Information Shielding Through Neural Deactivation: A Fundamental Mechanism of Human Information Processing
Bibliographic record
Abstract
Abstract Information processing is a core issue in cognitive neuroscience. The brain can either accept or shield information. However, information shielding has traditionally been regarded as a byproduct of other cognitive processes that prepare brain areas for activation, with the accompanying brain deactivation seen as merely supportive. Here, we offer an alternative perspective, demonstrating that people can directly perform information shielding without engaging other cognitive processes. Using information shielding as a novel investigative lens, we provide direct evidence that deactivation serves as a primary mechanism in its own right. We found that information shielding was characterized by a deactivation-dominant neural dynamic, with initial executive control region activation giving way to increased sensory region deactivation, especially after a 3-second boundary. Neural decoding results further confirmed this deactivation trend. This deactivation pattern was consistent across both auditory and visual modalities. Furthermore, non-invasive brain stimulation demonstrated a causal relationship between the deactivation pattern and information shielding, and we found that this deactivation mechanism extended to general unnecessary information processing, regardless of whether the necessity was determined objectively or subjectively. This challenges the conventional emphasis on activation as the primary mode of information processing and opens new fundamental avenues for understanding inhibitory cognitive processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".