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Record W2012880872 · doi:10.1037/a0015937

Ventromedial prefrontal damage and memory for context: Perceptual versus semantic features.

2009· article· en· W2012880872 on OpenAlexaff
Elisa Ciaramelli, Julia Spaniol

Bibliographic record

VenueNeuropsychology · 2009
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsToronto Metropolitan UniversityBaycrest Hospital
Fundersnot available
KeywordsConfabulation (neural networks)PsychologyVentromedial prefrontal cortexSemantic memoryCognitive psychologyContext (archaeology)Episodic memoryPrefrontal cortexPerceptionCognitionSemantic featureExplicit memoryNeuroscienceArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Memory for context is known to rely on episodic binding and strategic retrieval processes. It is unclear, however, whether memory for different contextual features taps the same cognitive and neural mechanisms. Here, the authors compare memory for a perceptual feature (i.e., the format in which an item had been presented) and for a semantic feature (i.e., the concept with which an item had been paired) in 13 patients with lesions in ventromedial prefrontal cortex, including patients with and without confabulation, and 13 healthy controls. Participants studied picture-word pairs and received an old-new recognition test that included intact pairs, rearranged pairs, format pairs (studied pairs in which the picture-word format of each item was switched), old-new pairs, and new-new pairs. Hit rates for intact pairs were similar for all participant groups. Compared with controls, patients, especially those with confabulation, had higher false-alarm rates for format pairs but comparable false-alarm rates for rearranged pairs. The authors propose that distinct monitoring processes are engaged during retrieval of perceptual and semantic context, with only the former crucially dependent on ventromedial prefrontal cortex.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.610

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 integrity0.0000.000
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 head0.041
GPT teacher head0.319
Teacher spread0.278 · 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

Citations18
Published2009
Admission routes1
Has abstractyes

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