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Record W2065512708 · doi:10.1037//0097-7403.26.4.454

Conditioning across the duration of a backward conditioned stimulus.

2000· article· en· W2065512708 on OpenAlexaff
Cathryn Romaniuk, Douglas A. Williams

Bibliographic record

VenueJournal of Experimental Psychology Animal Behavior Processes · 2000
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsConditioningExcitatory postsynaptic potentialClassical conditioningMeasures of conditioned emotional responsePsychologyUnconditioned stimulusStimulus (psychology)Inhibitory postsynaptic potentialNeuroscienceMathematicsCognitive psychologyStatistics

Abstract

fetched live from OpenAlex

Five conditioned suppression experiments examined the extent to which an appetitively motivated lever-press response can be punished by different components of a backward conditioned stimulus (CS). Using a 0-s unconditioned stimulus (US)-CS interval, Experiments 1 and 2 showed that the initial 3 s of a normally 30-s backward CS served as a more effective punisher than the CS as a whole. Experiment 3 found no such effect if the US-CS interval were 3 s rather than 0 s. Experiments 4A and 4B found that if the US-CS interval were 0 s, the initial part of the backward CS acquired excitatory properties although the CS as a whole passed a summation test for conditioned inhibition. By contrast, the 3-s US-CS interval supported inhibitory conditioning across the whole duration of the backward CS. Taken together, these findings support a modified version of Wagner's sometimes opponent process model, which suggests that different components of a backward CS become either excitatory or inhibitory depending on the components' temporal proximity to the US.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.105
GPT teacher head0.433
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2000
Admission routes1
Has abstractyes

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