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Time-sensitive trajectory modeling of practice effects

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q3 · 1 of 1 report an effect size · 1 claim rests on one study

Description

The article recommends that researchers "should avoid discarding trials whenever possible", because removing early trials as practice replaces one stationarity assumption with another unprincipled one and, as the results show, is "actually discarding signal, not noise". Instead, time-sensitive analyses of pilot data can identify timescales of change and what amount of practice would leave participants at reasonably stable performance.

Accounts

  • Avoid discarding trials as practice trials; use time-sensitive analyses instead: The article recommends that researchers "should avoid discarding trials whenever possible", because removing early trials as practice replaces one stationarity assumption with another unprincipled one and, as the results show, is "actually discarding signal, not noise". Instead, time-sensitive analyses of pilot data can identify timescales of change and what amount of practice would leave participants at reasonably stable performance. (Aaron Cochrane et al. (2021))

Claims

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Key Sources

  • Aaron Cochrane and C. Shawn Green. (2021). Trajectories of performance change indicate multiple dissociable links between working memory and fluid intelligence. npj Science of Learning. https://doi.org/10.1038/s41539-021-00111-w
Page metadata
Field Value
type research-method
id time-sensitive-trajectory-modeling-of-practice-effects
title Time-sensitive trajectory modeling of practice effects
description The article recommends that researchers "should avoid discarding trials whenever possible", because removing early trials as practice replaces one stationarity assumption with another unprincipled one and, as the results show, is "actually discarding signal, not noise".
status review
generated by process:settle-candidates on 2026-10-26
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