Accuracy-only logistic psychometric fits support a two-timescale model but cannot identify which decision component changed¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Logistic psychometric function fits to accuracy alone selected an intermediate-complexity model combining continuous and day-resetting change, but unlike the DDM fits provided no mechanistic account of what changed on which timescale. → 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

q2 · i? · associational · r2

Comparison fits of logistic psychometric functions to accuracy-only data (best model LOOIC = 61182.7, combining continuous and day-resetting threshold change) mirrored the DDM model ranking, supporting two timescales of change, but could not attribute change to drift rate versus response boundary.

"These results, as in the main results, supported an intermediate- complexity model involving two timescales of change. Yet, unlike the DDM models, the logistic fits were unable to provide any mechanistic account regarding what changed on which timescale"

Discussion¶

Related Claims¶

• By-participant model fits replicate the continuous DR, flexible RB result for most individual learners — related
• Model comparison favors continuous drift rate with flexible response boundary over constant, fully continuous, and day-resetting alternatives — related
• Drift rate increases as a continuous exponential function of cumulative trial number across four days of perceptual training — related
• Response time reliably decreased at all coherence levels while accuracy improved reliably only at intermediate coherence levels — related