Learning arbitrary visuoauditory mappings during interception of moving targets
Bibliographic record
Abstract
The brain represents multisensory mappings relevant for interaction with the world. These mappings mostly involve intrinsically relevant signals, such as vision and proprioception of the hand in reaching. Here, we studied how more arbitrary maps are learned. The employed visuoauditory map coupled visual target position to the pitch of an accompanying sound. Our participants thus had to reach to intercept a moving target. The pitch of the accompanying sound was a function of target position either on the screen or relative to the fixation direction (in different subsets of participants, n = 5 for both, so far), which was also varied in the experiment. Participants sat in front of a monitor with their heads immobilized by a bite-bar. Targets appeared on a variety of positions and moved with a variety of velocities (left or right). After 500 ms the fixation point changed size and color, indicating that the reaching movement could be initiated. Our design involved a pre-test (intercepting visual targets), a learning phase (intercepting visual and audible targets, while the duration of target visibility was progressively reduced), and a testing phase (intercepting audible targets). Finger position at the moment of contact with the screen was determined using Optotrak, and fixation quality was assessed using EyeLink II. Participants in both groups could perform the task reasonably: even for the audible targets the pointing positions were significantly correlated with the target position at interception. We are currently analyzing the pointing errors within subjects as a function of fixation direction, initial target position and target velocity. This will provide a general idea of factors playing into the control of interception. More importantly, however, we will test the effect of mapping (screen versus gaze-centered) between participants, in order to examine whether the arbitrary mapping was better represented in screen-(/world-) or gaze-centered coordinates.
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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 | 0.003 |
| 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".