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.
Highlights
A plethora of empirical findings has established an association between gaze allocation and decision behaviour on the group-level
With the Gaze-weighted linear accumulator model (GLAM; [21]), we have proposed an analytical tool that allows the model-based investigation of the relationship between gaze allocation and choice behaviour at the level of the individual, in choice situations involving more than two alternatives, solely requiring participants’ choice, response time (RT) and gaze data, in addition to estimates of the items’ values
They explore whether (1) gaze biases are present on the individual level and (2) the strength of this association varies between individuals
Summary
Gaze-weighted linear accumulator model detailsLike the aDDM, the GLAM assumes that preference formation, during a simple choice process, is guided by the allocation of visual gaze (for an overview, see Fig 1). For γ values less than 1, the absolute decision signal Ai is discounted, resulting in generally higher choice probabilities for items that have been looked at longer. For γ values less than 0, the sign of the absolute decision signal Ai changes, when the item is not looked at, leading to an overall even stronger gaze bias, as evidence for these items is actively lost, when they are not looked at. This type of gaze-dependent leakage mechanism is supported by a variety of recent empirical findings [15, 21]. To determine the relative decision signals, the average absolute decision signals A i are transformed in two steps: First, for each item i, the relative evidence R i is computed as the difference between the average absolute decision signal of the item A i (Eq 1) and the maximum of all other average absolute decision signals A j61⁄4i ( obtained from Eq 2) is computed:
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