Improving Decision Making Models by Considering Attention Processes: The Gaze-Weighted Advantage Race Diffusion Model
Considering the type of information that people pay attention to when making decisions and how long their attention lasts can improve the predictions of decision-making models. Previous research has demonstrated that options receiving the most attention during the decision-making process are typically the ones that are chosen. Additionally, more valuable options tend to receive more attention than inferior options. However, the interaction between these two effects is not yet fully understood. There are two possible ways in which attention and subjective value could interact: attending to an option could amplify its subjective value in a multiplicative way, or attention could increase its choice probability in an additive way. Although some studies suggest a multiplicative interaction between attention and value (Smith & Krajbich, 2019), others provide evidence for an additive interaction (Cavanagh et al., 2014). The attentional drift-diffusion model (aDDM) successfully explained the effect of attention by assuming a multiplicative interaction between attention and value (Krajbich et al., 2010). The model posits that when individuals pay attention to an option, the accumulation process for that option is amplified. More recently, the gaze-weighted linear accumulator model (GLAM) following aDDM has been suggested, which assumes independent accumulators for each option and uses the gaze percentage for each option instead of fixation duration (Thomas et al., 2019). Models that assume a multiplicative interaction between attention and value have the advantage that they can predict magnitude effects in decision making, where options with higher subjective values are chosen faster than those with lower values. The present study introduces the Gaze-weighted Advantage Race Diffusion (GARD) model, which simultaneously assumes both additive and multiplicative interactions between attention and value. We rigorously tested this new model on three existing datasets on human food choice by Krajbich et al. (2010), Smith and Krajbich (2018), and Chen and Krajbich (2016). Our results show that the GARD model outperforms existing models that assume only a multiplicative interaction between attention and value, indicating that it provides a more accurate description of people's decision-making processes.