#45: MAC SHINE AND PAUL CISEK – EXPLORING THE EVOLUTION, INTEGRATION AND COMPLEXITIES OF THE BRAIN: BASAL GANGLIA, DOPAMINE, AND BEYOND.
Bibliographic record
Abstract
In this special episode of Stimulating Brains, we dive deep into the intricacies of the human brain with two esteemed guests, Associate Professor Mac Shine from Sydney University and Associate Professor Paul Cisek from the University of Montreal. Building upon our earlier conversation with Maxine in episode 9, this episode sees these brilliant minds sharing their insights on the basal ganglia, the role of dopamine, and the fascinating interplay between various brain regions. In addition, we explore the modulation of the thalamus by the basal ganglia, discussing its impact on both the cortex and the brainstem. Moreover, the conversation takes us on a journey through the evolution of the brain, examining the concept of the phylogenetic refinement approach. Join us in this intellectually stimulating episode as we explore groundbreaking concepts that could significantly impact both systems and clinical neuroscience. Some resources mentioned in the episode:Tony Prescott work on layered control architecture - (1408) Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots - YouTubeElisabeth Murray, Steven Wise book - The Evolution of Memory Systems - Paperback - Elisabeth Murray; Steven Wise; Kim Graham - Oxford University Press (oup.com)Bill Powers book review - Behavior: The Control of Perception — LessWrongThe seven problems of the basal ganglia - Seven problems on the basal ganglia - PubMed (nih.gov)Functional neuroimaging as a catalyst for integrated neuroscience - Functional neuroimaging as a catalyst for integrated neuroscience - PubMed (nih.gov)Strong inference by John Platt - Strong Inference | ScienceJosh Berke - (1408) Josh Berke - Dopamine firing versus dopamine release during motivated behavior - ViDA 2020 - YouTube
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".