The Role of Working Memory in Insight Problem Solving


Insight problem-solving is a unique skill proven to be a highly effective tool in business. However, some critical differences exist between how people handle insight problems and non-insight problems. Working memory plays a much smaller role in solving insight problems.

Working memory plays less of a role in insight problem-solving than non-insight problems.

Researchers have used various methods to investigate working memory’s role in insight problem-solving. Some have employed a single task, while others have studied different problem types.

Researchers have also explored the effects of executive functions. Some studies have found a weak association between executive functions and insight problem-solving, while other investigators have suggested a connection. The results above may be due to the difficulty of studying a subject’s response to a problem.

Researchers have investigated executive function’s effects by measuring subjects’ reaction time while performing probe tasks. The reaction time indicates the load of executive functions on a person’s brain. The load increases abruptly in the last stage of the solution process.

In the first experimental investigation of the role of the executive function in spatial insight problem-solving, participants solved a series of problems. They were asked to solve them in 50 minutes. They were given a verbal hint and told how to respond to the problem if unsure.

Relationship between Aha and accuracy on insight problems and CRAs

The relationship between Aha and accuracy is not immediately apparent. There is not enough empirical data to determine whether these two elements are connected. However, it has been suggested that the experience of Aha is a marker for insight. To test this, researchers should investigate problem types and solution processes.

This study aims to explore the relationship between the experience of Aha and the accuracy of solutions. To this end, researchers studied a series of CRAs (creative problem-solving) tasks. They were given ratings on six dimensions of the problem-solving assignment. They found that specific sizes predicted the magnitude of Aha for both correct and incorrect solutions.

During the solving of these CRAs, participants reported feelings of positive emotions. Specifically, they described the experience of Aha as having a warm feeling and a strong sense that the solution was correct. Furthermore, during the explanation, participants indicated that the answer was sudden and pleasant.

Correlation plots between accuracy and Aha and insight-related effect

This study aimed to explore the relationship between accuracy, Aha, and insight-related effect in insight problem-solving. The relationship between the three components was analyzed in a repeated measures ANOVA of solution accuracy across different problem types. The results indicated moderate to strong positive relationships between these factors.

First, anagrams (tasks involving finding three compound words) are often associated with aha. This is because participants may feel that these are simple memory search tasks. The best anagram ratings were found when the correct solutions were matched with the aha rating.

Second, it is well established that an insight problem can increase motivation and creativity when compared to non insight problems. This can be attributed to the feeling of “sudden clarity.” This is called an aha! Moment, and is also associated with ease and pleasure.

Third, insight and aha-related affect was measured by participants’ confidence in their response and the pleasantness of the insight experience. The average aha rating was above the average for the other three variables.

Neural networks and evolutionary algorithms could help solve insight problems.

Neural networks and evolutionary algorithms can change how people interact with the world around them. As a result, these two approaches can help solve insight problem-solving in various industries.

Neural networks are computer systems designed to process and interpret data. They work similarly to the neurons in the human brain. They can classify, cluster, and predict the expected output from given inputs. They are also used for fraud detection, weather forecasting, and marketing segmentation.

Evolutionary algorithms follow a natural process that simulates the way living things evolve. They seek the best solutions, allowing for the highest quality optimization schemes. Each solution undergoes multiple iterations, generating more robust options that are then evaluated. The weakest ones are eliminated, and new solutions are created.

Evolutionary algorithms are powerful in that they can handle large numbers of data. They can also take advantage of knowledge from previously optimized solutions. For example, a genetic algorithm can produce near-optimal solutions in a short amount of time.