Neural substrates of spatial processing and navigation in blindness: An activation likelihood estimation meta-analysis
Bibliographic record
Abstract
Even though vision is considered the best suited sensory modality to acquire spatial information, blind individuals can form spatial representations to navigate and orient themselves efficiently in space. Consequently, many studies support theamodality hypothesisof spatial representations since sensory modalities other than vision contribute to the formation of spatial representations, independently of visual experience and imagery. However, given the high variability in abilities and deficits observed in blind populations, a clear consensus about the neural representations of space has yet to be established. To this end, we performed a meta-analysis of the literature on the neural correlates of spatial processing and navigation via sensory modalities other than vision, like touch and audition, in individuals with early and late onset blindness. An activation likelihood estimation (ALE) analysis of the neuroimaging literature revealed that early blind individuals and sighted controls activate the same neural networks in the processing of non-visual spatial information and navigation, including the posterior parietal cortex, frontal eye fields, insula, and the hippocampal complex. Furthermore, blind individuals also recruit primary and associative occipital areas involved in visuo-spatial processing via cross-modal plasticity mechanisms. The scarcity of studies involving late blind individuals did not allow us to establish a clear consensus about the neural substrates of spatial representations in this specific population. In conclusion, the results of our analysis on neuroimaging studies involving early blind individuals support theamodality hypothesisof spatial representations.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".