A Speed-Accuracy Tradeoff Hierarchical Model Based on Cognitive Experiment

Abstract

Most tests are administered within an allocated time. Due to the time limit, examinees might have different trade-offs on different items. In educational testing, the traditional hierarchical model cannot adequately account for the tradeoffs between response time and accuracy. Because of this, some joint models were developed as an extension of the traditional hierarchical model based on covariance. However, they cannot directly reflect the dynamic relationship between response time and accuracy. In contrast, response moderation models took the residual response time as the independent variable of the response model. Nevertheless, the models enlarge the time effect. Alternatively, the speed-accuracy tradeoff (SAT) model is superior to other experimental models in the SAT experiment. Therefore, this paper incorporates the SAT model with the traditional hierarchical model to establish a SAT hierarchical model. The results demonstrated that the Bayesian Markov chain Monte Carlo (MCMC) algorithm performed well in the SAT hierarchical model of parameters by using simulation. Finally, the deviance information criterion (DIC) more preferred the SAT hierarchical model than other models in empirical data. This means that it is indispensable to add the effect of response time on accuracy, but likewise should limit the effect on the empirical data.

Joint Modeling of Response Accuracy and Time in Between-Item Multidimensional Tests Based on Bi-Factor Model

“…Therefore, some other distribution models should be considered, such as Shifted Wald distribution ( Anders et al, 2016 ) and the semi-parameter model ( Wang et al, 2013 ). Finally, the joint hierarchical model cannot fully explain the relationship between the RT and accuracy (e.g., Meng et al, 2015 ; Guo et al, 2020 ). Therefore, a dependent joint hierarchical model can be obtained with some extensions.…”

“…To make full use of RTs, much of the literature has joined RT and item response accuracy (RA) in a unidimensional item response theory (IRT) modeling framework (e.g., Meng et al, 2015 ; Bolsinova and Tijmstra, 2018 ; Guo et al, 2020 ). Among these, the most popular is a two-level hierarchical framework for RA and RT ( van der Linden, 2007 ).…”

Post-error Slowing Reflects the Joint Impact of Adaptive and Maladaptive Processes During Decision Making

“…This in turn depends on the task characteristics, such as its global difficulty or on the speed-accuracy tradeoff (SAT) policy of subjects performing the task. Indeed, most decisions require balancing speed and accuracy, making the SAT a universal property of behavior (Henmon, 1911 ; Rinberg et al, 2006 ; Salinas et al, 2014 ; Guo et al, 2020 ; Reynaud et al, 2020 ; Miletić et al, 2021 ). Humans and other non-human animals are able to adjust their SAT depending on the context, favoring either hasty (i.e., high speed, low accuracy) or cautious (i.e., low speed, high accuracy) decision policies (Chittka et al, 2009 ; Heitz, 2014 ; Spieser et al, 2017 ; Thura, 2020 ).…”

Post-error slowing reflects the joint impact of adaptive and maladaptive processes during decision making

“…This in turn depends on task characteristics, such as the global task difficulty or the manipulation of the decision speed. Indeed, most decisions require balancing speed and accuracy, making the speed-accuracy tradeoff (SAT) a universal property of behavior (Henmon 1911, Rinberg et al 2006, Salinas et al 2014, Guo et al 2019, Reynaud et al 2020, Miletic et al 2021). Humans and other non-human animals are able to adjust their SAT depending on context, favoring either hasty (i.e., high speed, low accuracy) or cautious (i.e., low speed, high accuracy) decision policies (Chittka et al 2009, Heitz 2014, Spieser et al 2017, Thura 2020).…”