System 1 and System 2 Thinking

Published by Elaine Gallagher on

abstract image of neuro connections representing thoughts and system thinking

System 1 and System 2 Thinking

The notion of System 1 and System 2 thinking has been a cornerstone of the literature and theory on decision making since its development. For many years, Daniel Kahneman and his influential book ‘Thinking Fast and Slow’ has offered a neat and intuitive way to explain our decision making and thought processes through a binary categorisation. While he did not coin this terminology, he did popularise it and it has been in common parlance for many years.

abstract image of neuro connections representing thoughts and system thinking

But what does it mean?

In short, Kahneman’s work states that when making decisions, we either utilise ‘fast’ (System 1) thinking, or ‘slow’ (System 2) thinking. To summarise briefly, System 1 thinking describes our automatic thinking – those thought processes that are effortless, quick, and allow us to make rapid decisions and judgements with little cognitive effort or conscious deliberation. System 2, on the other hand relates to the more deliberate and involved cognitive processing. This type of decision making takes a more analytical approach, requires greater levels of attention and mental effort, and is used for more complex problems.

In his acceptance speech for the Talcott Parsons Prize for behavioural economics in 2011, Kahneman describes a simple example of System 1 and System 2 thinking. When we are asked what 2+2=, we automatically respond with 4. It is immediate and automatic, and we have very little option but for the answer to appear in our mind. In other words, it is something that happens to us. On the other hand, if we are asked what 174×14=, for most of us, the answer to this question does not spring to mind and instead we need to employ the effort of long multiplication and follow a set of steps before we get to the answer of 2058. In other words, this is something that we need to do, which requires effort and directed attention.

Does the theory still stand up to criticism?

In recent years, as this theory has found its way into the non-academic sphere and has been considered for more industry-born problems, it has been subject to a range of criticism, with some writing off the model as completely inaccurate and therefore useless. While it is true that the model is flawed in some ways, we must question if it is wise to completely discredit the theory, or whether it may still have some benefits and legitimate use. I’ve delved into this topic to try to get a sense of whether the model stands up to scrutiny or if indeed it should fall out of popular use.

One of the main critiques of the model is that cognitive processes are too complex to be neatly categorised into one system or the other. As a result, System 1 and System 2 thinking has been described as oversimplified. Some critics have stated that the distinction between automatic and controlled processes is not always clear, and it is likely to involve at least some level of overlap in many instances. Kahneman himself has acknowledged that the model is a simplified representation of cognition, but has advised against taking the model too literally, and stating that cognitive processes will have varying degrees of interplay between the two systems.

Criticism has also emerged in relation to the apparent lack of a boundary between System 1 and System 2. The two systems may interact and influence each other in ways that are not easily captured by a clear-cut binary process. However, it has been suggested that in this case, it is not appropriate to think of two distinct systems, but rather to view this as a continuum. This notion is supported by researchers such as Stanovich, West & Toplak (in their research on rationality) and Evans & Holyoak (in their research on inference-based reasoning). Viewing the decision-making process as being along a continuum, whereby at one end more automatic thinking exists and the other end more deliberate thinking, with the individual moving between these fluidly.

Many interpret the model as having an overemphasis on System 1 being error-prone, with System 2 being deemed relatively error-free. This should not be seen as the case, as both systems are open to error, and there will be many instances where System 2, despite utilising more resources and effort, will lead to an error. Kahneman aligns with this view, stating that both systems are open to errors, with the nature of these errors differing between the two systems.

What’s in a name?

Interestingly, it appears much of the criticism levelled at the Dual Process Theory may be as a result of a simple misnomer. Kahneman himself stated that “System 1 and System 2 are fictitious characters; they do not exist as systems or have a distinctive home in the brain.” He has also apologised for the use of the terminology of ‘System 1 and System 2’ but did state that he still finds the terminology useful. The use of the word ‘system’ may be misrepresentative in terms of how the model is viewed. He has stated that it may have been more appropriate to call them ‘Type 1’ and ‘Type 2’ cognition, as this may lend itself more suitably to their functionality. If you want to learn more about some of the common potential misinterpretations of the model, this article concisely debunks some of the common myths associated with the two systems thinking approach.

Is it all academic?

A notable portion of the criticism levelled at System 1 and System 2 thinking comes from non-academics. This suggests it’s likely that during its move to the mainstream, the model has lost some of the necessary nuances around its interpretation. System 1 and System 2 are not (and were not) intended to be representative of brain processes in a literal sense that can be followed via two distinct paths. This often literal interpretation of the model has unsurprisingly resulted in the model being dismissed as lacking in credibility. This raises a wider issue around not whether the model is wrong, but whether we are interpreting and applying it in the right way. Do the issues lay entirely with the model, or can we attribute some of them with how we interpret and utilise academically grounded tools within a real-world environment?

The supposed System 1, System 2 fallacy is a good example of the saying that ‘all models are wrong, but some are useful’ attributed to statistician George Box. This statement tells us that no model is perfect, and all are subject to oversimplification leading to some level of inaccuracy and error. It does, however, acknowledge that many models, while somewhat inaccurate, are still useful and are valuable tools to gather insights and to understand otherwise complex systems. As long as we are aware of their limitations, we should not be swayed away from the use of models such as System 1 and System 2, while bearing in mind that they should be considered only an approximation. We need to be careful to ensure we don’t end up throwing the baby out with the bath water.

Abstract image of the head of a sculpture sliced in parts, representing system thinking

How does it apply to learning?

This discussion is important for the learning industry as, if we take this model at face value, then it may be considered that we could develop interventions that target one, or the other, of the two systems. This, of course, wouldn’t be feasible and would lead to the development of an ineffective and illogical solution. Instead, we could think about System 1 and System 2 as a ‘boost’ of sorts (read more about boosts here). It can give us guidance around how certain types of information may be interpreted, but we must also bear in mind the variability within our target audience and the importance of context.

Here at BAD, we use it as a way to ensure that we ‘go with the grain of human nature’ in our designs. We don’t treat our end-user as a computer but as a living breathing human who will be influenced by a range of design techniques which may trigger ‘fast thinking’ (such as in the case of emotion or social comparison, for example), or indeed require more effortful, deductive ‘slow thinking’. While we understand that the model is not perfect, we can still appreciate its uses and the benefits it can offer when designing learning solutions. At BAD we also have experienced Behavioural Scientists that have the expertise to understand and interpret psychological models, while, importantly also understanding their limitations.

If you are interested in exploring how different psychological models could apply to any people based goals you have for your organisation, get in touch with one of our team. We are always keen to chat about all things behavioural science related!