Cover Story: Elimination Round or Aptitude Test- How to Align CUET with NEP 2020 Goals  |  Life Inspirations: Master of a Dog House  |  Education Information: Climate Predictions: Is it all a Piffle!  |  Leadership Instincts: Raj Mashruwala Establishes CfHE Vagbhata Chair in Medical Devices at IITH   |  Parent Interventions: What Books Children Must Read this Summer Vacation   |  Rajagiri Round Table: Is Time Ripe for Entrepreneurial Universities in India?  |  Life Inspirations: How to Overcome Fear of Public Speaking  |  Technology Inceptions: Smart IoT-based, indigenously-developed, ICU Ventilator “Jeevan Lite” Launched  |  Parent Interventions: Meditation Reduces Guilt Feeling  |  Teacher Insights: Music Relief for Study Stress  |  Teacher Insights: Guided Play Effective for Children  |  Teacher Insights: Doing Calculations Boosts Mental Strength  |  Best Practices: Hugging for Happiness  |  Parent Interventions: Is Frequent Childcare Outside of the Family Beneficial for a Child's Development  |  Technology Inceptions: How to Prevent the Toxic Effects of Tricloson used in Consumer Products?  |  
March 04, 2022 Friday 12:36:40 PM IST

How to Measure Attention?

Attention is central to many aspects of cognition, but there is no singular neural measure of a person’s overall attentional functioning across tasks.

Using a model of fMRI data collected from 92 individuals performing several types of attention-related tasks, the lab of Yale's Marvin Chun successfully predicted how well those individuals would perform on the tasks based on their brain scans alone. This generalized model can also predict the severity of an individual case of attention deficit and hyperactivity disorder.

The new research makes it possible to put a number on it like heart rate and blood pressure. It was done by analyzing brain scans of individuals as they performed a series of attention-related tasks, such as sustained focus exercises, and then linked that information to patterns of activity across different brain regions. They then created a computational model that is so sensitive it can predict how well an individual will perform on an attention-related task even when the brain is resting. The measurement can help diagnose ADHD and be used as neurofeedback to help improve an individual's own focus.


Comments