Tillman Gabriel, Van Zandt Trish, Logan Gordon D
School of Health and Life Sciences, Federation University, Ballarat, Australia.
Department of Psychology, Vanderbilt University, Nashville, TN, USA.
Psychon Bull Rev. 2020 Oct;27(5):911-936. doi: 10.3758/s13423-020-01719-6.
Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models.
当前大多数序贯抽样模型在其参数上具有试验间的随机变异性。这些变异性来源使得模型为了拟合反应时间数据而变得更加复杂，没有对数据的生成方式提供任何进一步的解释，并且最近因允许模型具有无限灵活性而受到批评。为了探索和测试对试验间变异性参数的需求，我们开发了一种简单的N选加速决策的序贯抽样模型：竞赛扩散模型。该模型通过证据累加器的竞赛做出加速决策，这些累加器在一次试验中以有噪声的方式整合信息。竞赛扩散模型不假设任何证据积累过程在试验间会有所不同，因此，该模型为关键反应时间现象提供了替代解释，比如相对于正确反应时间而言快速和缓慢的错误反应时间。总体而言，我们的论文提供了充分的理由来重新思考在序贯抽样模型中纳入试验间变异性参数的做法。
