Brain learns to recognize familiar faces regardless of where they are in the visual field

November 08, 2018

A Dartmouth study finds that recognition of faces varies by where they appear in the visual field and this variability is reduced by learning familiar faces through social interactions. These biases are stable and idiosyncratic. More importantly, these biases are reduced for more familiar identities suggesting that the brain recognizes personally familiar faces more uniformly across the visual field. The findings suggest that repeated social interactions may tune populations of visual neurons in the face processing network to enable consistent and rapid recognition of familiar faces. The study was published in eNeuro, an open-access journal of the Society for Neuroscience.

Prior research in human facial recognition has often focused on how people perceive unfamiliar faces but this is one of the first to examine how early visual processes may be tuned by regular, social interactions with others to optimize one's ability to recognize faces of people who are important to us.

"For many of us, we spend most of our time with people we know, so understanding the underlying brain activity that enables us to recognize our friends, family, colleagues and peers, is essential to learning more about how we process relevant social stimuli," said senior author Maria Gobbini, an associate professor of psychological and brain sciences at Dartmouth.

To understand how the brain processes personally familiar faces across different retinal locations, study participants (graduate students) were asked to identify either two or three photographs of their peers' faces. As participants stared at a central red dot on a computer screen, an image of a peer's face would flash briefly on the screen peripherally in one of eight locations. After the image disappeared, they were prompted to identify which person they saw. Following the experiment, participants were asked to rate how well they knew the person in the image on a scale of one (not close) to seven (very close). The team then ran computational simulations to test the effect of learning in face-responsive cortical areas. The results of the simulation suggest that early face areas in face processing pathways are more likely to show a visual field bias that can be tuned by learning.

Previous research in facial recognition and identity has shown that the perception of gender and age varies across retinal locations as well. For example, an androgynous face may appear as a female face when shown in a specific visual location and as a male face when shown in another location. Gobbini and colleagues' new study found that this same type of variability across the visual field (idiosynchratic, retinotopic biases) is found for identification of faces that are low in familiarity but is reduced for highly familiar faces. The results suggest that personally familiar faces may be detected in a prioritized way at an early stage of visual processing. "Much in the same way that the human language system is adapted and optimized to process an individual's native language, including auditory recognition of speech sounds, the face perception system is finely tuned in each individual for interaction with the people that play an important role in that person's life, and this tuning extends to learning at early stages of visual processing," concludes Gobbini.
Maria (Ida) Gobbini is available for comment at:

Dartmouth College

Related Learning Articles from Brightsurf:

Learning the language of sugars
We're told not to eat too much sugar, but in reality, all of our cells are covered in sugar molecules called glycans.

When learning on your own is not enough
We make decisions based on not only our own learning experience, but also learning from others.

Learning more about particle collisions with machine learning
A team of Argonne scientists has devised a machine learning algorithm that calculates, with low computational time, how the ATLAS detector in the Large Hadron Collider would respond to the ten times more data expected with a planned upgrade in 2027.

Getting kids moving, and learning
Children are set to move more, improve their skills, and come up with their own creative tennis games with the launch of HomeCourtTennis, a new initiative to assist teachers and coaches with keeping kids active while at home.

How expectations influence learning
During learning, the brain is a prediction engine that continually makes theories about our environment and accurately registers whether an assumption is true or not.

Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.

Learning is optimized when we fail 15% of the time
If you're always scoring 100%, you're probably not learning anything new.

School spending cuts triggered by great recession linked to sizable learning losses for learning losses for students in hardest hit areas
Substantial school spending cuts triggered by the Great Recession were associated with sizable losses in academic achievement for students living in counties most affected by the economic downturn, according to a new study published today in AERA Open, a peer-reviewed journal of the American Educational Research Association.

Lessons in learning
A new Harvard study shows that, though students felt like they learned more from traditional lectures, they actually learned more when taking part in active learning classrooms.

Learning to look
A team led by JGI scientists has overhauled the perception of inovirus diversity.

Read More: Learning News and Learning Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to