Abstract
We use computational modeling to examine the ability of evidence accumulation models to produce the reaction time (RT) distributions and attentional biases found in behavioral and eye-tracking research. We focus on simulating RTs and attention in binary choice with particular emphasis on whether different models can predict the late onset bias (LOB), commonly found in eye movements during choice (sometimes called the gaze cascade). The first finding is that this bias is predicted by models even when attention is entirely random and independent of the choice process. This shows that the LOB is not evidence of a feedback loop between evidence accumulation and attention. Second, we examine models with a relative evidence decision rule and an absolute evidence rule. In the relative models a decision is made once the difference in evidence accumulated for 2 items reaches a threshold. In the absolute models, a decision is made once 1 item accumulates a certain amount of evidence, independently of how much is accumulated for a competitor. Our core result is simple—the existence of the late onset gaze bias to the option ultimately chosen, together with a positively skewed RT distribution means that the stopping rule must be relative not absolute. A large scale grid search of parameter space shows that absolute threshold models struggle to predict these phenomena even when incorporating evidence decay and assumptions of either mutual inhibition or feedforward inhibition.
Highlights
When choosing between alternatives, individuals spend varying amounts of time deliberating over their choice
Simulating the relative threshold model shows that there are parameter combinations which satisfy all five of these criteria simultaneously. These are in a small minority, but the fact that these values are found demonstrates that the underlying properties of this two parameter relative threshold model allow it to simultaneously fit the behavior observed in visual attention and choice experiments
The simulations show that it is possible for a model of decision making to predict the late onset bias (LOB) effect without any modulation of attention or feedback loop
Summary
We use computational modeling to examine the ability of evidence accumulation models to produce the reaction time (RT) distributions and attentional biases found in behavioral and eye-tracking research. In the relative models a decision is made once the difference in evidence accumulated for 2 items reaches a threshold. As one item begins to be preferred, there is a bias to fixate on it more, which in turn increases the probability of accumulating evidence in its favor (Glöckner & Betsch, 2008; Glöckner & Herbold, 2011; Shimojo et al, 2003) This explains findings that the cascade is larger when individuals are selecting the option they like, as opposed to dislike (Mitsuda & Glaholt, 2014; Simion & Shimojo, 2006). Attention is allocated randomly and the cascade emerges because of a fundamental property of the decision making process This interpretation posits only that individuals are biased to accumulating evidence in favor of the currently attended item.
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