How much of this page will you read?
How much will you remember?
And does it make a difference when you’re reading, or where?
Those are the sorts of questions that a University of Chicago neuroscientist asks in an innovative new study – one that examines brain scans to uncover how attention is sustained over time, and when it might fluctuate.
“Maybe in general, we’re pretty good at paying attention, or maybe we struggle – but it’s not the same all the time,” said lead author Monica Rosenberg, an assistant professor in UChicago’s Department of Psychology.
“We wanted to build a model that could predict a person’s attentional state based on what we see in their brain scans.”
Published in the Proceedings of the National Academy of Sciences, the study relies on functional MRI data collected for this study as well as data from previous research, combining the results of 107 individuals from five different data sets.
By using what Rosenberg calls “green science” – replicating results in data collected for other purposes – the study expands its pool of participants beyond what is usually found in a single lab.
The research examines functional MRI scans of people who performed a computerized task multiple times in one day – watching a stream of images and pressing a button in response to some them – as well as those who performed the same task on different days.
It also examines brain scans of those who have been administered anesthesia, as well as 30 scans of a single individual over the course of 10 months.
The participants’ ages ranged from 18 to 56.
“If we want to build brain-based models that are applicable in clinical or translational settings, they have to be able to generalize across data sets,” said Rosenberg, an expert on attention.
“It has to be the case that models don’t just predict behavior from data collected on a single hospital scanner from a single group of individuals.
“If a model can’t predict something about people across different sites and populations, it’s less practically useful.”
Prior research has found that every person has a unique pattern of functional brain connectivity – a sort of fingerprint that can predict their cognitive and attentional abilities.
Using brain scans, a new study proposes a model that can predict when someone is paying closer attention – and when their attention might fluctuate.
Rosenberg and her co-authors – including scholars from Yale University and the University of Florida – tested whether those patterns could extend to predict how a person’s attention changes from moment to moment, or day to day.
They found that patterns of functional brain connectivity reliably predicted when people were more and less focused on the computer task.
These predictions were highly accurate when averaged across many scan sessions.
However, the patterns still predicted attentional state even when measured in short window of time, such as 30 seconds of an fMRI session.
Previous studies have historically used single data sets, due in part to the high cost of fMRI.
“It’s only in the past couple of years that sharing data sets has become much more common,” Rosenberg said. “That’s what gives us access to a wider variety of samples, which allow us to ask how general our models are.”
Rosenberg hopes further research can provide insights into how attention changes over longer periods of time, like development and aging.
She is also in the process of testing whether predictive models can translate to settings outside the lab.
For example, her lab is asking whether patterns of functional brain connectivity can predict attention fluctuations as we listen to a story or watch a movie.
“When we collect brain data in an MRI scanner,” she said, “we often give people psychological tasks that involve seeing pictures and pressing buttons. That’s really not how we navigate the world.”
Our ability to stay focused is limited: prolonged performance of a task typically results in mental fatigue and decrements in performance over time.
This so-called vigilance decrement has been attributed to depletion of attentional resources, though other factors such as reductions in motivation likely also play a role.
In this study, we examined three electroencephalography (EEG) markers of attentional control, to elucidate which stage of attentional processing is most affected by time-on-task and motivation.
To elicit the vigilance decrement, participants performed a sustained attention task for 80 min without breaks.
After 60 min, participants were motivated by an unexpected monetary incentive to increase performance in the final 20 min.
We found that task performance and self-reported motivation declined rapidly, reaching stable levels well before the motivation manipulation was introduced.
Thereafter, motivation increased back up to the initial level, and remained there for the final 20 min. While task performance also increased, it did not return to the initial level, and fell to the lowest level overall during the final 10 min.
This pattern of performance changes was mirrored by the trial-to-trial consistency of the phase of theta (3–7 Hz) oscillations, an index of the variability in timing of the neural response to the stimulus. As task performance decreased, temporal variability increased, suggesting that attentional stability is crucial for sustained attention performance.
The effects of attention on our two other EEG measures – early P1/N1 event-related potentials (ERPs) and pre-stimulus alpha (9–14 Hz) power – did not change with time-on-task or motivation.
In sum, these findings show that the vigilance decrement is accompanied by a decline in only some facets of attentional control, which cannot be fully brought back online by increases in motivation.
The vigilance decrement might thus not occur due to a single cause, but is likely multifactorial in origin.
University of Chicago