Smarter Decisions: New IQ Research Reveals Why Higher Intelligence Leads to Better Predictions and Outcomes

A groundbreaking study from the University of Bath’s School of Management has shed new light on the connection between intelligence quotient (IQ) and decision-making. The research, published in the Journal of Personality and Social Psychology, found that individuals with higher IQs are more accurate in their predictions, which can lead to improved life outcomes.

The study analyzed data from a nationally representative sample of people over 50 in England, assessing their ability to predict their own life expectancy. Participants were asked to estimate their probability of living to certain ages, and these estimates were compared with the probabilities taken from Office for National Statistics life tables.

To control for lifestyle, health, and genetic longevity factors, researchers used cognitive test scores and genetic markers linked to intelligence and educational success. The study revealed that individuals with higher IQs tend to have more accurate beliefs about uncertain future events – they are better at assessing probability.

The results showed that people with a lower IQ made forecasting errors that were more than twice as inaccurate as those made by people with a high IQ. Individuals with higher IQs also demonstrated more consistent judgment and made fewer errors, both positive and negative.

“Accurately assessing the probability of good and bad things happening to us is central to good decision-making,” said Professor Chris Dawson, lead author of the study. “This research highlights one possible channel through which people with lower IQs do worse on various outcomes.”

Professor Dawson suggests that explicitly stating probability estimates on information related to health and finance could help individuals prone to forecasting errors make more informed decisions.

“I found that certain genetic traits linked to intelligence and education are associated with more accurate predictions, suggesting that lower cognitive ability may causally contribute to the formation of more biased assessments,” said Professor Dawson. “Probability estimation is the most important aspect of decision-making, and people who struggle with this are at a distinct disadvantage.”

The study’s findings have significant implications for personal finance, health, and economic growth. Poorly calibrated expectations can lead to bad financial decisions, reduced economic welfare, and adverse effects on national growth.

As Professor Dawson notes, “Expectations about the future shape how households make critical decisions – like how much to save, when to retire, or whether to invest.” By recognizing the importance of accurate probability estimation in decision-making, individuals and policymakers can take steps to improve outcomes and promote more informed choices.

The human brain is a masterful machine that makes decisions based on associations between stimuli in our environment. But did you know that these decisions can also be influenced by indirect associations between seemingly unrelated events? A recent study by the Cellular Mechanisms in Physiological and Pathological Behavior Research Group at the Hospital del Mar Research Institute has shed new light on this process, revealing how a specific scent can alter our mind’s decision-making processes.

The research team, led by PhD student José Antonio González Parra and supervised by Dr. Arnau Busquets, conducted experiments with mice to understand the mechanisms behind indirect associations between different stimuli. They trained the mice to associate two distinct smells – banana and almond – with sweet and salty tastes respectively. Later, a negative stimulus was linked to the smell of banana, causing the mice to reject the sweet taste associated with it.

The researchers used genetic techniques to observe which brain areas were activated throughout this process. They found that the amygdala, a region linked to responses such as fear and anxiety, played a crucial role in encoding and consolidating these associations. Other brain areas also interacted with the amygdala, forming a brain circuit that controls indirect associations between stimuli.

Dr. Busquets explained that if amygdala activity was inhibited while the mice were exposed to the stimuli, they were unable to form these indirect associations. This finding has significant implications for treating mental disorders linked to amygdala activity, such as PTSD and psychosis.

The researchers believe that the brain circuits involved in decision-making processes in humans are similar to those in mice. Therefore, understanding these complex cognitive processes can help us design therapeutic strategies for humans, including brain stimulation or modulation of activity in specific areas.

In conclusion, this study has revealed how a scent can change our mind’s decisions by altering indirect associations between stimuli. By exploring the neural mechanisms behind this process, we may be able to develop innovative treatments for mental disorders that affect millions of people worldwide.

The human brain is incredibly skilled at solving complicated problems. One reason for this is that humans can break down complex tasks into manageable subtasks that are easy to solve one at a time. This strategy helps us handle obstacles easily, as shown by the example of going out for coffee, where we can revise how we get out of the building without changing the other steps.

While there’s a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it’s been difficult to devise experimental scenarios that allow precise characterization of the computational strategies used to solve problems. A new study by MIT researchers has successfully modeled how people deploy different decision-making strategies to solve a complicated task – in this case, predicting how a ball will travel through a maze when the ball is hidden from view.

The human brain cannot perform this task perfectly because it’s impossible to track all possible trajectories in parallel, but the researchers found that people can perform reasonably well by flexibly adopting two strategies known as hierarchical reasoning and counterfactual reasoning. The researchers were also able to determine the circumstances under which people choose each of those strategies.

“Weak humans are capable of doing is breaking down the maze into subsections, and then solving each step using relatively simple algorithms,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences at MIT. “When we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done.”

The researchers recruited about 150 human volunteers to participate in the study and evaluated how accurately they could estimate timespans of several hundred milliseconds. For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant if they were running parallel simulations, using hierarchical reasoning alone, counterfactual reasoning alone, or combinations of the two reasoning strategies.

The researchers compared the subjects’ performance with the models’ predictions and found that for every subject, their performance was most closely associated with a model that used hierarchical reasoning but sometimes switched to counterfactual reasoning. This suggests that instead of tracking all possible paths that the ball could take, people broke up the task into smaller subtasks, picked the direction in which they thought the ball turned at the first junction, and continued to track the ball as it headed for the next turn.

If the timing of the next sound they heard wasn’t compatible with the path they had chosen, they would go back and revise their first prediction – but only some of the time. Switching back to the other side represents a shift to counterfactual reasoning, which requires people to review their memory of the tones that they heard.

The researchers found that people decided whether to go back or not based on how good they believed their memory to be. “People rely on counterfactuals to the degree that it’s helpful,” Jazayeri says. “People who take a big performance loss when they do counterfactuals avoid doing them. But if you’re someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

The research was funded by various organizations, including the Lisa K. Yang ICoN Fellowship, the Friends of the McGovern Institute Student Fellowship, and the National Science Foundation Graduate Research Fellowship.

By slightly varying the amount of memory impairment programmed into the models, the researchers also saw hints that the switching of strategies appears to happen gradually, rather than at a distinct cut-off point. They are now performing further studies to try to determine what is happening in the brain as these shifts in strategy occur.