, and even suggest possible non-invasive treatments, such as soundwave therapy involving the stimulation of the brain using acoustics. Researchers are currently exploring ways of using such knowledge to treat chronic pain.
Inner chatter
**Not all brain research involves advanced imaging techniques or observing people as they solve complex problems**. Sometimes, it can simply be listening to people talk, revealing their inner voice. After all, don’t we all experience ‘chatter’ – the silent conversations we have with ourselves?
And it’s important, because, according to experimental psychologist and neuroscientist Ethan Kross, **chatter is often made up of cyclical negative thoughts that jeopardize our performance**. It is, therefore, vital to understand this inner speech and how it shapes our thinking and decision-making.
But how do we know what’s happening in other people’s heads? One approach is simply to ask them. However, their answers are often given with the benefit of hindsight, commenting on what they remember saying to themselves after something happened.
As a result, our preferred method of finding out what’s going on in someone’s head is to train participants to give detailed descriptions of their inner dialogue in response to a reminder, such as a beep from a watch or phone.
Other insights, such as how the conversations in our heads help or hinder our self-confidence, may come from those rare people who hear no inner voice at all or by attempting to limit ‘normal’ chatter using meditation.
Building a map of the brain
The **Human Connectome Project** was launched in 2009 with $40 million of funding and an audacious goal. It aims to build a **functional and anatomical map of the human brain** and create a body of data that will shed light on brain disorders, including dyslexia, Alzheimer’s, schizophrenia, and autism.
Since then, it has carried out brain scans on thousands of volunteers – their results stored in a vast, searchable database. A mathematical approach known as ‘**graph theory,**’ used successfully in exploring the impact of social media and the spread of infectious diseases, breaks the brain images down into networks made up of dots and lines – known as ‘**nodes**’ and ‘**edges**.’
In 2016, the data helped researchers identify **180 previously unmapped brain areas**, building a more complete picture of cognition and opening a new window into how the brain works. So far, findings point to cross-talk between areas once thought to work separately and, perhaps surprisingly, the importance of ‘holes’ in networks for avoiding us getting confused by multiple sensory inputs.
