Abstract
In many domains of psychological research, decisions are subject to a speed-accuracy trade-off: faster responses are more often incorrect. This trade-off makes it difficult to focus on one outcome measure in isolation – response time or accuracy. Here, we show that the distribution of choices and response times depends on specific task instructions. In three experiments, we show that the speed-accuracy trade-off function differs between two commonly used methods of manipulating the speed-accuracy trade-off: Instructional cues that emphasize decision speed or accuracy and the presence or absence of experimenter-imposed response deadlines. The differences observed in behavior were driven by different latent component processes of the popular diffusion decision model of choice response time: instructional cues affected the response threshold, and deadlines affected the rate of decrease of that threshold. These analyses support the notion of an “urgency” signal that influences decision-making under some time-critical conditions, but not others.
Highlights
Decision-making has been a focus of psychological research for decades
Comput Brain Behav (2020) 3:252–268 paper, we test this hypothesis in three experiments and show that choices and response times differ between cue-induced speed-accuracy trade-off (SAT) conditions and deadline-induced SAT conditions. We show that these differences can be attributed to different latent component processes of the popular diffusion decision model (DDM) of choice response time (Forstmann et al 2016; Ratcliff 1978; Ratcliff and McKoon 2008; Ratcliff and Rouder 1998)
We have provided evidence that deadline-based and cuebased manipulations of the speed-accuracy trade-off have different psychological effects on latent decision-making processes
Summary
Decision-making has been a focus of psychological research for decades. In many decision-making contexts, time pressure is of critical importance. Thresholds that sit further from the starting point (blue lines) represent a more conservative decision strategy whereby the decision-maker accumulates a greater amount of evidence before committing to a decision; this leads to slower but more accurate responses Such changes between low and high thresholds have provided a good explanation of SAT behavior across a range of contexts (Bogacz et al 2010; Forstmann et al 2008; Ratcliff and McKoon 2008; Ratcliff and Rouder 1998; Voss et al 2004; Wagenmakers et al 2008), the link between the computational explanation of SAT and its low-level neural implementation is still a matter of active research (e.g., Heitz and Schall 2012; Reppert et al 2018). Our analyses included a systematic model comparison of DDMs with fixed and decreasing threshold parametrizations encoding different ways of adjusting response caution to establish which adjustment mechanisms better explain decision processes under different forms of time pressure
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
