Comparison of Decision Learning Models Using the Generalization Criterion Method

Abstract

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

“…Several updating rules have been proposed in the literature; none has emerged as being generally superior (e.g., Ahn, Busemeyer, Wagenmakers, & Stout, 2008;Steingroever et al, 2013aSteingroever et al, , 2013bYechiam & Busemeyer, 2005). We therefore considered three updating rules that have received support in studies on experience-based decision making.…”

Psychopathic Personality Traits and Iowa Gambling Task Performance in Incarcerated Offenders

“…Underlying cognitive processes in the IGT were assessed using a mathematical model of the task, the Prospect Valence Learning (PVL) model (Ahn, Busemeyer, Wagenmakers, & Stout, 2008), which has yielded interesting insights into the behaviour in drug-using samples (Fridberg et al, 2010).…”

“…PVL model (Ahn et al, 2008) To examine how participants learned from their choices across the task, and how wins and losses influenced their choice strategy, we applied the PVL model to the data. The PVL model involves four parameters that are associated with different cognitive components of decision-making.…”

“…The final parameter is the choice consistency parameter reflecting the trade-off between explore/ exploit behaviour. A full description of the model can be found in Ahn et al (2008).…”

Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation

“…Computational models such as the Expectancy-Valence (EV) model (Busemeyer and Stout 2002), the Perseverance Valence Learning (PVL) model, the PVL-Delta model (Ahn et al 2008(Ahn et al , 2011Fridberg et al 2010), and the Value Plus Perseverance (VPP) model (Worthy et al 2013), consider factors such as the attention given to outcome valence (i.e., to wins vs. losses), how the recency of feedback affects future decisions, and how choices are influenced by experience (i.e., to what extent choices are random). Such models assume that the valence experienced on each trial informs a probabilistic choice mechanism, and quantifies outcome expectation and prediction error on an individual trial-by-trial basis, thereby estimating individuals' subjective experiences of the task and task-expectations, rather than objective task outcomes (Yechiam and Busemeyer 2005).…”