Detection of unexpected events during spatial navigation in humans: bottom‐up attentional system and neural mechanisms
Bibliographic record
Abstract
Navigation is a complex cognitive ability requiring the processing and integration of several different types of information extracted from the environment. While navigating, however, an unexpected event may suddenly occur, which individuals are required to detect promptly in order to apply an appropriate behavioural response. The alerting mechanism that is integral to the detection of unexpected events is referred to as the bottom-up attentional system. Using event-related functional magnetic resonance imaging, we investigated the neural basis of bottom-up detection of unexpected events while individuals moved within a virtual environment. We identified activation within a right fronto-temporo-parietal network in response to unexpected events while navigating in this virtual environment. Furthermore, when an unexpected event requires an adjusted behavioural response, a region of the right ventrolateral pre-frontal cortex (areas 45 and 47/12) is selectively activated. Our data replicate earlier findings on the neural mechanisms underlying visual attention and extend these findings to the more complex real-life ability of spatial navigation, thereby suggesting that these neural mechanisms subserve the bottom-up attentional systems that are crucial for effective locomotion in real surroundings.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".