Speed accuracy trade-off under response deadlines

Abstract

Perceptual decision making has been successfully modeled as a process of evidence accumulation up to a threshold. In order to maximize the rewards earned for correct responses in tasks with response deadlines, participants should collapse decision thresholds dynamically during each trial so that a decision is reached before the deadline. This strategy ensures on-time responding, though at the cost of reduced accuracy, since slower decisions are based on lower thresholds and less net evidence later in a trial (compared to a constant threshold). Frazier and Yu (2008) showed that the normative rate of threshold reduction depends on deadline delays and on participants' uncertainty about these delays. Participants should start collapsing decision thresholds earlier when making decisions under shorter deadlines (for a given level of timing uncertainty) or when timing uncertainty is higher (for a given deadline). We tested these predictions using human participants in a random dot motion discrimination task. Each participant was tested in free-response, short deadline (800 ms), and long deadline conditions (1000 ms). Contrary to optimal-performance predictions, the resulting empirical function relating accuracy to response time (RT) in deadline conditions did not decline to chance level near the deadline; nor did the slight decline we typically observed relate to measures of endogenous timing uncertainty. Further, although this function did decline slightly with increasing RT, the decline was explainable by the best-fitting parameterization of Ratcliff's diffusion model (Ratcliff, 1978), whose parameters are constant within trials. Our findings suggest that at the very least, typical decision durations are too short for participants to adapt decision parameters within trials.

Global gain modulation generates time-dependent urgency during perceptual choice in humans

“…This optimal urgency signal required a fast transition from a flat early portion to a steep deflection toward the decision bound closer to the deadline (cf. refs 20, 30) that is qualitatively very different from the gradual, approximately linear urgency signals that subjects in the current study appeared to implement. As such, although our subjects responded to deadline-induced speed pressure by adjusting their decision policies in a time-dependent fashion, they failed to do so optimally.…”

“…Theoretical considerations suggest that such a time-invariant policy is sub-optimal if the potential cost of continued evidence accumulation grows with elapsed decision time, as is often the case in decision-making contexts that place a premium on fast responding1820. Yet, in support of the principle of time-invariance, recent reports have suggested that human decision-makers may fail to implement a dynamic, time-variant commitment policy that would yield higher reward rates in such settings152930. In the present study, we describe strong, convergent evidence to the contrary.…”

“…On the other hand, this may not have been the case in previous studies that imposed only small, implicit penalties for slow responses (in the form of foregone rewards; for example, ref. 30), or did not provide performance-related incentives at all (for example, ref. 31).…”

“…While these findings have illuminated the mechanistic basis of SAT regulation in non-human primates, time-invariance remains a dominant assumption in the human decision-making literature10 and recent empirical and model comparison reports have reinforced this stance15293031. Moreover, even in non-human primates, little is known about the neurophysiological source of urgency.…”

Neurodynamic Evidence Supports a Forced-Excursion Model of Decision-Making under Speed/Accuracy Instructions

“…Therefore, we do not claim that our model is better supported than urgency models, either here or in general. However, since a number of studies evaluating the concept of an urgency signal have been unable to support it, suggesting instead that standard sequential sampling models can fully account for all behavioral data (Balci et al, 2011; Karşılar et al, 2014; Hawkins et al, 2015a,b), we propose that forced-excursion model variants should at least be considered as an appropriate alternative to urgency signals, reconciling decades of model-based support for decision boundary variation with recent neural evidence.…”