The research team used six different questionnaires to determine the extent to which the participants had a materialistic attitude and tended to compare themselves with others, whether they used social media more actively or passively, whether they were addicted to social media, how stressed and how satisfied they were with their lives.

Downward spiral set in motion

“The data showed that a stronger materialistic approach goes hand in hand with a tendency to compare oneself with others,” points out Phillip Ozimek. This comparison is very easy to make on social media, primarily through passive use, i.e. by looking at the content posted by other users. Materialism and passive use were also linked to addictive use of social media. “By this we mean, for example, that users are constantly thinking about the respective channels and fear that they are missing out on something if they are not online,” explains Phillip Ozimek. This in turn leads to symptoms of poorer mental health, i.e. stress. The final link in the chain is reduced life satisfaction. “Social media is one of six stepping stones to unhappiness,” concludes Phillip Ozimek.

Social media attracts and breeds materialists

“Overall, the study provides further evidence that the use of social media is associated with risks, especially for people with a highly materialistic mindset,” says the psychologist. This is particularly worrying, because social media can trigger and increase materialistic values, for example through (influencer) marketing. At the same time, the platforms attract materialists anyway, as they are a perfect way to satisfy many materialistic needs.

“It’s definitely a good idea to be aware of the amount of time you spend on social media and to reduce it,” recommends Phillip Ozimek. He advises against giving up Social Media completely. “If you did, you’re likely to overcompensate.” The research team also suggests recording materialism and social media use in patients undergoing treatment for mental health disorders. “While these factors are often irrelevant, they can be a starting point for additional interventions that patients can try out at home.”

The study, published in Nature Human Behaviour and funded by Wellcome, uses an AI computational model — known as a generative neural network — to simulate how neural networks in the brain learn from and remember a series of events (each one represented by a simple scene).

The model featured networks representing the hippocampus and neocortex, to investigate how they interact. Both parts of the brain are known to work together during memory, imagination and planning.

Lead author, PhD student Eleanor Spens (UCL Institute of Cognitive Neuroscience), said: “Recent advances in the generative networks used in AI show how information can be extracted from experience so that we can both recollect a specific experience and also flexibly imagine what new experiences might be like.

“We think of remembering as imagining the past based on concepts, combining some stored details with our expectations about what might have happened.”

Humans need to make predictions to survive (e.g. to avoid danger or to find food), and the AI networks suggest how, when we replay memories while resting, it helps our brains pick up on patterns from past experiences that can be used to make these predictions.

Researchers played 10,000 images of simple scenes to the model. The hippocampal network rapidly encoded each scene as it was experienced. It then replayed the scenes over and over again to train the generative neural network in the neocortex.

The neocortical network learned to pass the activity of the thousands of input neurons (neurons that receive visual information) representing each scene through smaller intermediate layers of neurons (the smallest containing only 20 neurons), to recreate the scenes as patterns of activity in its thousands of output neurons (neurons that predict the visual information).

This caused the neocortical network to learn highly efficient “conceptual” representations of the scenes that capture their meaning (e.g. the arrangements of walls and objects) — allowing both the recreation of old scenes and the generation of completely new ones.

Consequently, the hippocampus was able to encode the meaning of new scenes presented to it, rather than having to encode every single detail, enabling it to focus resources on encoding unique features that the neocortex couldn’t reproduce — such as new types of objects.

The model explains how the neocortex slowly acquires conceptual knowledge and how, together with the hippocampus, this allows us to “re-experience” events by reconstructing them in our minds.

The model also explains how new events can be generated during imagination and planning for the future, and why existing memories often contain “gist-like” distortions — in which unique features are generalised and remembered as more like the features in previous events.

Senior author, Professor Neil Burgess (UCL Institute of Cognitive Neuroscience and UCL Queen Square Institute of Neurology), explained: “The way that memories are re-constructed, rather than being veridical records of the past, shows us how the meaning or gist of an experience is recombined with unique details, and how this can result in biases in how we remember things.”

According to Freya Mowat, veterinary ophthalmologist and professor in the School of Veterinary Medicine’s department of surgical sciences, researchers wanted to determine factors, including age and vision, that influence a dog’s interest in interacting with video content. Ultimately, the goal of the study, which launched two years ago, was to support development of more sensitive ways to assess canine vision — something that has been sorely lacking in veterinary medicine.

“The method we currently use to assess vision in dogs is a very low bar. In humans, it would be equivalent to saying yes or no if a person was blind,” says Mowat. “We need more sensitive ways to assess vision in dogs, using a dog eye chart equivalent. We speculate that videos have the potential for sustaining a dog’s attention long enough to assess visual function, but we didn’t know what type of content is most engaging and appealing to dogs.”

Published recently in the journal Applied Animal Behaviour Science, the study found that dogs are most engaged when watching videos that feature other animals. Content featuring other dogs was the most popular. But if a National Geographic documentary about canine evolution seems too highbrow for your four-legged friend, Scooby Doo might be a perfectly acceptable option as well.

To better understand the type of content dogs might be most attracted to on screen, Mowat created a web-based questionnaire for dog owners around the globe to report the TV-watching habits of their canine companions.

Participants responded to questions about the types of screens in their homes, how their dogs interacted with screens, the kinds of content their dogs interacted with the most, as well as information about their dog’s age, sex, breed and where they live. They also provided descriptions of their dogs’ behavior when watching videos. Most commonly, dog owners described their pets’ behavior as active — including running, jumping, tracking action on screen and vocalizing — compared with passive behaviors like lying down or sitting. Dog owners also had the option to show their dog(s) four short videos featuring subjects of possible interest, including a panther, a dog, a bird and traffic moving along a road. They were then asked to rate their dog’s interest in each video and how closely the dog tracked the moving objects on the screen.

Mowat received 1,600 responses from dog owners across the world, including from the United States, Canada, the United Kingdom, the European Union and Australasia. Of those respondents, 1,246 ultimately completed the study. The following are some of the most interesting highlights:

• Age and vision were related to how much a dog interacted with a screen.
• Sporting and herding dog breeds appear to watch all content more than other breeds.
• Video content featuring animals was the most popular, with other dogs being by far the most engaging subjects to watch.
• Humans do not appear to be very appealing for dogs to watch, ranking ninth out of 17 predetermined categories.
• Cartoons were engaging for more than 10% of dogs.
• Movement on screens was a strong motivator for screen attention.

Mowat says she plans to build on the results of this study. Future research will focus on the development and optimization of video-based methods that can assess changes in visual attention as dogs age as well as answer questions that could help our four-legged friends age as gracefully as possible.

“We know that poor vision negatively impacts quality of life in older people, but the effect of aging and vision changes in dogs is largely unknown because we can’t accurately assess it,” she says. “Like people, dogs are living longer, and we want to make sure we support a healthier life for them as well.”

Another goal for Mowat is to compare how a dogs’ vision ages compared with the human or humans they share a home with.

“Dogs have a much shorter lifespan than their owner, of course, and if there are emerging environmental or lifestyle factors that influence visual aging, it might well show up in our dogs decades before it shows up in us,” she explains. “Our dogs could be our sentinels — the canine in the proverbial coal mine.”

Part of the immune system active for too long

A team of researchers led by Onur Boyman, professor of immunology at the University of Zurich (UZH) and Director of the Department of Immunology at the University Hospital Zurich (USZ), has shown in a study that the complement system plays an important role in Long Covid. It is part of the innate immune system and normally helps to fight infections and eliminate damaged and infected body cells. “In patients with Long Covid, the complement system no longer returns to its basal state, but remains activated and, thus, also damages healthy body cells,” says Boyman.

Continued activation of complement system damages tissue and blood cells

The researchers followed 113 COVID-19 patients for up to one year after their acute SARS-CoV-2 infection and compared them with 39 healthy controls. After six months, 40 patients had active Long Covid disease. More than 6,500 proteins in the blood of the study participants were analyzed both during the acute infection and six months later. “The analyses of which proteins were altered in Long Covid confirmed the excessive activity of the complement system. Patients with active Long Covid disease also had elevated blood levels indicating damage to various body cells, including red blood cells, platelets and blood vessels,” explains Carlo Cervia-Hasler, a postdoctoral researcher in Boyman’s team and first author of the study.

Bioinformatics recognizes protein patterns

The measurable changes in blood proteins in active Long Covid indicate an interaction between proteins of the complement system, which are involved in blood clotting and the repair of tissue damage and inflammation. In contrast, the blood levels of Long Covid patients who recovered from the disease returned to normal within six months. Active Long Covid is therefore characterized by the protein pattern in the blood. The blood markers were discovered using bioinformatics methods in collaboration with Karsten Borgwardt during his time as a professor at ETH Zurich.

Improved diagnostics and new therapeutic approaches

“Our work not only lays the foundation for better diagnosis, but also supports clinical research into substances that could be used to regulate the complement system. This opens up new avenues for the development of more targeted therapies for patients with Long Covid,” summarizes Onur Boyman.