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Beneficial Effects of the NMDA Antagonist Ketamine on Decision Processes in Visual Search

2010· article· en· W2046822461 on OpenAlexafffund
Kelly Shen, Sarah Kalwarowsky, W. Clarence, Emiliano Brunamonti, Martin Paré

Bibliographic record

VenueJournal of Neuroscience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchQueen's University
KeywordsNMDA receptorNeurosciencePsychologyStimulus (psychology)Saccadic maskingSensory systemPerceptionKetamineEye movementVisual perceptionCognitive psychologyReceptorMedicine

Abstract

fetched live from OpenAlex

The ability of sensory-motor circuits to integrate sensory evidence over time is thought to underlie the process of decision-making in perceptual discrimination. Recent work has suggested that the NMDA receptor contributes to mediating neural activity integration. To test this hypothesis, we trained three female rhesus monkeys (Macaca mulatta) to perform a visual search task, in which they had to make a saccadic eye movement to the location of a target stimulus presented among distracter stimuli of lower luminance. We manipulated NMDA-receptor function by administering an intramuscular injection of the noncompetitive NMDA antagonist ketamine and assessed visual search performance before and after manipulation. Ketamine was found to lengthen response latency in a dose-dependent fashion. Surprisingly, it was also observed that response accuracy was significantly improved when lower doses were administered. These findings suggest that NMDA receptors play a crucial role in the process of decision-making in perceptual discrimination. They also further support the idea that multiple neural representations compete with one another through mutual inhibition, which may explain the speed-accuracy trade-off rule that shapes discrimination behavior: lengthening integration time helps resolve small differences between choice alternatives, thereby improving accuracy.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.303
Teacher spread0.283 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations30
Published2010
Admission routes2
Has abstractyes

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