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Record W7053000773

Understanding the Nature, Extent, and Brain Dynamics of Deficient Pattern Separation

2023· other· en· W7053000773 on OpenAlexfundno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2023
Typeother
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHippocampal formationEpisodic memoryMnemonicHippocampusPerceptionCognitionAmnesiaTemporal lobeAssociative property
DOInot available

Abstract

fetched live from OpenAlex

Recent research suggests that the specificity and precision in long-term declarative memory depend on pattern separation. Subfields within the mammalian hippocampus have been shown to mediate this neurobiological process, particularly the dentate gyrus (DG). This subfield interacts with other parts of the medial temporal lobe and neocortex to differentiate highly similar details belonging to separate, yet overlapping, events into discrete episodes at encoding. In humans, the brain-behavior correlates of pattern separation have been explored in modified associative memory tests, which tax the mnemonic discrimination of previously learned images of everyday objects from visually similar lures. Older individuals with reduced hippocampal volumes and patients with hippocampal lesions are impaired relative to controls on these tests. Based on this evidence, researchers have concluded that visual mnemonic discrimination tests are functionally sensitive to the process of hippocampal pattern separation. This assertion may be premature. Despite the preponderance of studies of visual pattern separation over the past 15 years, little is known about whether hippocampal pattern separation works 1) in other modalities or cognitive domains; 2) through interacting with prior knowledge or pre-experimentally novel information, and 3) in concert with activities of perceptual categorization. The present research addresses these issues. In Study 1, I examine whether presumed deficits in pattern separation apply to perception as they do to memory and are evident, even within vision, for stimuli such as faces, which presumably do not crucially depend on the hippocampus. In Study 2, I pursue whether pattern separation extends to modalities other than vision, notably audition. In Study 3, I aim to quantify the impact of prior knowledge on pattern separation and whether discrimination of abstract inputs can be measured at encoding and retrieval. Three groups of participants were tested throughout these studies: young adults, middle-older adults, and older adults. In addition, a rare individual with focal hippocampal lesions to his DG helped to contextualize hippocampal involvement in Studies 1 and 2. The research I conducted on memory and perception combines novel behavioral paradigms and electrophysiological (EEG) techniques sensitive to the temporal dynamics involved in oddity detection to understand better the nature, extent, and brain dynamics of deficient pattern separation. The data analyzed allowed me to make inferences about the nature, scope, and brain dynamics of pattern separation in younger, middle-older, and older adults and in a hippocampal patient. The research addresses unanswered questions about pattern separation and the role of the hippocampus in learning and memory across other processing domains, modalities and involving different types of stimuli. As our population ages, so will the number of individuals who will suffer age-related cognitive impairment. One of the most common among them is a decline or loss of episodic memory, characterized by an inability to recall past personal experiences in detail, specificity, and precision. Similar losses of detail, specificity, and accuracy are observed in perception. Knowledge gained from this research helps to inform the development of tools for clinical assessment and intervention.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.201
Teacher spread0.184 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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