Internal redundancy in simple response time tasks

许多简单决策模型都基于两种主要机制，即决策阈值和感知信息的积累速率。决策阈值受上下文信息（如指令、响应频率等）的影响，而决策速率受刺激的物理特性（如对比度、信噪比等）的影响。经常被引用的模型包括Ratcliff的扩散模型和Brown和Heathcote的线性弹道累加器模型。近年来，这些模型加入了一个额外的组成部分：跨试验决策阈值的可变性。添加此组件是为了解决快速错误，即错误响应比预期的要快。****模型的新组件改善拟合的事实并不能作为添加新组件的充分理由。阈值的可变性应该是可以观察到的，以便可以直接评估其存在和对性能的影响。在这个研究项目中，我首先提出了一种新型的图，即条件内延迟-精度曲线。这条曲线将使我们能够观察到阈值的可变性及其对正确和错误反应速度的后续影响。其次，我研究了阈值的可变性是否在学习中起作用。一种可能性是阈值是波动的，这样系统就可以找到最佳的阈值。第三，我研究了阈值可变性对响应时间分布的影响。如果决策阈值在试验之间波动，则响应时间分布是许多分布的叠加，每个分布的偏移量略有不同，导致左尾。最后，针对扩散模型和线性弹道蓄能器模型不能适应响应时间分布的快速误差、高精度和适度偏差的问题，提出了一种基于内部冗余的简单决策模型。****作为该项目的一部分，人类实验探索简单的决策任务。词汇判断任务是一项常用的任务，在这项任务中，参与者必须尽快决定由字母组成的刺激是单词还是非单词。我还探讨了刺激属性被正式操纵的两个任务。在一个任务中，这些属性必须一起权衡（所有的属性对决策都很重要，就像在相同-不同的任务中一样，Bamber, 1969）；在另一种情况下，必须对属性进行分离加权（只有一个属性对决策是足够的，就像在冗余属性目标检测任务中一样，Miller, 1982）。****在文献中，快速错误通常被认为是由外在机制引起的快速猜测。在这里，我假设快速错误是响应速度和阈值可变性的连续体。这种统一的观点应该有助于理解快速错误的原因和作用，更好地具体说明决策模型，并对培训方案产生影响

Many models of simple decision making are based on two major mechanisms, a decision threshold and a rate of accumulation for the perceived information. The decision thresholds are influenced by contextual information such as instructions, response frequencies, etc. whereas rates are influenced by the physical properties of the stimuli such as contrast, signal-to-noise ratio, etc. Highly-cited models include Ratcliff's Diffusion model and Brown and Heathcote's Linear Ballistic Accumulator model. In recent years, these models have incorporated an additional component: variability in the decision threshold across trials. This component was added to account for fast errors, i.e., erroneous responses that are faster than expected.****The fact that a new component of a model improves fit is not a sufficient justification for its addition. Variability in threshold should be made observable so that its existence and impact on performance can be assessed directly. In this research project, I first propose a new type of plot, the within-condition latency-accuracy curve. This curve will allow us to observe variability in threshold and its subsequent effect on the speed of correct and erroneous responses. Second, I examine whether variability in thresholds has a role in learning. One possibility is that the thresholds are fluctuating so that the system can find optimal threshold values. Third, I examine the impact of threshold variability on response time distributions. If a decision threshold is fluctuating between trials, the response time distribution is the superposition of many distributions, each with slightly different offsets, resulting in a left tail. Finally, as the Diffusion and the Linear Ballistic Accumulator models cannot accommodate fast errors, high accuracy and modest skew in response time distributions, a new model of simple decision making based on internal redudancy is proposed.****As part of the project, experiments with humans explore simple decision making tasks. The lexical decision task is a commonly-used task in which participants must decide as quickly as possible if a stimulus composed of letters is a word or a non-word. I also explore two tasks where the stimulus attributes are manipulated formally. In one task, the attributes must be weighed in conjunction (all the attributes matter for a decision, as in the same-different task, Bamber, 1969); in the other, the attributes must be weighed disjunctively (only one attribute is sufficient for a decision, as in the redundant-attribute target detection task, Miller, 1982).****Fast errors have often been treated in the literature as fast guesses caused by an extrinsic mechanism. Here, I assume that fast errors are on a continuum of response speed and threshold variability. This unifying view should help understand the causes and the role of fast errors, better specify models of decision making and have implications for training programmes.**