Joint RT-and-accuracy diffusion modeling of trial-level learning trajectories

Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 associational), q3 · 0 of 1 report an effect size · 2 claims rest on one study

Description

The article recommends analyzing learning with by-trial evidence-accumulation models fit to the joint distributions of response time and accuracy, rather than aggregating across sessions or relying on accuracy alone. It argues that "using only impoverished discrete-choice behavioral data could prevent or bias possible inferences regarding learning trajectories". The approach provides a clear justification for using the diffusion model's drift rate parameter as an index of perceptual learning as a function of each training trial.

Accounts

• Model learning with joint RT-and-accuracy diffusion models at the trial level rather than accuracy-only aggregated analyses: The article recommends analyzing learning with by-trial evidence-accumulation models fit to the joint distributions of response time and accuracy, rather than aggregating across sessions or relying on accuracy alone. It argues that "using only impoverished discrete-choice behavioral data could prevent or bias possible inferences regarding learning trajectories". The approach provides a clear justification for using the diffusion model's drift rate parameter as an index of perceptual learning as a function of each training trial. (Cochrane et al. (2023))

Claims

• Response time reliably decreased at all coherence levels while accuracy improved reliably only at intermediate coherence levels [+M]
• Model comparison favors continuous drift rate with flexible response boundary over constant, fully continuous, and day-resetting alternatives [+M]

Related Research Methods

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Key Sources

• 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

Page metadata

Field	Value
type	research-method
id	joint-rt-and-accuracy-diffusion-modeling-of-trial-level-learning-trajectories
title	Joint RT-and-accuracy diffusion modeling of trial-level learning trajectories
description	The article recommends analyzing learning with by-trial evidence-accumulation models fit to the joint distributions of response time and accuracy, rather than aggregating across sessions or relying on accuracy alone.
status	review
generated	by process:settle-candidates on 2026-10-26
trust	unverified — no one has confirmed this page's content

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