Think fast! The implications of emphasizing urgency in decision-making

Abstract

<p>Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.</p>

Age differences in diffusion model parameters: a meta-analysis

“…More specifically, for instructions that focused either on accuracy or speed the teams that used the diffusion model often found an effect in non-decision time (in addition to an effect in threshold separation), whereas the LBA teams often detected an effect in drift rate. The reasons for this pattern of results will need to be investigated further in future research (for a recent discussion of this topic, see Evans, 2020;. Based on these varying findings, we hypothesize that somehow different age effects might emerge if older and younger adults are compared based on different sequential sampling models.…”

An integrated theory of deciding and acting.

“…Although investigators generally observed the predicted parametric modulations, some experimental manipulations modulated additional parameters. For example, manipulations of SAT have produced additional modulations of drift rate (Donkin et al, 2011; Heathcote & Love, 2012; Ho et al, 2012; Rae et al, 2014; Starns et al, 2012) and nondecision time (Arnold et al, 2015; de Hollander et al, 2016; Huang et al, 2015; Servant et al, 2018), and these violations seem to be model-dependent (Evans, 2020). Dutilh et al (2019) recently conducted a blinded collaborative assessment of the quality of inferences from evidence accumulation model analyses.…”

Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans

“…There is an ongoing debate on how accurately different models can mimic each other when estimating the non-decision time (Donkin et al 2011;Goldfarb et al 2014;Lerche and Voss 2018). The DDM, for example, tends to predict longer non-decision times than LBA (Dutilh et al 2019), as well as might be more susceptible to urgency manipulations (Evans 2020). Although an extended DDM has been shown to account for magnitude effects (Ratcliff et al 2018), the drift rate of a DDM represents the relative signal difference between the two options.…”