Model comparison favors continuous drift rate with flexible response boundary over constant, fully continuous, and day-resetting alternatives¶

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? The continuous DR, flexible RB model improved fit over the flexible DR flexible RB model (ΔLOOIC = −301.2), while continuous and day-resetting RB constraints fit worse; Bayes factor comparisons agreed. → Cochrane 2023

Evidence¶

Cochrane 2023¶

Cochrane, A., Sims, C. R., Bejjanki, V. R., Green, C. S., & Bavelier, D. (2023). Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making. npj Science of Learning 8:19. https://doi.org/10.1038/s41539-023-00168-9

q3 · i? · associational · r2

LOOIC-based comparison of six candidate DDM parameterizations fit to the four-day joint RT and accuracy data; the continuous DR flexible RB model beat the fully flexible model (ΔLOOIC = −301.2), and the day-resetting RB variant fit worse (ΔLOOIC = 1931.6). Bridge-sampling Bayes factors gave the same ranking.

"In contrast, a model in which DR was a continuous function of overall trial number while RB flexibly changed did improve model fit over the more complex ( flexible DR flexible RB) model ( ΔLOOIC = −301.2)."

Discussion¶

Related Claims¶

• By-participant model fits replicate the continuous DR, flexible RB result for most individual learners — related
• Drift rate increases as a continuous exponential function of cumulative trial number across four days of perceptual training — related
• Accuracy-only logistic psychometric fits support a two-timescale model but cannot identify which decision component changed — related
• Response boundary changes flexibly, with within-day decreases in most participants and primarily day-1-to-2 between-day decreases — a narrower finding that bears on this claim