Traditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information (Yang et al., 2016; Hoppe & Rothkopf, 2016; Chukoskie et al., 2013). These fixations also causally affect one’s choices, biasing them toward the longer-fixated item (Shimojo et al., 2003; Armel et al., 2008). We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models (Krajbich et al., 2010; Song et al., 2019), we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings (Averbeck et al., 2006; Cohen & Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources.
bioRxiv Subject Collection: Neuroscience