MEG Based Precision-Gain Index (PGI) Reveals Dual-task Costs in Vision and Audition
Bibliographic record
Abstract
Perception allows humans to adaptively navigate their environment by integrating sensory signals into coherent representations and it depends on how precisely the brain weights prediction errors from sensory inputs. However, when multiple senses compete, it remains unclear how this precision is distributed and whether it predicts behavioral efficiency under attentional load. To further explore this question, we recruited thirty adults who performed auditory–visual oddball tasks during magnetoencephalography (MEG) under single-attention (‘pure load’) and dual-stream (‘dual load’) conditions. Behavioral efficiency was quantified using the inverse-efficiency score (IES), with modality-specific loss under dual load defined as the increase in IES from single- to dual-stream blocks. To capture neural precision, we derived a modality-specific precision-gain index (PGI) from the difference wave, computed as the normalized contrast between attended and ignored deviant–standard responses. At the group level, dual load impaired behavioral efficiency, with a larger cost for vision than for audition. In parallel, PGI increased under dual load for both modalities (vision Δ=+0.044, p=.0075; audition Δ=+0.033, p=.014). Finally, we examined whether PGI in the 100–200 ms window could predict these modality-specific efficiency losses. In vision, higher PGI predicted larger efficiency losses under dual load (r=.45, p=.0144). In contrast, in audition, a greater ΔPGI (dual minus pure) predicted smaller efficiency losses (r=−.44, p=.0149). Thus, within ~200 ms, PGI captures modality-specific, load-dependent precision and forecasts how well participants sustain efficiency under dual-task demands.
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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 | 0.001 |
| 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".