Some task demands induce collapsing bounds: Evidence from a behavioral analysis

Abstract

Traditional models of choice-response time assume that sensory evidence accumulates for choice alternatives until a threshold amount of evidence has been obtained. Although some researchers have characterized the threshold as varying randomly from trial to trial, these investigations have all assumed that the threshold remains fixed across time within a trial. Despite decades of successful applications of these models to a variety of experimental manipulations, the time-invariance assumption has recently been called into question, and a time-variant alternative implementing collapsing decision thresholds has been proposed instead. Here, we investigated the fidelity of the collapsing threshold assumption by assessing relative model fit to data from a highly constrained experimental design that coupled a within-subject mixture of two classic response time paradigms-interrogation and free response-within a random dot motion (RDM) task. Overall, we identified strong evidence in favor of collapsing decision thresholds, suggesting that subjects may adopt a dynamic decision policy due to task characteristics, specifically to account for the mixture of response time paradigms and motion strengths across trials in the mixed response signal task. We conclude that time-variant mechanisms may serve as a viable explanation for the strategy used by human subjects in our task.

Supplemental Material for The Role of Passing Time in Decision-Making

“…The DIC weights were calculated as described in the method section of Experiment 1, with the x-axis showing different participants (ordered by their weight in favor of the two models), and y-axis being the weight associated with each model. Firstly, previous studies in the debates about fixed vs. collapsing bound models have focused on individual-level metrics (Hawkins, Forstmann, Wagenmakers, Ratcliff, & Brown, 2015;Palestro, Weichart, Sederberg, & Turner, 2018), meaning that our study remains consistent with the types of inferences made in those studies. Secondly, after calculating the group-level DIC values, closer inspection of these results suggested that the conclusions of group-level DIC values were misleading in some cases (note that the same criticism would apply to group-level WAIC, or any other group-level metric for that matter).…”

Not all Speed-Accuracy Trade-Off Manipulations Have the Same Psychological Effect

“…4), it may be that evidence accumulation models with more complex functional forms for the decreasing threshold will further improve the explanation of performance under these conditions. Therefore, further research is required to establish under which conditions more complex functional forms are necessary to explain the behavioral patterns, which might also include other SAT manipulations that do not explicitly use deadlines (e.g., Palestro et al 2018).…”

Multifaceted adaptation of the neural decision process with prior knowledge of time constraints and stimulus probability

“…Such a collapsing-bound scheme has been established in theoretical work to be optimal when sensory discriminability varies across trials (Malhotra et al 2018;Moran 2015), when there is a cost to continued accumulation (Drugowitsch et al 2012;Boehm et al 2019), or when missed deadlines are penalised (Frazier and Yu 2008). Yet, behavioral modelling evidence is extremely mixed as to whether human subjects actually collapse their decision bounds in practice (Malhotra et al 2017;Evans and Hawkins 2019;Palestro et al 2018), with several recent studies that performed formal model comparisons clearly favouring standard constant-bound models (Hawkins et al 2015;Voskuilen et al 2016). In fact, definitively establishing the role of collapsing bounds based solely on behavioural modelling is highly challenging because one of its primary qualitative expressions -increased error rates on trials with longer RTs -can be alternatively produced with increased between-trial drift rate variability , and many collapsing bound models suffer from identifiability issues due to a lack of constraints .…”