How the Brain Distinguishes Internal and External Sounds: An fMRI Investigation of Auditory Sound Externalization
Bibliographic record
Abstract
Abstract Auditory externalization, the perception of a sound source as located outside the head, is essential for spatial hearing and auditory scene analysis. However, its neural correlates remain poorly understood. This study investigated differences in brain activation elicited by externalized versus internalized sound sources. Twenty-nine healthy participants underwent a 3T functional magnetic resonance imaging (fMRI) scan while listening to auditory stimuli presented in three spatialization conditions: reverberant externalized sounds (highest externalization), anechoic externalized sounds (intermediate externalization) and diotic anechoic sounds (internalized). Whole-brain analyses revealed greater activation for externalized compared to internalized sound sources in the left superior temporal gyrus, including the planum temporale, the cerebellum and the left posterior cingulate gyrus. Internalized sounds elicited greater relative activity in the left inferior temporal gyrus. Direct comparison between the two externalized conditions revealed stronger left superior temporal gyrus activation for reverberant sounds, while anechoic sounds preferentially activated the right middle temporal gyrus. These findings confirmed the key role of the planum temporale in auditory externalization and the involvement of higher-order brain regions, suggesting broader networks underpinning the perception of sound location.
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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 | 0.001 |
| 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".