New! Sign up for our free email newsletter.
Science News
from research organizations

Attention to angry faces can predict future depression

Date:
June 16, 2015
Source:
Binghamton University
Summary:
Up to 80 percent of individuals with a past history of depression will get depressed again in the future. However, little is known about the specific factors that put these people at risk. New research suggests that it may be due to the things you pay attention to in your life.
Share:
FULL STORY

Up to 80 percent of individuals with a past history of depression will get depressed again in the future. However, little is known about the specific factors that put these people at risk. New research suggests that it may be due to the things you pay attention to in your life.

Researchers at Binghamton University recruited 160 women -- 60 with a past history of depression, 100 with no history of depression. They showed each woman a series of two faces, one with a neutral expression and the other with either an angry, sad or happy expression. Using eye-tracking, they found that women with a past history of depression paid more attention to the angry faces. More importantly, among women with a history of prior depression, those who tended to look the most at the angry faces were at greatest risk for developing depression again over the next two years.

"If you're walking around day to day, your attention will just be drawn to certain things and you'll tend to look at some things more than others. What we showed is if your attention is drawn to people who appear to be angry with you or critical of you, then you're at risk for depression," said Brandon Gibb, professor of psychology at Binghamton University and director of the Mood Disorders Institute and Center for Affective Science.

"I think the most interesting thing about this is that we followed these women for two years, and the women who are paying attention to angry faces are the most likely to become depressed again, and they become depressed in the shortest amount of time. So they're at greatest risk," said graduate student and lead author of the study Mary Woody. "We might be able to identify women who are at greatest risk for future depression just by something as simple as how they pay attention to different emotional expressions in their world."

To address these types of attentional biases, computer programs and games are being used to retrain peoples' attention. This approach has shown promise in the treatment of anxiety and is now being tested as a treatment for depression. Woody said that, by showing the important role that attentional biases play in depression risk, this new research highlights the promise of these types of attention retraining programs.

"It's a very important first step in developing a new line of treatment for people who are at risk for depression and for who currently have depression," Woody said.

"Some people might be able to use this instead of traditional therapy or could use it as an adjunct to traditional treatment," Gibb added.


Story Source:

Materials provided by Binghamton University. Note: Content may be edited for style and length.


Journal Reference:

  1. M. L. Woody, M. Owens, K. L. Burkhouse, B. E. Gibb. Selective Attention Toward Angry Faces and Risk for Major Depressive Disorder in Women: Converging Evidence From Retrospective and Prospective Analyses. Clinical Psychological Science, 2015; DOI: 10.1177/2167702615581580

Cite This Page:

Binghamton University. "Attention to angry faces can predict future depression." ScienceDaily. ScienceDaily, 16 June 2015. <www.sciencedaily.com/releases/2015/06/150616114237.htm>.
Binghamton University. (2015, June 16). Attention to angry faces can predict future depression. ScienceDaily. Retrieved March 28, 2024 from www.sciencedaily.com/releases/2015/06/150616114237.htm
Binghamton University. "Attention to angry faces can predict future depression." ScienceDaily. www.sciencedaily.com/releases/2015/06/150616114237.htm (accessed March 28, 2024).

Explore More

from ScienceDaily

RELATED STORIES