RT Journal Article
SR Electronic
T1 GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 741678
DO 10.1101/741678
A1 Felix Molter
A1 Armin W. Thomas
A1 Hauke R. Heekeren
A1 Peter N. C. Mohr
YR 2019
UL http://biorxiv.org/content/early/2019/08/20/741678.abstract
AB Recent empirical findings have indicated that gaze allocation plays a crucial role in simple decision behaviour. Many of these findings point towards an influence of gaze allocation onto the speed of evidence accumulation in an accumulation-to-bound decision process (resulting in generally higher choice probabilities for items that have been looked at longer). Further, researchers have shown that the strength of the association between gaze and choice behaviour is highly variable between individuals, encouraging future work to study this association on the individual level. However, only few decision models exist that easily allow studying the gaze-choice association on the individual level, due to the high cost of developing and implementing them. The model space is particularly scarce for choice sets with more than two choice alternatives. Here, we present GLAMbox, a Python-based toolbox that is built upon PyMC3 and allows the easy application of the gaze-weighted linear accumulator model (GLAM) to experimental choice data. The GLAM assumes gaze-dependent evidence accumulation in a linear stochastic race that extends to decision scenarios with many choice alternatives. GLAMbox enables Bayesian parameter estimation of the GLAM for individual, pooled or hierarchical models, provides an easy-to-use interface to predict choice behaviour and visualize choice data, and benefits from all of PyMC3’s Bayesian statistical modeling functionality. Further documentation, resources and the toolbox itself are available at https://github.com/glamlab/glambox.