Cortical Tracking of Complex Sound Envelopes: Modeling the Changes in Response with Intensity
Bibliographic record
Abstract
Characterizing how the brain responds to stimuli has been a goal of sensory neuroscience for decades. One key approach has been to fit linear models to describe the relationship between sensory inputs and neural responses. This has included models aimed at predicting spike trains, local field potentials, BOLD responses, and EEG/MEG. In the case of EEG/MEG, one explicit use of this linear modeling approach has been the fitting of so-called temporal response functions (TRFs). TRFs have been used to study how auditory cortex tracks the amplitude envelope of acoustic stimuli, including continuous speech. However, such linear models typically assume that variations in the amplitude of the stimulus feature (i.e., the envelope) produce variations in the magnitude but not the latency or morphology of the resulting neural response. Here, we show that by amplitude binning the stimulus envelope, and then using it to fit a multivariate TRF, we can better account for these amplitude-dependent changes, and that this leads to a significant improvement in model performance for both amplitude-modulated noise and continuous speech in humans. We also show that this performance can be further improved through the inclusion of an additional envelope representation that emphasizes onsets and positive changes in the stimulus, consistent with the idea that while some neurons track the entire envelope, others respond preferentially to onsets in the stimulus. We contend that these results have practical implications for researchers interested in modeling brain responses to amplitude modulated sounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".