High-Field Functional Imaging of Pitch Processing in Auditory Cortex of the Cat
Bibliographic record
Abstract
The perception of pitch is a widely studied and hotly debated topic in human hearing. Many of these studies combine functional imaging techniques with stimuli designed to disambiguate the percept of pitch from frequency information present in the stimulus. While useful in identifying potential "pitch centres" in cortex, the existence of truly pitch-responsive neurons requires single neuron-level measures that can only be undertaken in animal models. While a number of animals have been shown to be sensitive to pitch, few studies have addressed the location of cortical generators of pitch percepts in non-human models. The current study uses high-field functional magnetic resonance imaging (fMRI) of the feline brain in an attempt to identify regions of cortex that show increased activity in response to pitch-evoking stimuli. Cats were presented with iterated rippled noise (IRN) stimuli, narrowband noise stimuli with the same spectral profile but no perceivable pitch, and a processed IRN stimulus in which phase components were randomized to preserve slowly changing modulations in the absence of pitch (IRNo). Pitch-related activity was not observed to occur in either primary auditory cortex (A1) or the anterior auditory field (AAF) which comprise the core auditory cortex in cats. Rather, cortical areas surrounding the posterior ectosylvian sulcus responded preferentially to the IRN stimulus when compared to narrowband noise, with group analyses revealing bilateral activity centred in the posterior auditory field (PAF). This study demonstrates that fMRI is useful for identifying pitch-related processing in cat cortex, and identifies cortical areas that warrant further investigation. Moreover, we have taken the first steps in identifying a useful animal model for the study of pitch perception.
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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 | 0.000 |
| 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".