How does the brain adapt to different levels of mental challenge? A new neuroimaging study reveals that when we engage in more complex cognitive tasks, our brain activity becomes not only richer in detail but also more streamlined. The findings suggest that the brain adjusts its patterns of activity to match the demands of the task, allowing for more efficient processing during mentally challenging activities.
The study, published in the Proceedings of the National Academy of Sciences, was driven by a desire to understand how the brain manages different cognitive demands. Previous research by the same team had revealed the brain’s remarkable ability to reconstruct missing data from minimal measurements, raising questions about why the brain can generate such detailed and efficient activity patterns with limited input.
“Several years ago, my co-author and graduate student at the time, Lucy Owen, and I came out with a precursor to this study, where we found something very surprising,” explained study author Jeremy Manning, an associate professor of psychological and brain sciences at Dartmouth College and director of the Contextual Dynamics Lab.
“At the time, we were working with neurosurgical patients who had electrodes implanted in their brains to monitor for seizure activity. A challenge with working with those recordings is that our brains contain roughly a hundred billion neurons, but we can only safely implant around a few hundred wires into someone’s brain. So there is a massive undersampling problem: for every measurement we take, we miss roughly a billion others! We wanted to understand how much of that ‘missing’ data we could reliably and accurately reconstruct using statistical ‘hacks.'”
“We were very surprised to find that just a few hundred measurements from an essentially random sampling of locations throughout someone’s brain could give us enough information to fill in an accurate guess about activity patterns throughout their entire brain, at millimeter-scale resolutions (roughly on par with the best fMRI available today), but at millisecond-scale sampling rates (roughly 1000 times faster than fMRI),” Manning said. “If human language was similarly efficient, I’d be able to tell you the details of every Wikipedia article just by speaking a dozen or so words.”
“In that initial study, we were primarily concerned with the ‘how’ and ‘what’ aspects of our approach: in other words, we reported how we built our model and generated guesses, how we validated the guesses, and the circumstances that affected accuracy, and so on. But it left us with a much deeper question that we weren’t able to answer back then: why is it possible to reconstruct what nearly our entire brain is doing at a given moment, using a comparatively miniscule number of measurements? That led us down a rabbit hole of additional questions about the fundamental properties of brain activity patterns. Our findings are reported in this new study.”
To answer these deeper questions about how the brain adjusts its activity to match cognitive demands, the researchers examined a dataset collected from previous neuroimaging experiments. These experiments involved functional magnetic resonance imaging (fMRI) scans of participants as they listened to different audio recordings.
Some participants listened to a coherent, seven-minute story, while others listened to a scrambled version of the story, in which either the paragraphs or individual words were randomly ordered. A final group underwent a resting state scan with no auditory stimulus, meant to simulate a condition of minimal cognitive engagement.
The goal was to analyze how the brain’s activity changed under these varying levels of cognitive demand. In a high-demand task—following a coherent story—the brain has to actively process and organize information to make sense of the narrative. In contrast, during the scrambled story conditions, the brain’s task is less cognitively challenging because the information is less meaningful. The resting state condition provided a baseline measure of brain activity in the absence of any specific cognitive task.
The authors sought to investigate two properties of brain activity: informativeness and compressibility. The authors hypothesized that these properties might shift depending on the complexity of a task, allowing the brain to strike a balance between flexibility and efficiency.
To assess the informativeness and compressibility of brain activity, the researchers used advanced computational techniques. They measured informativeness by analyzing how much specific information about the task was reflected in participants’ brain activity. Compressibility, on the other hand, was evaluated by examining how efficiently the brain’s activity patterns could be represented using fewer components or data points. A highly compressible brain pattern is one in which fewer pieces of information are needed to reconstruct the full activity.
“In the world of machine learning, the ability to reconstitute a detailed pattern from its parts is called ‘compression,'” Manning told PsyPost. “Highly compressible patterns can be accurately rebuilt from just a tiny sliver, like reconstructing the complete text of a novel from just a single word. Another related property is called ‘informativeness.’ This refers to how ‘expressive’ a sequence of patterns is– akin to the length of a novel.”
The researchers uncovered two key findings. First, brain activity was more informative and compressible when participants engaged in the more demanding task of listening to a coherent story compared to the scrambled story or resting conditions. This suggests that during higher-level cognitive tasks, the brain produces detailed, information-rich activity that is also organized efficiently. In simpler tasks, or during rest, the brain’s activity is less organized and contains less specific information.
Second, the study found that these brain patterns became more informative and compressible over time as participants continued to listen to the coherent story. As the narrative unfolded, the brain seemed to adapt by refining and optimizing its activity patterns. This pattern was less pronounced in the scrambled conditions, where the lack of a coherent structure in the story likely led to less mental engagement and, consequently, less organization in the brain’s activity.
“Going into this study, we would have guessed that ‘compression’ and ‘informativeness’ would have changed in opposite directions,” Manning said. “That would be analogous to either being able to reconstruct short novels from just a few words (perhaps under certain cognitive circumstances — representing high compressibility but low informativeness), or being able to reconstruct longer novels from more words (perhaps under different circumstances — representing low compressibility and high informativeness). Finding that compression and informativeness change in the same direction helped us to understand that these two aspects of how our brains respond can vary independently from each other.”
The researchers also took a closer look at different brain networks to see if certain regions were more affected by task complexity than others. They found that higher-order brain networks, which are typically associated with complex functions like decision-making and memory, showed more pronounced changes in informativeness and compressibility than lower-order networks, which are primarily involved in basic sensory processing. This supports the idea that the brain’s ability to adjust its activity is not uniform across all regions; instead, areas involved in more complex cognitive functions are especially responsive to task demands.
“We have known for a long time that our ‘thoughts’ come from patterns of electrical activity in our brains,” Manning told PsyPost. “What we found is that our brains seem to change the ‘language’ that those patterns are expressed in according to what we are doing. When we are highly engaged or thinking ‘deeply’ about what we are doing, our brains move into a mode where the activity patterns become both highly compressible and highly informative.”
“In other words, our brains start representing what we are doing or thinking about in a very efficient way that is also incredibly robust to data corruption. That helped us understand why, under the right circumstances, it becomes possible to accurately guess what someone’s entire brain is doing from just a few hundred measurements.”
“When we stop being engaged, or when we think more ‘shallowly’ about what we are doing, our brains switch into a much less efficient mode, where the activity patterns become less structured, less informative, and more idiosyncratic,” Manning continued. “We’re not totally sure why this happens, but we go through some speculations in our paper.”
The research provides valuable insights into the fundamental mechanisms of human cognition. But the study, like all research, has limitations. The study only examined a specific set of tasks and stimuli, which means that the results may not apply to all types of mental activities. Additionally, the researchers used one method of measuring brain activity—fMRI—which provides a detailed view of brain activity but is limited by its relatively slow sampling rate compared to other techniques.
“We looked at data from a little over 100 participants, using one set of experimental conditions, and using one method for measuring brain activity,” Manning noted. “Although it is tempting to generalize to ‘all humans and circumstances,’ the true test of these findings, as with any study, will be in how well they replicate and generalize.”
The researchers suggest that future studies could examine how the brain’s ability to adjust informativeness and compressibility might apply to other cognitive processes, such as decision-making, problem-solving, or creativity. Understanding how these brain properties change in different contexts could offer new insights into the nature of cognition and how the brain adapts to a wide range of mental challenges.
Despite its limitations, the study provides a compelling look at how the brain organizes itself to meet the demands of complex tasks. The findings suggest that the brain’s ability to adapt is not just a matter of activating more areas or working harder, but rather involves fine-tuning its activity patterns to balance flexibility and efficiency.
In the long term, this line of research could help scientists better understand how the brain supports higher-level cognitive functions and what happens when these processes break down, such as in conditions like dementia or traumatic brain injury. By identifying the mechanisms that allow the brain to optimize its activity for different tasks, researchers may eventually develop new interventions or treatments to support cognitive health and recovery.
“We are deeply curious about understanding fundamental questions about how our brains work, and what makes us ‘us.’ This line of work is a tiny part of a much broader literature aimed at uncovering the neural basis of thought,” Manning said. “My website is www.context-lab.com. It has links to all of my lab’s publications, data, and software, along with some open courses that could be of interest to people who want to learn more about this stuff.”
The study, “High-level cognition is supported by information-rich but compressible brain activity patterns,” was authored by Lucy L. W. Owen and Jeremy R. Manning.
