Social learning research has found that people with high intelligence are more likely to switch to a new solution when it becomes available, especially if that solution is better than the existing one. High openness to experience was also associated with switching to new solutions, but especially to solutions that were of equal or lower quality than the existing solution. This research personality and individual differences.
The ability to learn from others is an important trait for human evolutionary success. This ability is called social learning. It allows humans to acquire knowledge, behaviors, attitudes, and skills by observing and interacting with other people. People can learn by observing the actions of others and noticing the consequences of those actions. For example, a child may learn how to behave in the classroom by observing classmates and teachers.
Social learning also involves imitating behaviors that are thought to bring rewards and avoiding behaviors that lead to negative outcomes. Parents, peers, teachers, peers, and members of the media can all serve as models for learning. This process does not occur automatically, as humans interpret what they observe and decide whether an action is relevant or appropriate. Social learning plays an important role in the development of social norms, values, language, and daily habits. It can propagate both beneficial behaviors, such as cooperation, and harmful behaviors, such as aggression and prejudice.
Study author Tiaan Bartenshaw and his colleagues conducted two studies. The first study investigated the extent to which individual differences in intelligence and openness to experience influence human social learning. They investigated the extent to which a person’s decision to switch (or not switch) to a new solution to a problem depends on these two characteristics. The new solutions investigated were better, worse, or equivalent to existing solutions.
The study participants were 201 first year undergraduate students from the University of Western Australia and 369 participants recruited from the UK through Prolific. Prolific participants were paid £5 for their participation. The average age of participating college students was 20 to 21 years old, while the average age of Prolific participants was 44 to 45 years old.
Study participants were trained to solve two behavioral tasks: a padlock task and a maze task. In the padlock task, participants learned how to open a treasure chest to win a “prize” (as the study authors put it, “an obviously counterfeit $3 coin”). To open the chest, we had to open four padlocks. Three padlocks were opened during training, and for the fourth padlock, participants were given the choice of using the trained solution or a new solution.
Each solution to the padlock task was a seven-digit padlock combination that participants had to memorize and enter by clicking a numbered button. Here, the quality of the solution was determined by the number of times participants had to change buttons when entering a combination. They range from very inefficient solutions where 6 digits are different from the previous digits to efficient solutions where all digits are the same.
In the maze task, participants’ task was to navigate the maze in a taxi. They had to travel through each maze four times. Three times during training and once when choosing between the trained solution and the new solution. In this task, the quality of the solution depended on the length and turns of the route taken by the taxi.
Participants also completed an assessment of intelligence (using four subtests: Advanced Vocabulary Test, Raven’s Advanced Progressive Matrix, Letter Number Sequencing Task, and Connectivity Test) and personality assessment (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 received one, three, or six rounds of training on each trial before being presented with a new solution. Participants were 90 first-year undergraduate students from the University of Western Australia. Of these, 78 percent were women.
The results showed that participants were more likely to switch to a new solution that was better than the one they were trained to use. However, if the quality of the solution they were trained to use and the new solution are equal, participants are more likely to use the solution they were trained to use.
People with higher intelligence were more likely to switch to new solutions. This trend was especially strong when the new solution outperformed the trained solution. High openness to experience (a personality trait that describes a tendency to be curious, imaginative, creative, and interested in new ideas and experiences) was also associated with a tendency to switch to new solutions, but their quality was comparable to or worse than that learned by solution study participants.
Study 2 showed that longer training with a solution decreased the likelihood of switching to a new solution. As the study authors note, familiarity with the trained solution increases “conservative bias” and reduces social learning.
“Our findings demonstrate that individual differences in intelligence and personality, along with experiential factors, are important for human social learning,” the study authors concluded.
This study sheds light on the nuances of human social learning. However, it should be noted that this experiment was conducted for two relatively short tasks in which the choice of solution had no practical relevance for the individual participants. Studies examining social learning behavior in situations where solution choices have relevant real-world consequences for participants may yield different results.
The paper, “Individual differences in intelligence and personality guide human social learning,” was authored by Tiaan Burtenshaw, Bradley Walker, Jill Gignac, Cyril C. Gruter, and Nicholas Fay.
