We Arrive at Negative Conclusions More Easily Under Threat

Post by D. Chloe Chung

What's the science?

We accumulate information over time to make important decisions, sometimes even in highly stressful situations. As there is often endless information available to us, we need to decide when to stop gathering information in order to make judgments. However, under stressful, threatening conditions, we are prone to make decisions even with a small amount of information (e.g. hearing a faint sound in a dark alleyway, perceiving it as a threat, and concluding that the environment is dangerous). This week in the Journal of Neuroscience, Globig and colleagues investigated how we process information in threatening situations.

How did they do it?

A total of 83 participants were divided into a threat manipulation group and a control group. The threat manipulation group was informed that they would later have to deliver a speech on an undisclosed topic in front of judges. Then, they were asked to solve difficult math problems in a limited time. These anticipated threats were designed to increase the anxiety and stress levels in participants. On the contrary, the control group was told that they would later have to write a short essay on a random topic that would not be judged and were given easier math problems to solve. After the manipulation, both groups played the “Factory Game”: Participants had to determine whether they were in a telephone or television factory based on the number of telephones or televisions shown on a moving conveyor belt that they were observing. During this test, for each participant, one type of factory was randomly assigned as the “desirable” factory and the other one as the “undesirable” factory. Participants were told that they would earn points when they visit the desirable factory but lose points when they visit the undesirable factory. They were also told that they would earn points when making a correct judgment about the factory type but would lose points upon a wrong judgement. The two payments were independent of each other.

What did they find?

After first checking that the level of anxiety was successfully increased in the “threat manipulation group” participants, the authors observed that the threat manipulation group tended to determine that they were in the undesirable factory (for example, the television factory) after observing a smaller proportion of undesirable items (televisions) compared to the control group. This means that with perceived threats, participants were more likely to draw conclusions based on less evidence, but only when drawing conclusions about the undesirable condition specifically. When it came to the desirable factory, the presence of perceived threats did not change the amount of evidence required to draw conclusions about the desirability of the situation. To understand how threat affects evidence accumulation specifically the authors used a computational modelling approach, finding a higher relative rate for negative evidence in the threat manipulation group. They found that threat biases the way in which participants weigh valenced (positive or negative) information.

What’s the impact?

This work confirms that the way we collect and process evidence can be greatly impacted by threats present in our situation, suggesting that we draw conclusions about our undesirable situation quickly, and with little evidence. Findings from this study suggest that, for those who are more sensitive to threats due to anxiety or other mood disorders, this process of accumulating negative information faster could be harmful as it may lead to an overly negative assessment of their situation.

Globig et al. Under threat weaker evidence is required to reach undesirable conclusions. Journal of Neuroscience (2021).Access the original scientific publication here.

Predicting Preference for Art Through Low- and High-Level Features

Post by Leanna Kalinowski

What's the science?

We are surrounded by visual art, from classic paintings in a museum to photographs on social media. While navigating through this art-filled world, we constantly make judgements about whether we like or dislike a particular piece. However, the process by which we perceive art is unclear. Do prior experiences with certain features of the piece of art shape our preferences, or are the visual properties of an image more important? The answer is that both are likely important. Computational methods have previously been applied to tease apart how we develop different preferences. However, in the case of visual art, this process is much more challenging due to the visual complexity and variation of some art. This week in Nature Human Behavior, Iigaya and colleagues developed and tested a computational framework to investigate how preferences for visual art are formed.

How did they do it?

The authors first divided the properties of an image into two categories: ‘low-level’ and ‘high-level’. ‘Low-level’ (i.e., bottom-up) features included those derived from an image’s statistics and visual properties, such as hue and brightness, while ‘high-level’ (i.e., top-down) features included those that require human judgement, such as realism and emotion. Participants were asked to report how much they liked various paintings and photographs on a four-point scale, and the authors used these ratings to determine the extent to which they could predict art preferences. They also applied machine learning: a deep convolutional neural network (DCNN) that had been trained for object recognition to predict the pattern by which these visual features emerge when the brain processes visual images.

What did they find?

By engineering a linear feature summation (LFS) model, the authors first observed that visual preference for art can be predicted through a combination of low- and high-level features. This model predicted preferences for both paintings and photographs, suggesting that the features used for driving visual preferences may be universal across different mediums. They also found that their model may represent a biologically plausible computation, as their DCNN model mirrored the results from the LFS model above. Specifically, when the authors did not specify certain features for the DCNN as they did with the LFS model, they found that the DCNN model could learn to predict all of those features on its own.

What's the impact?

The findings here uncover a mechanism through which art preferences can be predicted, shedding light on how these preferences are formed in the brain. These tools have the potential to influence the arts and media industry by predicting which works of art may be more likely to be preferred, and could be extended to predict judgements and perceptions beyond art.   

Iigaya et al. Aesthetic preference for art can be predicted from a mixture of how- and high-level visual features. Nature Human Behaviour (2021) Access the original scientific publication here.

Twitter Behaviour is Related to Reflective Thinking

Post by D. Chloe Chung

What's the science?

Many of us are using at least one type of social media platform to help us connect with others and discuss important social issues. Despite these benefits, social media can also be misused to easily spread false information and fuel political polarization. Given the power of social media, several studies have looked at how personalities or demographic characteristics are related to the different ways people use social media. Adding to this line of research, this week in Nature Communications, Mosleh and colleagues examined how people’s cognitive style is associated with their behaviour on Twitter.

How did they do it?

The authors recruited approximately 2,000 people who regularly use Twitter, mostly from the United Kingdom and the United States. These participants took the Cognitive Reflection Test (CRT), which measures people’s tendency to follow their instinct and choose wrong answers. Specifically, this test measures one’s ability to perform self-reflective thinking to find correct answers while suppressing a “gut feeling” related to an incorrect response. A higher CRT score indicates that the participant is better at cognitive reflection. Next, the authors collected several pieces of information related to the Twitter activity of the participants, such as how many accounts they follow and what type of content they have recently tweeted. They also gathered demographic information about the participants including their education, political ideology, religion, and income. Based on these data, the authors created a co-follower network to examine what type of accounts are followed by participants who share similar CRT scores.

What did they find?

First, the authors focused on examining the content the participants consume on Twitter. The authors observed that Twitter users with higher CRT scores (i.e. more reflective thinking) showed a tendency to follow fewer Twitter accounts. From the co-follower network analysis, the authors found a distinct division in the types of Twitter accounts followed by people with higher and lower CRT scores, suggesting that critical thinking is reflected in an individual’s account-following behaviour on Twitter. Interestingly, there was a group of accounts followed by people with lower CRT scores (i.e. less reflective thinking), supporting the notion of “cognitive echo chambers,” in which people tend to interact with those who share similar ideologies. The authors analyzed the type of content participants tended to tweet and found that the degree of reflective thinking was associated with the quality of information shared. Specifically, people who think more reflectively were more likely to share higher-quality news from reliable sources, while people who engage less in reflective thinking shared political misinformation and scams more often. Upon analyzing individual words in participants’ tweets, the authors observed that more reflective people tended to use words related to morality, insight, and inhibition, which may indicate that these people are more likely to inhibit their instincts by engaging in analytical thinking.

What’s the impact?

This study demonstrates how different cognitive styles can be reflected in our behaviour on Twitter. In particular, this work shows what can drive the dissemination of misinformation on social media. In contrast to the “intuitionist” perspective that emphasizes the importance of intuition in everyday behaviours, findings from this study suggest that reflective or analytic thinking plays a crucial role in our day-to-day judgment on social media. It will be interesting to investigate whether these findings can be also applied to other social media platforms.

Mosleh et al. Cognitive reflection correlates with behavior on Twitter. Nature Communications (2021). Access the original scientific publication here.