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Record W7132884847

Ready for Action! When the Brain Learns, Yet Memory-Guided Behavior Does Not Follow

2023· dissertation· W7132884847 on OpenAlexaff
Manda Fischer

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeural correlates of consciousnessObject (grammar)Action (physics)Neural activityALARMElectroencephalographyTemporal lobeAmygdalaBrain activity and meditation
DOInot available

Abstract

fetched live from OpenAlex

The ability to use memory for action is critical for everyday life, such as for remembering which alarm signals danger. Although research suggests that memory for past events influences how we respond to events in the present, the magnitude of behavioral benefit varies and can even be absent. How is it that the brain learns, yet the learning does not influence memory-guided action? In this thesis, I show that 1) not all learned memory representations are usable for behavior and 2) those that are usable for behavior are supported by distinct neural mechanisms during retrieval. Study 1 demonstrates that learning and behavioral expression of learning are separable and differentially affected by attention. Studies 2 and 3 provide further evidence that memory-guided behavior is supported by distinct neural substrates. Specifically, Study 1 leveraged high-density electroencephalography (EEG) and revealed a striking brain-behavior dissociation. Learning was observed neurally in two attention conditions. However, only one condition, in which attention was directed to the task-relevant object at learning, led to a memory-guided behavioral benefit at retrieval. Further, these behaviorally accessible memories were accompanied by an implicit neural memory component that correlated with the degree of response preparation, but not with response execution. In Study 2, I conducted a systematic review and a meta-analysis and demonstrated that fronto-parietal attention and control networks are critical for memory-guided behavior. Study 3 empirically validated this working model, using time-frequency source localization. Specifically, I showed that, in addition to fronto-parietal networks, memory-guided behavior is accompanied by anterior temporal lobe activity that scales with the magnitude of behavioral benefit. Together, the results of the presented studies suggest that 1) attention at learning affects memory accessibility and guided behavior at retrieval and 2) accessible memories that guide behavior are supported by distinct temporal and fronto-parietal networks and implicit neural memory that triggers response preparation.

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)0.004
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.174
GPT teacher head0.434
Teacher spread0.260 · 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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