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Critical and Sensitive Periods in Brain Development

2025· reference-entry· en· W4412418614 on OpenAlexaff
Bryan Kolb

Bibliographic record

VenueOxford Research Encyclopedia of Psychology · 2025
Typereference-entry
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Abstract The brain has a remarkable capacity for change, via a process known as neural plasticity, and this capacity allows the brain to adapt to changing internal and external environments. There are periods during brain development and maturation in which the brain is especially labile, and these periods are known as critical and sensitive periods. Critical periods are times during which specific environmental experiences are required for the maturation of specific brain regions or functions and in which specific experiences result in irreversible changes in brain organization and function. Sensitive periods refer to times during which the brain is unusually responsive to a wide range of experiences including internal events such as hormone release or external events such as music. Both types of periods are initiated by the timing of the appropriate balance of excitatory and inhibitory (E-I) inputs. When various factors stimulate GABA, a neurotransmitter, this changes the E-I balance, leading to a sequence of molecular events, which induce plastic changes in neurons. This plastic period closes as perineuronal nets develop around the cell body and put the brakes on plasticity. Two special sensitive times are in the early postnatal years and again in adolescence. Early experiences such as trauma or stress can influence the effects of adolescent experiences such as consumption of psychoactive drugs. Research is continuing to investigate what mechanisms of plasticity are triggered in critical and sensitive periods.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.458
Teacher spread0.370 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2025
Admission routes1
Has abstractyes

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