GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour

Abstract

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, few decision models exist that enable a straightforward characterization of the gaze-choice association at 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://glambox.readthedocs.io.

Humans actively sample evidence to support prior beliefs

“…To further investigate how attention, when freely allocated, shapes the accumulation of evidence and choice bias, we modelled the data from both viewing conditions using the Gaze-weighted Linear Accumulator Model (GLAM; Molter et al, 2019 ; Sepulveda et al, 2020 ; Thomas et al, 2019 ). GLAM belongs to the family of race models with an additional modulation by visual attention ( Figure 5A ).…”

“…The Gaze-weighted Linear Accumulator Model ( Thomas et al, 2019 ; Molter et al, 2019 ) is part of the family of linear stochastic race models in which different alternatives (i; left or right) accumulate evidence (Ei) until a decision threshold is reached by one of them, determining the chosen alternative. The accumulator for an individual option was defined by the expression: …”

“…Larger values of τ indicate stronger sensitivity to small differences in absolute evidence (A i ). Given that R i represents an average of the relative evidence across the entire trial, the drift rate in E i can be assumed to be constant, which enables the use of an analytical solution for the passage of time density (for details see Thomas et al, 2019 ; Molter et al, 2019 ). Notice that unlike the aDDM ( Krajbich et al, 2010 ), GLAM does not deal with the dynamics of attentional allocation process in choice.…”

Uncovering the computational mechanisms underlying many-alternative choice

“…In line with many empirical findings (e.g., Krajbich et al, 2010 ; Krajbich and Rangel, 2011 ; Lopez-Persem et al, 2016 ; Tavares et al, 2017 ; Smith and Krajbich, 2019 ; Thomas et al, 2019 ), we also considered a relative evidence accumulation model (as captured by the GLAM; Thomas et al, 2019 ; Molter et al, 2019 ), which assumes that individuals accumulate and compare noisy evidence in favour of each item relative to the others. As with the IAM, a choice is made as soon as the accumulated relative evidence for an item reaches a predetermined decision threshold (see Materials and methods).…”

“…The GLAM ( Thomas et al, 2019 ; Molter et al, 2019 ) assumes that choices are driven by the accumulation of noisy evidence in favour of each available choice alternative i . As for the IAM ( Equations 8–12) , a choice is determined by the first accumulator that reaches a common pre-defined decision boundary b ( ).…”