I like it like that

April 16, 2019

Written by: Greer Prettyman

 

Let’s say a new ice cream shop opens in your neighborhood. You visit one day and are given the choice between two flavors: your absolute favorite and one you don’t really like. The choice is a no-brainer—you go for the one you prefer and are perfectly content. The next weekend you visit again, but this time you have to choose between two flavors that you like equally well. It’s a much harder choice and you are forced to re-evaluate your preferences in order to make a decision. Afterward, do you feel confident in your choice and increase your preference for the one you selected? Or do you feel regret and start to prefer the one you left behind? And while all of this is happening, what’s going on in your brain?

 

The field of neuroeconomics is based around using economic models and neuroscience methods to understand and predict how the brain makes decisions. Classic decision-making models propose that when you encounter a choice, your brain decides by picking the option with the highest subjective value, the amount something is valued to you given its benefits and costs as well as personal preferences.

However, that simple decision-making model suggests that if you’re presented with two options that have equal subjective value—say, raspberry and strawberry ice cream—your brain would reach a stalemate and you could never choose. Obviously there are mechanisms in place so your brain doesn’t get stuck when faced with a hard decision between equal valued options, but how does it decide which to choose in these cases?

One theory suggests that after making a hard decision, you should develop a greater preference for the option you picked to reduce cognitive dissonance, the uncomfortable feeling that your choices aren’t aligning with your preferences. In order to reduce this discomfort, you increase your preference for the chosen option as a way to make sense of your choice. For example, in a study back in 1958, participants had to rate the desirability of various household items such as toasters and lamps1. They then had to choose which of two similarly rated items to take home, a hard decision that should create cognitive dissonance. Researchers found evidence for post-decision “spreading of alternatives;” after making their decisions, the women in the study rated the item they chose as more desirable than before and the item they didn’t choose as less desirable. Basically, in order to reduce the discomfort of a difficult choice, the participants changed their preferences to align with their choices.

 

However, an alternative theory explored in a recent paper out of the University of Melbourne suggests that rather than strengthening your preferences​ after ​making a hard decision, the brain updates preferences in real time in order to to facilitate choices between equally valued options2. This study used fMRI to find evidence for the first time that changes in neural activity before a decision is made predict later preference changes.

In this study, researchers measured brain activity while participants made some realistic everyday tough choices—decisions about which snacks to eat. The students who participated in the study reported enjoying and frequently eating snack foods and were familiar with many different brands of snacks. They also had to come to the research visit after not eating for a few hours so they would be hungry and motivated to think about their snack choices.

First, the researchers got a baseline measurement of how much each person valued each snack. Participants indicated how much they would be willing to pay for a wide variety of snack foods on a scale from $0 to $4. Therefore, a snack that was rated worth $3 is assumed to have higher subjective value to that individual than one rated $2.

preferences_fig1
Figure 1. Example of the types of decisions made during the scanner task. An easy choice might be between potato chips (rated worth $4) and twinkies (rated worth $1) while a hard choice might be between chocolate and cookies (both rated worth $3.50). Note, this is an illustrative example, not the materials used during the actual study.

Next, participants were put into an MRI scanner where they made decisions about the snacks while their brain activity was recorded. For each choice, participants were presented with two snacks and asked to pick which one they wanted a chance to receive in an auction after the scan. Some of these choices were easy; the snacks had been rated with very different values so the participant should have a clear preference. Other choices were made to be hard, for example between two snacks that were both rated similarly, so the participant should not have an obvious preference and might have more trouble choosing (See Figure 1 for examples). Participants’ eye movements were also measured so researchers could track how long they looked at each snack option.

Following the decision phase, participants completed another round of rating how much they would pay for each snack. This allowed the experimenters to get a metric of how their preferences (the amount they were willing to pay for the snack) changed after making decisions in the scanner. At the end of the study, the participants were given a surprise memory test and asked if they remembered what choices they made about each snack in the scanner.

 

Going in, the researchers knew from many previous studies that a neural network including the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS) is involved in computing subjective value3. Voigt and colleagues confirmed that activation in the vmPFC and VS corresponded with participant’s ratings of value for each snack.

During the decision phase, they found two regions which had higher activation during hard decisions compared to easy decisions: the left middle frontal gyrus and the left dorsal anterior cingulate cortex (dACC). These regions have previously been associated with decision conflict, consistent with difficulty of choosing between similarly valued options. The crucial finding was that changes in preference after the scan were predicted by activity during hard decisions in two other brain regions: the left dorsolateral prefrontal cortex (dlPFC) and the precuneus (Figure 2). The amount of time participants spent looking at each snack also predicted their choice during the scan and the amount of preference change in the post-scan rating. However, this was only true for the decisions that participants remembered making, which also activated the hippocampus, a region involved in forming and storing memories.

preferences_fig2
Figure 2. Medial and lateral schematics of the brain. This study found activation in the valuation network, the vmPFC and ventral striatum, relating to subjective value. The dorsal anterior cingulate cortex (dACC) and middle frontal gyrus activated more to hard choices than easy choices, indicating a role in identifying decision conflict. Activity in the dlPFC and precuneus at the time of making hard decisions predicted changes in reported preferences.

These results extend what we knew from classic decision-making theories by suggesting that rather than changing after a decision, preferences are adjusted online during the decision-making process. The ability to predict choices and preference changes has implications for a better understanding and prediction of real-world choice behavior. Difficult decision-making may also be leveraged as a tool to nudge preferences in desired directions, such as preferring to reach for a piece of candy instead of a cigarette.

While our preferences may feel like a consistent part of who we are, they are in fact dynamic and flexible. So the next time you’re debating between oreo or cookie dough ice cream, keep in mind that the choice you make now might change which one you prefer later.

 

 

 

 

References:

  1. Brehm, J. W. (1956). Postdecision changes in the desirability of alternatives. The Journal of Abnormal and Social Psychology, 52(3), 384-389.
  2. Voigt, K. Murawksi, C., Speer, S., Bode, S. (2019). Hard decisions shape the neural coding of preferences. Journal of Neuroscience 39(4):718-726.
  3. Bartra O. McGuire J.T., Kable J.W. (2013). The valuation system: a coordinate based meta-analysis of bold fMRI experiments examining neural correlates of subjective value. Neuroimage 76:412-427.

 

 

Images:

Figure 2 created with BioRender

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