Metaphors and Science: Are Brains like Computers?

The idea that the brain and the computer are alike originated in the 1950s when big advances were made in the development of computers. Scientists began thinking of the differences and similarities between brains and computers, a famous example being the seminal work of John von Neumann The computer and the brain (Von Neumann, 1958). Since then, the metaphor of the brain as a computer has become ingrained in our thinking about how the mind functions. This begs the question of how far the comparison can go, since computers and brains are physically different from each other in obvious ways.

When it comes to similarities, brains and computers both process information. They take input, process it and produce output. For the brain, taking an input can mean experiencing perception or recalling a memory, while the output can be an action or a thought (Cisek, 1999). We call the processing that happens in between cognition. Just as well, computers can, for instance, take input from a user who is typing something on the keyboard and an output would appear on the screen. This process taking place between the two is called computation. 

It has also been argued that brains and computers are similar in terms of how information is represented. In a computer, information is encoded through bits. Each bit has a value of either 0 or 1, respectively an ‘off’ or an on ‘state’. These bits are manipulated and transported during processing. In the brain, neurons communicate through action potentials, which are electrical signals they pass to one another. Action potentials are all or nothing signals, meaning that a neuron either fires or not. Neurons get their input from other neurons connected to them. Based on these inputs they can either fire or not fire themselves, thereby relaying a signal to other neurons. This state of firing and not firing is seen by some researchers as the equivalent of the 0’s and 1’s of the computer bits (Von Neumann, 1958; Meier, 2017).

Furthermore, both brains and computers store information. Computers store information in their “memory”, whereby everything is represented by bits. This information storage is one-to-one – things get stored exactly as they are, and we can also retrieve them exactly as we stored them. Information storage in the brain is not so clear-cut. We are certainly able to create memories and recollect them later. Still, two people experiencing the same event will register that event in their minds differently based on their own interpretation of it. Unlike computers, brains do not have an identifiable depository of memories. Memories are not stored in a specific place in the brain and cannot be separated from the information processing (Alberini, 2011). We also do not keep an exact copy of what happened to us in the past – most likely we keep a representation, a compact version of a memory, because our brain extracts, synthesizes and integrates information (Alberini, 2011; Schacter et al., 2011). In addition, our thoughts and emotions act on memories, so with each recall, we technically never remember the exact same thing (Nader, 2016). Lastly, while computers have physical constraints on their memory, it is unknown whether there is a limit to how many memories a brain can accumulate in a lifetime, as researchers are still investigating how to measure memory capacity (Reber, 2010).

The similarities we discussed are rather superficial and require many caveats, so the explanatory power of our metaphor is quite limited. Moreover, there are many more aspects that set computers and brains apart. Unlike computers, communication in the brain is done chemically in addition to electrically (Lovinger, 2008). Brains grow and evolve, while computers do not. Brains are plastic, meaning that parts of the brain can take up new functions as well as losing them. Computers can be shut down, while the brain functions continuously, even when we sleep. 

Although the reverse of the comparison does not have to be true, some people also think computers are like brains, alluding to the possibility of having conscious machines in the future. So far, consciousness remains something that only the brain is capable of, and the prospect of computers acquiring it seems unlikely – they lack the ability to set their own goals and take decisions autonomously, they cannot share information flexibly between programs and most of their programs do not have a learning capacity (Dehaene, 2014). 

These big differences make scientists in modern neuroscience and psychology avoid directly comparing brains with computers. Yet, some theories recognize that brains are like computers in the sense that they are both computing systems. This definition of computing systems is of a more general nature, separating underlying principles from implementation. This is the central idea of the Classical Computational Theory of Mind (CCTM) (or computationalism) (Rescorla, 2020). According to the CCTM, the brain is a system that processes information and fulfills the requirements of a computing system. Therefore, it is especially important to disentangle the specific implementation of a computing device with either the brain or computer (Marcus et al., 2014). The metaphor might then be more accurately phrased as “the brain is a computing system”.  

The “brain is a computer” is not the only metaphor that exists about the brain. For instance, Graham suggests that we should see the brain as the internet (Graham, 2021). The internet is a highly connected network of routers – units that send packages of information from one to one another, forming a link from your computer to a server – which transfer information between users. This information between users does not always take the same route in the router network, meaning that these networks are highly adaptive. This metaphor highlights the importance of the connectivity and plasticity in the brain and suggests that the brain should be viewed as a communication system. Like the routers of the internet, the neurons transmit in a flexible way, changing the flow of information according to the situation. This high flexibility and connectivity of the brain ensures that information can be distributed over many parts in the brain. Looking at the brain as the internet, it means that we see it as a collection of local processors (groups of neurons) that are linked together. This view is useful for understanding theories that focus on the role of brain connectivity. For instance, the global neuronal workspace theory of consciousness states that information becomes conscious when it is globally accessed by many local specialized processors in the brain (Dehaene et al. 1998; Mashour et al., 2020). 

The “brain as a computer” metaphor is easily adopted by the public because it puts together two things that people have experience with but usually do not understand the underlying principles of. So even though the metaphor is not extremely popular in research nowadays, it keeps shaping the way people think about the brain. Baria and Cross (2021) warn this might lead to underestimating the complexity of the brain and seeing computers as too intelligent. People might therefore overestimate the capabilities of “intelligent” computing systems and the dangers they pose. The thought of “smart” computing systems can create irrational fears (for instance, the fear that soon robots will be able to take over the world). On the other hand, metaphors describing the brain are a starting point for developing theories and a way to communicate difficult concepts in familiar terms. Metaphors should not be avoided, but it is important to remember that they can lead to wrong associations if we treat them more as facts than as metaphors. That is why it is always good to explicitly mention the assumptions that underlie metaphors about complex concepts, such as how the brain works. <<