In the experiment, participants were shown images of human faces while having EEG electrodes on the heads.
HUMAN-COMPUTER INTERACTION
A research team from the University of Copenhagen and University of Helsinki demonstrates it is possible to predict individual preferences based on how a person’s brain responses match up to others. This could potentially be used to provide individually-tailored media content — and perhaps even to enlighten us about ourselves.
We have become accustomed to online algorithms trying to guess our preferences for everything from movies and music to news and shopping. This is based not only on what we have searched for, looked at, or listened to, but also on how these activities compare to others. Collaborative filtering, as the technique is called, uses hidden patterns in our behavior and the behavior of others to predict which things we may find interesting or appealing.
But what if the algorithms could use responses from our brain rather than just our behavior? It may sound a bit like science fiction, but a project combining computer science and cognitive neuroscience showed that brain-based collaborative filtering is indeed possible. By using an algorithm to match an individual’s pattern of brain responses with those of others, researchers from the University of Copenhagen and the University of Helsinki were able to predict a person’s attraction to a not-yet-seen face.
Previously the researchers had placed EEG electrodes onto the heads of study participants and showed them images of various faces, and thereby demonstrated that machine learning can use electrical activity from the brain to detect which faces the subjects found most attractive.
“Through comparing the brain activity of others, we’ve now also found it possible to predict faces each participant would find appealing prior to seeing them. In this way, we can make reliable recommendations for users – just as streaming services suggest new films or series based on the history of the users,” explains senior author Dr. Tuukka Ruotsalo of the University of Copenhagen’s Department of Computer Science.
Towards mindful computing and greater self-awareness
Industries and service providers are more and more often giving personalized recommendations and we are now starting to expect individually tailored content from them. Consequently, researchers and industries are interested in developing more accurate techniques of satisfying this demand. However, the current collaborative filtering techniques which are based on explicit behaviour in terms of ratings, click behaviour, content sharing etc. are not always reliable methods of revealing our real and underlying preferences.
“Due to social norms or other factors, users may not reveal their actual preferences through their behaviour online. Therefore, explicit behaviour may be biased. The brain signals we investigated were picked up very early after viewing, so they are more related to immediate impressions than carefully considered behaviour,” explains co-author Dr. Michiel Spapé.
“The electrical activity in our brains is an alternative and rather untapped source of information. In the longer term, the method can probably be used to provide much more nuanced information about people’s preferences than is possible today. This could be to decode the underlying reasons for a person’s liking of certain songs – which could be related to the emotions that they evoke,” explains Tuukka Ruotsalo.
But researchers don’t just see the new method as a useful way for advertisers and streaming services to sell products or retain users. As lead author Keith Davis points out:
“I consider our study as a step towards an era that some refer to as “mindful computing”, in which, by using a combination of computers and neuroscience techniques, users will be able to access unique information about themselves. Indeed, Brain-Computer Interfacing as it is known, could become a tool for understanding oneself better.”
Nevertheless, there is still a way to go before the technique can be applied beyond the laboratory. The researchers point out that brain-computer interface devices must become cheaper and easier to use before they find themselves in the hands or strapped to the heads of casual users. Their best guess is that this will take at least 10 years.
The researchers also underscore that the technology comes with a significant challenge for protecting brain-based data from misuse and that it is important for the research community to carefully consider data privacy, ownership and the ethical use of raw data collected by EEG.
Original Article: Computers can now predict our preferences directly from our brain
More from: University of Copenhagen | University of Helsinki
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Brain-based collaborative filtering
- Nanobiotechnology-Based Strategies for Crossing the Blood–Brain Barrier
Nanobiotechnology-Based Delivery of Therapeutics for Brain Tumors Across the BBB The application of anticancer drugs and gene therapy for malignant brain tumors may involve direct introduction ...
- How does the brain focus with so many distractions?
Ritz explained the collaborative effort between two brain ... one for filtering - while the anterior cingulate cortex monitors task performance and directs adjustments to these dials based on ...
- Psychosis Development Linked to Brain’s Filter/Predictor Mechanisms
One is a “filter” that directs ... and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study,” in ...
- Neuromorphic and Brain-Based Robots
Neuromorphic and brain-based robotics have enormous potential for furthering our understanding of the brain. By embodying models of the brain on robotic platforms, researchers can investigate the ...
- Optical Brain Imaging
All biological tissue is, to differing extents, permeable to electromagnetic (EM) radiation of different frequencies and intensities. This can also be considered as permeability of biological tissue ...
Go deeper with Google Headlines on:
Brain-based collaborative filtering
[google_news title=”” keyword=”brain-based collaborative filtering” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]
Go deeper with Bing News on:
Mindful computing
- Upwork Reports First Quarter 2024 Financial Results
SAN FRANCISCO, May 01, 2024 (GLOBE NEWSWIRE) -- Upwork Inc. (Nasdaq: UPWK), the world’s largest work marketplace that connects businesses with independent talent from across the globe, ...
- VR environment for teens may offer an accessible, affordable way to reduce stress
Social media. The climate crisis. Political polarization. The tumult of a pandemic and online learning. Teens today are dealing with unprecedented stressors, and over the past decade, their mental ...
- Former Google workers fired for protesting Israel deal file complaint claiming protected speech
Dozens of former Google workers filed a complaint with the US National Labor Relations Board on Tuesday after they were fired or placed on administrative leave last month for protesting the company’s ...
- Tech roundtable: Power to the people tech
Our panel posits that artificial intelligence isn’t the only technology trend making waves in accounting firms and finance departments.
- In Today’s AI Race, Don’t Gamble with Your Digital Privacy
While the AI age is upon us, we must take our digital privacy seriously. I look into privacy policies of popular AI chatbots to understand how your personal data is handled.
Go deeper with Google Headlines on:
Mindful computing
[google_news title=”” keyword=”mindful computing” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]