Visual recalibration of auditory spatial perception: two separate neural circuits for perceptual learning
Bibliographic record
Abstract
A remarkable example of rapid perceptual learning is the visual recalibration of auditory spatial perception, which can result in either a bias (ventriloquism after-effect) or an improvement (multisensory enhancement) in auditory localization. Here, we examine the possibility that these after-effects might depend on two distinct neural pathways (geniculostriate vs. collicular-extrastriate). To this end, patients with a lesion of the striate cortex (hemianopic patients) or temporoparietal cortex (neglect patients) were asked to localize weak sounds, before and after a brief exposure to repetitive auditory-visual stimulation which was given either in the normal or in the affected field. Adaptation comprised spatially disparate (Experiment 1) or spatially coincident (Experiment 2) auditory-visual stimuli. After exposure to spatially disparate stimuli in the normal field, all patients exhibited the usual shifts toward the visual attractor, at each sound location. In contrast, when the same kind of adaptation was given in the affected field, a consistent shift was still evident in neglect patients but not in patients with hemianopia. After adaptation to spatially coincident stimuli, and regardless of the adaptation hemifield, all patients exhibited a significant improvement in auditory localization, which was largest for sounds presented at the adapted location. The findings suggest the presence of two distinct recalibration mechanisms. Adapting to spatially conflicting stimuli invokes a corrective mechanism implemented within the geniculostriate circuit, which tries to reduce the registered discrepancy. Adapting to spatially aligned inputs invokes a mechanism implemented along a collicular-extrastriate circuit, which tries to reduce the localization error.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".