Influence of attention mechanisms on cerebellar and basal ganglia activity during vocal emotion decoding
Bibliographic record
Abstract
Abstract Emotional prosody processing involves a widespread network of brain regions, but the specific roles of the cerebellum and basal ganglia in explicit and implicit tasks are not well known or understood. This study investigated how the cerebellum and basal ganglia contribute to explicit (emotion categorization) and implicit (gender categorization) processing of emotional prosody, namely when attention is directly versus implicitly oriented towards the emotion of the voice stimuli, respectively. Twenty-eight healthy French-speaking participants (average age: 65 years old) underwent high-resolution functional MRI while performing explicit and implicit vocal emotion processing tasks. Neuroimaging results revealed—and replicated—that both tasks recruited a widespread network, including the superior temporal cortex, inferior frontal cortex, primary motor and somatosensory cortices, basal ganglia, and cerebellum. The explicit task elicited stronger activations in the basal ganglia (caudate nucleus, putamen) and cerebellar regions (Crus I/II, lobules VI, VIIb, and X), consistent with higher cognitive control demands. In contrast, the implicit task was associated with activations in cerebellar lobules IV-V, VI, VIII, and IX, along with the thalamus. Regression-based functional connectivity analyses further demonstrated stronger connectivity between the right cerebellar lobule IX and the putamen, as well as the cerebellar vermis (XII), particularly during implicit processing. These findings highlight the distinct contributions of the cerebellum and basal ganglia to emotional prosody processing, with explicit tasks engaging associative and cognitive control networks, while implicit tasks rely more on sensorimotor and automatic neural processing mechanisms.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".