Highly intelligent people are more likely to ditch old habits for better ideas, study finds

A study of social learning found that people with higher intelligence are more likely to switch to novel solutions when they become available, particularly if they are better than the existing ones. Higher openness to experience was also associated with switching to novel solutions, but specifically to those that were similar or worse in quality than the existing solutions. The research was published in Personality and Individual Differences.

The ability to learn from others is a trait that is crucial for human evolutionary success. This ability is called social learning. Thanks to it, humans are able to acquire knowledge, behaviors, attitudes, or skills by observing and interacting with other people. People can learn by watching what others do and noticing the consequences of their actions. For example, a child may learn how to behave in a classroom by observing classmates and teachers.

Social learning can also involve copying behaviors that appear to bring rewards or avoiding behaviors that lead to negative consequences. Parents, peers, teachers, colleagues, and media figures can all serve as models for learning. The process is not automatic, because people interpret what they observe and decide whether a behavior is relevant or appropriate. Social learning plays an important role in the development of social norms, values, language, and everyday habits. It can spread both helpful behaviors, such as cooperation, and harmful behaviors, such as aggression or prejudice.

Study author Tiarn Burtenshaw and his colleagues conducted two studies. In the first study, they examined the extent to which individual differences in intelligence and openness to experience guide human social learning. They examined how much a person’s decision to switch to a new solution to a problem (or not) depends on these two traits. The novel solutions examined were either superior, inferior, or equal to the existing solution.

Study participants were 201 first-year undergraduate university students from the University of Western Australia and 369 participants recruited through Prolific from the UK. The Prolific participants were paid £5 for participation. The average age of participating university students was 20 to 21 years, while the average age of Prolific participants was 44 to 45 years.

Study participants trained to solve two behavioral tasks: the Padlock task and the Maze task. In the Padlock task, participants learned to open a chest to win a “prize” (as the study authors stated, “an obviously counterfeit $3 coin”). Opening the chest required opening four padlocks. Three padlocks were opened during training, while for the fourth padlock, participants were given a choice between using the solution they were trained to use or using a novel solution.

Each solution on the Padlock task was a seven-digit padlock combination, which participants had to memorize and then enter by clicking numbered buttons. Here, the solution quality was determined by the number of times the participant would need to change buttons when entering the combination—ranging from a highly inefficient solution where six digits were different from the previous digit, to an efficient solution where all digits were the same.

In the Maze task, participants’ task was to navigate a taxi through a maze. They had to navigate each maze four times: three times during training and once when given a choice between the trained solution and a novel solution. In this task, solution quality depended on the length and the number of turns in a route the taxi takes.

Participants also completed an assessment of intelligence (using four subtests: the Advanced Vocabulary Test, Raven’s Advanced Progressive Matrices, Letter-Number Sequencing task, and Connections Test) and an assessment of personality (the HEXACO-60).

Study 2 used the same design as Study 1, with the difference that the amount of training participants received was systematically varied. In this study, participants either received one, three, or six rounds of training on each trial before being shown the novel solution. Participants were 90 first-year undergraduate students from the University of Western Australia. Seventy-eight percent of them were women.

Results showed that participants were more likely to switch to a novel solution if it was superior to the one they trained to use. However, when the solution they were trained to use and the novel solution were equal in quality, participants were more likely to use the solution they were trained to use.

Individuals with higher intelligence showed a higher tendency to switch to novel solutions. This tendency was particularly strong when the novel solution was superior to the one they were trained on. Higher openness to experience—a personality trait describing a tendency to be curious, imaginative, creative, and interested in new ideas and experiences—was also associated with the tendency to switch to new solutions, but to those that were similar in quality or worse than the solution study participants learned.

Study 2 showed that longer training with a solution was associated with a decreased likelihood of switching to a new one. As the study authors put it, greater familiarity with the trained solution increased “maintenance bias,” reducing social learning.

“Our findings demonstrate the importance of individual differences in intelligence and personality, alongside experiential factors, to human social learning,” the study authors concluded.

The study sheds light on the nuances of human social learning. However, it should be noted that the experiment was conducted on two relatively short tasks where the choice of solutions had no practical relevance for participants personally. Studies examining social learning behavior in situations where the choice of solutions produces relevant real-world effects for participants might yield different results.

The paper, “Individual differences in intelligence and personality guide human social learning,” was authored by Tiarn Burtenshaw, Bradley Walker, Gilles Gignac, Cyril C. Grueter, and Nicolas Fay.