An uncertainty-based model of the effects of fixation on choice

Abstract

When people view a consumable item for a longer amount of time, they choose it more frequently; this also seems to be the direction of causality. The leading model of this effect is a drift-diffusion model with a fixation-based attentional bias. Here, we propose an explicitly Bayesian account for the same data. This account is based on the notion that the brain builds a posterior belief over the value of an item in the same way it would over a sensory variable. As the agent gathers evidence about the item from sensory observations and from retrieved memories, the posterior distribution narrows. We further postulate that the utility of an item is a weighted sum of the posterior mean and the negative posterior standard deviation, with the latter accounting for risk aversion. Fixating for longer can increase or decrease the posterior mean, but will inevitably lower the posterior standard deviation. This model fits the data better than the original attentional drift-diffusion model but worse than a variant with a collapsing bound. We discuss the often overlooked technical challenges in fitting models simultaneously to choice and response time data in the absence of an analytical expression. Our results hopefully contribute to emerging accounts of valuation as an inference process.

Humans reconfigure target and distractor processing to address distinct task demands

“…Our results also provide insight into the dynamic implementation of attentional control. Previous work has shown that within-trial attentional dynamics play an important role in both decision making (Callaway et al, 2021; Krajbich et al, 2010; Li and Ma, 2021; Westbrook et al, 2020) and cognitive control (Adkins and Lee, 2021; Hardwick et al, 2019; Servant et al, 2014; Weichart et al, 2020; White et al, 2011). These foundational experiments have largely focused on spatial attention, with far less known about the dynamics of feature-based attention, where processing of targets and distractors is less mutually constrained.…”

Value certainty in drift-diffusion models of preferential choice.

“…Another approach to the role of uncertainty on choice has been suggested in a recent study by Li and Ma (2021), who proposed a model in which the decision maker chooses based on a comparison of the subjective utility of each option. In their model, utility is a This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

Considering what we know and what we don't know: Expectations and confidence guide value integration in value-based decision-making

“…Here we show that, as with attention, the influence of value confidence on choice critically depends on a decision-maker's expectations. This finding speaks against uncertainty aversion (or its converse, confidence bonus) as the main driver of choice variability associated with value confidence (Li & Ma, 2021). Rather it suggests a more nuanced role for value confidence in decision-making.…”

“…Like attention, value confidence has been proposed to confer a choice benefit at least as long as options are appetitive (Lee & Coricelli, 2020;Li & Ma, 2021). On its face, this proposal resonates with ubiquitous uncertainty aversion, as well as the idea that confidence itself may be a value signal (Lebreton et al, 2015).…”

“…Most research on attention and choice has used gaze as a proxy for attention, allowing participants to freely fixate between the items in the choice set (Orquin & Mueller Loose, 2013). However, recent research has suggested that this fixation behavior is itself driven by estimated value and uncertainty (Song et al, 2019;Callaway et al, 2021;Jang et al, 2021;Li & Ma, 2021). Thus, to directly measure the causal influence of attention on choice, we instead manipulated fixation time to each item (cf.…”