Differentiating the roles of parietal cortex, auditory cortex and the thalamus in auditory stream segregation
Bibliographic record
Abstract
In the last decade, great progress has been made in identifying neural structures that underlie auditory streaming. Regions of the auditory cortex have been implicated in macaque electrophysiology (Fishman et al. 2001, 2004; Micheyl et al, 2005, 2007), human MEG (Gutschalk et al, 2005, 2007) and fMRI (Kondo & Kashino, 2009; Deike et al, 2010; Hill et al, 2011). The parietal cortex has been implicated using human fMRI (Hill et al, 2011; Cusack et al, 2005) and MEG (Teki et al, 2011). Finally, there is intriguing data from fMRI on the involvement of the thalamus (Kondo & Kashino, 2009), and from electrophysiology on subcortical regions in the auditory periphery (Pressnitzer et al, 2008). However, the roles of these different regions are far from clear. I will report results from multi-voxel pattern analyses of fMRI data, which probe what kind of information is encoded within each brain region. This revealed markers of stream segregation in the thalamus, auditory cortex and the parietal cortex, but representation of the basic stimulus features only in auditory cortex. I will discuss the roles of the different regions in automatic and voluntary scene analysis, selective attention, and multimodal object representation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".