Neural correlates of motion processing through echolocation, source hearing, and vision in blind echolocation experts and sighted echolocation novices
Bibliographic record
Abstract
We have shown in previous research (Thaler L, Arnott SR, Goodale MA. PLoS One 6: e20162, 2011) that motion processing through echolocation activates temporal-occipital cortex in blind echolocation experts. Here we investigated how neural substrates of echo-motion are related to neural substrates of auditory source-motion and visual-motion. Three blind echolocation experts and twelve sighted echolocation novices underwent functional MRI scanning while they listened to binaural recordings of moving or stationary echolocation or auditory source sounds located either in left or right space. Sighted participants' brain activity was also measured while they viewed moving or stationary visual stimuli. For each of the three modalities separately (echo, source, vision), we then identified motion-sensitive areas in temporal-occipital cortex and in the planum temporale. We then used a region of interest (ROI) analysis to investigate cross-modal responses, as well as laterality effects. In both sighted novices and blind experts, we found that temporal-occipital source-motion ROIs did not respond to echo-motion, and echo-motion ROIs did not respond to source-motion. This double-dissociation was absent in planum temporale ROIs. Furthermore, temporal-occipital echo-motion ROIs in blind, but not sighted, participants showed evidence for contralateral motion preference. Temporal-occipital source-motion ROIs did not show evidence for contralateral preference in either blind or sighted participants. Our data suggest a functional segregation of processing of auditory source-motion and echo-motion in human temporal-occipital cortex. Furthermore, the data suggest that the echo-motion response in blind experts may represent a reorganization rather than exaggeration of response observed in sighted novices. There is the possibility that this reorganization involves the recruitment of "visual" cortical areas.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".