Behavioral learning curves in a procedural working memory task are better fit by an exponential model than a linear model across five training sessions¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Exponential learning curves fit behavioral error-rate data better than linear models across all task conditions in a five-session longitudinal study. → Lena S. Geiger 2024
Evidence¶
Lena S. Geiger 2024¶
Lena S. Geiger, Torsten Wüstenberg, Zhenxiang Zang, Mirjam Melzer, Stephanie H. Witt, Marcella Rietschel, Markus M. Nöthen, Stefan Herms, Franziska Degenhardt, Andreas Meyer-Lindenberg & Carolin Moessnang. (2024). Longitudinal markers of cognitive procedural learning in fronto-striatal circuits and putative effects of a BDNF plasticity-related variant. npj Science of Learning. https://doi.org/10.1038/s41539-024-00282-2
q2 · i? · associational · r1
Group-level curve fitting of error rates across sessions s1-s5 of a modified Sternberg task in 23 healthy adults (11 Val, 12 Met carriers), comparing linear and exponential models via likelihood ratio tests. The authors report "asuperiority of the exponential model for all conditions" with strongest evidence in the practiced condition.
"The comparison of models within genotype groups using likelihood ratio tests revealed asuperiority of the exponential model for all conditions, with strongest evidence in thepracticed condition in the Met group (see Table 2)."
Discussion¶
Related Claims¶
- In the practiced condition, 23 brain parcels showed genotype differences in exponential model fit, mostly favoring the Met group and concentrated in frontal and striatal regions — related
- BDNF Val66Met genotype is associated with behavioral learning curves: Met-allele carriers show higher learning gain and, per the authors, less efficient early learning — related
- Repeated procedural training produces exponential decay of brain activation in a distributed set of regions including fronto-striatal circuits, paralleling exponential improvement in task performance — related
- Behavioral self-regulation development between ages three and seven is best described by an exponential (nonlinear) growth curve — related
- Quadratic latent growth fits MAP longitudinal achievement data better than linear growth across ten states — related
- Growth models show substantial variability in the shape and rate of learning, but no statistically significant EAPGS associations with that variation — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
exponential-model-behavioral-procedural-learning-curves |
title |
Behavioral learning curves in a procedural working memory task are better fit by an exponential model than a linear model across five training sessions |
description |
Behavioral learning curves in a procedural working memory task are better fit by an exponential model than a linear model across five training sessions |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
generated |
by process:wiki-ingest on 2026-10-26 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — lena-s-geiger-2024 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.