Think of your brain as a vast network of ideas connected to each other. These ideas can be concrete or abstract. The ideas can involve memories, emotions, and physical sensations.
When one node in the network is activated, say by seeing a word or image, it automatically activates its surrounding nodes, rippling outward like a pebble thrown in water.
As an example, consider the following two words:
“Bananas Vomit”
Suddenly, within a second, reading those two words may have triggered a host of different ideas. You might have pictured yellow fruits; felt a physiological aversion in the pit of your stomach; remembered the last time you vomited; thought about other diseases - all done automatically without your conscious control.
The evocations can be self-reinforcing - a word evokes memories, which evoke emotions, which evoke facial expressions, which evoke other reactions, and which reinforce other ideas.
Links between ideas consist of several forms:
In the next exercise, you’ll be shown three words. Think of a new word that fits with each of the three words in a phrase.
Here are the three words:
cottage Swiss cake
Ready?
A common answer is “cheese.” Cottage cheese, Swiss cheese, and cheesecake. You might have thought of this quickly, without really needing to engage your brain deeply.
The next exercise is a little different. You’ll be given two sets of three words. Within seconds, decide which one feels better, without defining the new word:
sleep mail switch
salt deep foam
Ready?
You might have found that the second one felt better. Isn’t that odd? There is a very faint signal from the associative machine of System 1 that says “these three words seem to connect better than the other three.” This occurred long before you consciously found the word (which is sea).
For another example, consider the sentence “Ana approached the bank.”
You automatically pictured a lot of things. The bank as a financial institution, Ana walking toward it.
Now let’s add a sentence to the front: “The group canoed down the river. Ana approached the bank.”
This context changes your interpretation automatically. Now you can see how automatic your first reading of the sentence was, and how little you questioned the meaning of the word “bank.”
The purpose of associations is to prepare you for events that have become more likely, and to evaluate how surprising the event is.
The more external inputs associate with each other, and the more they associate with your internal mind, the less surprising an event is, the more System 1 acts by intuition, and the harder it is to detect errors.
Consider this sentence: “how many animals of each kind did Moses take into the ark?”
The correct answer is none - it was Noah who took animals into the ark. But the idea of animals, Moses, and the ark all set up a biblical context that associated together. Moses was not a surprising name in this context.
However, say “how many animals of each kind did Kanye West take into the ark?” and the illusion falls apart. Kanye West is not congruent with the mention of animals and ark, and so the name evokes surprise, thus calling in System 2 to help.
System 1 maintains a model of your world by determining what is normal and not.
Violations of normality can be detected extremely quickly, within fractions of a second. If you hear someone with an upper-class English accent say, “I have a large tattoo on my rear end,” your brain spikes in activity within 0.2 seconds. This is surprisingly fast, given the large amount of world knowledge that needs to be invoked to recognize the discrepancy (that rich people don’t typically get tattoos).
We also communicate by norms and shared knowledge. When I mention a table, you know it’s a solid object with a level surface and fewer than 25 legs. It’s your System-1 brain that makes this immediate, unconscious association.
(Shortform note: this also explains why many moral arguments are based around semantics. In different communities, people will have different conceptions of what the same word means, like “life” in the abortion debate. The norms are entirely different, but often people don’t realize this.)
The System-1 brain wants to make sense of the world. It wants large events to cause effects, and it wants effects to have causes. It tries to bring coherence to a set of data points and sees interpretations that may not be explicitly mentioned.
For example: “After spending a day exploring sites in the crowded streets of New York, Jane discovered that her wallet was missing.”
Immediately, you likely pictured a pickpocket. If you were asked about this sentence later, you would likely recall the theft, even if it wasn’t stated in the text.
Once you receive a surprising data point, you also interpret new data to fit the narrative.
Imagine you’re observing a restaurant, and a man tastes a soup and suddenly yelps. This is surprising. Now two things can happen that will change your interpretation of the event:
In the first case, you’ll think the man is hyper-reactive. In the second case, there’s something wrong with the soup. In both cases, System 1 assigns cause and effect without any conscious thought.
All of this automatic associative thinking works much of the time, but it fails when you apply causal thinking to situations that require statistical thinking. We’ll cover many more of these biases throughout this summary.
The converse is also true. Complexity is mentally taxing. Maintaining multiple incompatible explanations requires mental effort and System 2. In contrast, clear cause and effect, and easy associative relationships, are much less taxing on the brain. It’s easier to see the world in black and white than in shades of gray.
Shortform warning: this chapter of Thinking, Fast and Slow cites the highly controversial literature on priming, which has failed to replicate in follow-up studies and has been accused of p-hacking or publishing only positive results.
Kahneman admitted: “I placed too much faith in underpowered studies...The experimental evidence for the ideas I presented in that chapter was significantly weaker than I believed when I wrote it.” And the “size of behavioral priming effects...cannot be as large and as robust as my chapter suggested.”
The concept of priming took association beyond mere thought, to the functional level of ideomotor activation. When an idea is triggered, its associations can cause you to behave in a meaningfully different way without your consciously realizing it.
Examples from research studies include:
In the reverse direction, behaving in a certain way can trigger ideas and emotions:
The implications of priming are profound - if we are surrounded everyday by deliberately constructed images, how can that not affect our behavior?
And likewise, if we are required to behave in a certain way (in a workplace, in a social community, as citizens), does that not affect our cognition and beliefs?
The effects may not be huge - being surrounded by images of money doesn’t make you violate the law or put yourself in physical harm to get money. But a few percentage points by swinging marginal voters can make a difference in elections.
Shortform warning: Many of the studies cited were later found to have insufficient power, such that either the studies were being p-hacked, or only cherry-picked positive results were being published. It appears the field was too eager to jump on evidence that fit their view of the world.
Note the irony about being biased about biases. When priming came out, the field of psychology/behavioral economics had just undergone a paradigm change of humans being subject to systematic biases. The field hungered for confirming evidence itself, becoming too ready to accept a neat story (priming) without employing its System 2 thinking to question whether the evidence was valid!
Kahneman notes that he’s still a believer in the idea of priming, “There is adequate evidence for all the building blocks: semantic priming, significant processing of stimuli that are not consciously perceived, and ideo-motor activation....I am still attached to every study that I cited, and have not unbelieved them, to use Daniel Gilbert’s phrase.”