MétaCan
Menu
Back to cohort
Record W2089840658 · doi:10.1093/brain/awv084

Differential functions of ventral and dorsal striatum

2015· letter· en· W2089840658 on OpenAlexaff
Yashar Zeighami, Ahmed A. Moustafa

Bibliographic record

VenueBrain · 2015
Typeletter
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsDorsumVentral striatumStriatumNeuroscienceDifferential (mechanical device)AnatomyBiologyPsychologyDopaminePhysics

Abstract

fetched live from OpenAlex

Sir, The case study by Vo and colleagues (2014) aims to address the differential roles of ventral versus dorsal striatum in learning, specifically, whether they are essential for learning or simply involved in it. The authors reported a dissociation between action-value (based on the outcomes, values will be assigned to actions) and stimulus-value learning (values will be associated with the stimuli), and how impairment of the dorsal striatum will affect each of these processes. To achieve this, Vo and colleagues tested a patient (known as XG) who has bilateral damage to the dorsal striatum, while the ventral striatum including the nucleus accumbens is spared. To compare XG’s performance in different tasks with a healthy population statistically, the researchers tested 11 matched control subjects. Among the seven reinforcement learning tasks employed, three could be solved only by learning stimulus-values, one only by learning action-values, and the remaining tasks, with either strategy. Surprisingly, they found that Patient XG was able to learn all the tasks involving action-value learning and his performance resembled those of healthy controls. However, he was impaired at learning the tasks which could only be learned using stimulus values; his performance in those tasks was significantly poorer than controls and was no different from random. Vo et al. also analysed …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.137
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.253
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicBotulinum Toxin and Related Neurological DisordersFrench-language works237,207