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Associative modulation of US processing: implications for understanding of habituation

2010· book-chapter· en· W956260264 on OpenAlexfundno aff
Allan R. Wagner, Edgar Vögel

Bibliographic record

VenueCambridge University Press eBooks · 2010
Typebook-chapter
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsHabituationPsychologyClassical conditioningCognitive psychologyContext (archaeology)NeuroscienceAssociative learningAssociative propertyConditioningPhenomenonCognitive scienceBiologyPhysics

Abstract

fetched live from OpenAlex

Considerable data from Pavlovian conditioning indicate that events that are associatively signaled by discrete cues are not as effectively processed as they otherwise would be. Extrapolating from early evidence of this phenomenon, especially so-called conditioned diminution of the unconditioned response (CDUR), Wagner (1976, 1979) suggested that a similar effect might be responsible for long-term habituation, as stimuli come to be “expected” in the context in which they have been exposed. In this paper we will reflect upon this reasoning in the light of more recent evidence from our laboratory and elsewhere. One major complication is that extended contexts (as well as discrete cues) can control response-potentiating, conditioned-emotional tendencies, in addition to the presumed decremental effects. Experiments that separate these effects will be exemplified, and one theoretical approach, through the models SOP and AESOP (Wagner, 1981; Wagner & Brandon, 1989) will be illustrated, with some implications for our further understanding of habituation. In the late 1970s, Wagner (1976; 1979) presented some views about associative learning that were centered upon the notion that events that are already represented, or “primed” in active memory, are not as effectively processed as they otherwise would be. One of several forms of evidence for this supposition was the so-called conditioned diminution of the UR, a phenomenon originally reported by Kimble and Ost (1961), in the context of human eyeblink conditioning.

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)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0020.008
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.176
GPT teacher head0.286
Teacher spread0.111 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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