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Record W2788953943 · doi:10.3389/fncom.2018.00011

Temporal Dissociation of Neocortical and Hippocampal Contributions to Mental Time Travel Using Intracranial Recordings in Humans

2018· article· en· W2788953943 on OpenAlexfundno aff
Roey Schurr, Mor Nitzan, Ruth Eliahou, Laurent Spinelli, Margitta Seeck, Olaf Blanke, Shahar Arzy

Bibliographic record

VenueFrontiers in Computational Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science Foundation
KeywordsTemporal lobeDissociation (chemistry)Hippocampal formationChronesthesiaNeuroscienceHippocampusPsychologyTemporal cortexNeocortexCognitive psychologyCognitionEpisodic memoryChemistryEpilepsy

Abstract

fetched live from OpenAlex

In mental time travel (MTT) one is "traveling" back-and-forth in time, remembering, and imagining events. Despite intensive research regarding memory processes in the hippocampus, it was only recently shown that the hippocampus plays an essential role in encoding the temporal order of events remembered, and therefore plays an important role in MTT. Does it also encode the temporal relations of these events to the remembering self? We asked patients undergoing pre-surgical evaluation with depth electrodes penetrating the temporal lobes bilaterally toward the hippocampus to project themselves in time to a past, future, or present time-point, and then make judgments regarding various events. Classification analysis of intracranial evoked potentials revealed clear temporal dissociation in the left hemisphere between lateral-temporal electrodes, activated at ~100-300 ms, and hippocampal electrodes, activated at ~400-600 ms. This dissociation may suggest a division of labor in the temporal lobe during self-projection in time, hinting toward the different roles of the lateral-temporal cortex and the hippocampus in MTT and the temporal organization of the related events with respect to the experiencing self.

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)0.001
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 score0.838
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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