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Record W2125015671 · doi:10.1162/08989290260138609

Hemispheric Encoding Asymmetry is More Apparent Than Real

2002· article· en· W2125015671 on OpenAlexaff
Michael B. Miller, Alan Kingstone, Michael S. Gazzaniga

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2002
Typearticle
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyDisconnectionEncoding (memory)Lateralization of brain functionEpisodic memoryNeuroimagingRight hemisphereNeuroscienceCognitive psychologyCognition

Abstract

fetched live from OpenAlex

Previous neuroimaging studies have claimed a left hemisphere specialization for episodic "encoding" and a right hemisphere specialization for episodic "retrieval." Yet studies of split-brain patients indicate relatively minor memory impairment after disconnection of the two hemispheres. This suggests that both hemispheres are capable of encoding and retrieval. In the present experiment, we examined the possible limits on encoding capacity of each hemisphere by manipulating the "depth" of processing during the encoding of unfamiliar faces and familiar words in the left and right hemispheres of two split-brain patients. Results showed that only the left hemisphere benefited from deeper (more elaborate) encoding of familiar words, and only the right hemisphere benefited from deeper encoding of unfamiliar faces. Our findings are consistent with the view that hemispheric asymmetries in episodic encoding are related to hemisphere-specific processing of particular stimuli. Convergent with recent neuroimaging studies, these results with split-brain patients also suggest that these hemispheric differences are not due to unique specializations in each half brain for encoding memories, but rather, are due to preferential recruitment of the synaptically closer prefrontal cortex to posterior regions processing material-specific information.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.063
GPT teacher head0.312
Teacher spread0.249 · 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.

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

Citations47
Published2002
Admission routes1
Has abstractyes

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