Brain areas involved in echolocation motion processing in blind echolocation experts
Bibliographic record
Abstract
People can echolocate their distal environment by making mouth-clicks and listening to the click-echoes. In previous work that used functional magnetic resonance imaging (fMRI) we have shown that the processing of echolocation motion increases activity in posterior/inferior temporal cortex (Thaler et al., 2011). In the current study we investigated, if brain areas that are sensitive to echolocation motion in blind echolocation experts correspond to visual motion area MT+. To this end we used fMRI to measure brain activity of two early blind echolocation experts while they listened to recordings of echolocation and auditory source sounds that could be either moving or stationary, and that could be located either to the left or to the right of the listener. A whole brain analysis revealed that echo motion and source motion activated different brain areas in posterior/inferior temporal cortex. Furthermore, the relative spatial arrangement of echo and source motion areas appeared to match the relative spatial arrangement of area MT+ and source motion areas that has been reported for sighted people (Saenz et al., 2008). Furthermore, we found that brain areas that were sensitive to echolocation motion showed a larger response to echo motion presented in contra-lateral space, a response pattern typical for visual motion processing in area MT+. In their entirety the data are consistent with the idea that brain areas that process echolocation motion in blind echolocation experts correspond to area MT+.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".