Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference
Alexander Skowron, Julian Q. Kosciessa, Robert Lorenz, R. Hertwig, W. van den Bos, Douglas D. Garrett
Cerebral Cortex September 1, 2025 DOI: 10.1093/cercor/bhaf219 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study with computational modeling Peer reviewed |
|---|---|
| Sample size | 47 |
| Population | Healthy adults |
| Topics | Default mode network |
| Key findings | BOLD signal variability across independent learning trials compressed with successive exposure to decision-related evidence, and this compression was greater in more accurate participants, primarily in default mode network regions. The authors argue this reflects increasing precision of latent state beliefs during efficient learning, and that more accurate participants had a flatter prior enabling larger uncertainty reduction. |
Abstract
Abstract To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that temporal neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured across independent learning trials) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in default mode network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.
Comparable studies
Other observational and cohort studies on default mode network, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| A default mode of brain function Normal adult human brain | 2001 | Observational study | |
| Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network Healthy subjects | 2008 | Observational cohort | |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis Healthy subjects | 2008 | Observational study |
Citations in the library
Cites 6
- A default mode of brain function Proceedings of the National Academy of Sciences January 16, 2001
- The Brain's Default Mode Network Annual Review of Neuroscience May 4, 2015
- Patterns of Brain Activity Supporting Autobiographical Memory, Prospection, and Theory of Mind, and Their Relationship to the Default Mode Network Journal of Cognitive Neuroscience July 6, 2009
- Episodic Memory Retrieval, Parietal Cortex, and the Default Mode Network: Functional and Topographic Analyses Journal of Neuroscience March 23, 2011
- Replay, the default mode network and the cascaded memory systems model Nature Reviews Neuroscience August 15, 2022
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