91探花

Ever get stuck trying to solve a puzzle?

Say, something like this:

What goes in the last box? (The answer and more puzzles are below.)

You look for a pattern, or a rule, and you just can鈥檛 spot it. So you back up and start over.

That鈥檚 your brain recognizing that your current strategy isn鈥檛 working, and that you need a new way to solve the problem, according to new research from the 91探花. With the help of about 200 puzzle-takers, a computer model and functional MRI (fMRI) images, researchers have learned more about the processes of reasoning and decision-making, pinpointing the brain pathway that springs into action when problem-solving goes south.

鈥淭here are two fundamental ways your brain can steer you through life 鈥� toward things that are good, or away from things that aren鈥檛 working out,鈥� said , associate professor of psychology and co-author of the new , published Feb. 23 in the journal Cognitive Science. 鈥淏ecause these processes are happening beneath the hood, you鈥檙e not necessarily aware of how much driving one or the other is doing.鈥�

Using a decision-making task developed by Michael Frank at Brown University, the researchers measured exactly how much 鈥渟teering鈥� in each person鈥檚 brain involved learning to move toward rewarding things as opposed to away from less-rewarding things. Prat and her co-authors were focused on understanding what makes someone good at problem-solving.

The research team first developed a computer model that specified the series of steps they believed were required for solving the Raven鈥檚 Advanced Performance Matrices (Raven鈥檚) 鈥� a standard lab test made of puzzles like the one above. To succeed, the puzzle-taker must identify patterns and predict the next image in the sequence. The model essentially describes the four steps people take to solve a puzzle:

• Identify a key feature in a pattern;
• Figure out where that feature appears in the sequence;
• Come up with a rule for manipulating the feature;
• Check whether the rule holds true for the entire pattern.

At each step, the model evaluated whether it was making progress. When the model was given real problems to solve, it performed best when it was able to steer away from the features and strategies that weren鈥檛 helping it make progress. According to the authors, this ability to know when your 鈥渢rain of thought is on the wrong track鈥� was central to finding the correct answer.

The next step was to see whether this was true in people. To do so, the team had three groups of participants solve puzzles in three different experiments. In the first, they solved the original set of Raven鈥檚 problems using a paper-and-pencil test, along with Frank鈥檚 test which separately measured their ability to 鈥渃hoose鈥� the best options and to 鈥渁void鈥� the worse options. Their results suggested that only the ability to 鈥渁void鈥� the worst options related to problem-solving success. There was no relation between one鈥檚 ability to recognize the best choice in the decision-making test, and to solve the puzzles effectively.

The second experiment replaced the paper-and-pencil version of the puzzles with a shorter, computerized version of the task that could also be implemented in an MRI brain-scanning environment. These results confirmed that those who were best at avoiding the worse options in the decision-making task were also the best problem solvers.

The final group of participants completed the computerized puzzles while having their brain activity recorded using fMRI. Based on the model, the researchers gauged which parts of the brain would drive problem-solving success. They zeroed in on the basal ganglia 鈥� what Prat calls the 鈥渆xecutive assistant鈥� to the prefrontal cortex, or 鈥淐EO鈥� of the brain. The basal ganglia assist the prefrontal cortex in deciding which action to take using parallel paths: one that turns the volume 鈥渦p鈥� on information it believes is relevant, and another that turns the volume 鈥渄own鈥� on signals it believes to be irrelevant. The 鈥渃hoose鈥� and 鈥渁void鈥� behaviors associated with Frank鈥檚 decision-making test relate to the functioning of these two pathways. Results from this experiment suggest that the process of 鈥渢urning down the volume鈥� in the basal ganglia predicted how successful participants were at solving the puzzles.

鈥淥ur brains have parallel learning systems for avoiding the least good thing and getting the best thing. A lot of research has focused on how we learn to find good things, but this pandemic is an excellent example of why we have both systems. Sometimes, when there are no good options, you have to pick the least bad one! What we found here was that this is even more critical to complex problem-solving than recognizing what鈥檚 working.鈥�

Co-authors of the study were , associate professor, and Lauren Graham, assistant teaching professor, in the 91探花Department of Psychology. The research was supported by the 91探花Royalty Research Fund, a 91探花startup fund award and the Bezos Family Foundation.

For more information, contact Prat at csprat@uw.edu.