Single-Trial Linear Ballistic Accumulator (STLBA)

Even in the simplest, controlled environments, human decision-making is profoundly variable. For example, when making a sequence of decisions about identical stimuli, the speed of information processing commonly fluctuates from one decision to the next, perhaps due to noise in the perceptual system or shifts in attention. Another aspect of the decision-making process that could differ from one decision to the next is the level of response caution that is exercised by the decision maker, that is, the amount of information that is required to make the decision. If response caution is low, a decision is made based on only a limited amount of information, but when response caution is high, more information is required before a decision is made. The Single-Trial Linear Ballistic Accumulator (STLBA) model is intended to quantify these trial-to-trial fluctuations in decision-making behavior.

STLBA accounts for the variability in RT that is due to the speed of information processing (called drift in the model). Complementary, STLBA accounts for the variability in RT that is due to response caution (quantified by variability in start point).

Download: You can download R code to compute single-trial parameters here .

This code requires that you first compute group-level LBA parameters. A good introduction on fitting the LBA model is Donkin, C., Averell, L., Brown, S., & Heathcote, A. (2009). Getting more from accuracy and response time data: Methods for fitting the Linear Ballistic Accumulator. Behavior Research Methods, 41, 1095-1110, which can be downloaded here .

Selected publications involving STLBA:

Gluth, S. & Meiran, N. (2019). Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data. eLife.

Gluth, S., & Rieskamp, J. (2016). Variability in behavior that cognitive models do not explain can be linked to neuroimaging data. Journal of Mathematical Psychology

Boehm, U., Van Maanen, L. , Forstmann, B.U., & Van Rijn, H. (2014). Trial-by-trial fluctuations in CNV amplitude reflect anticipatory adjustment of response caution. Neuroimage, 96 , 95-105.

Ho, T., Brown, S.D., Van Maanen, L. , Forstmann, B.U., Wagenmakers, E.-J., & Serences, J. (2012). The optimality of sensory processing during the speed-accuracy tradeoff. Journal of Neuroscience, 31 , 7992-8003

Turner, B., Van Maanen, L. & Forstmann, B.U. (2015). Combining cognitive abstractions with neurophysiology: The neural drift diffusion model. Psychological Review, 122, 312-336.

Van Maanen, L., Brown, S.D., Eichele, T., Wagenmakers E.-J., Ho, T., Serences, J., & Forstmann, B.U. (2011). Neural correlates of trial-to-trial adjustments in response caution. Journal of Neuroscience, 31, 17488-17495