Internal redundancy in simple response time tasks

许多简单决策模型基于两个主要机制：决策阈值和感知信息的累积率。决策阈值受背景信息的影响，如指令、反应频率等，而速率受刺激的物理属性影响，如对比度、信噪比等。被广泛引用的模型包括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：试验中决策阈值的可变性。添加该组件是为了解决快速错误，即比预期更快的错误响应。*模型的新组件提高了匹配度这一事实并不是添加该组件的充分理由。应使阈值的可变性可观察到，以便可以直接评估其存在及其对业绩的影响。在本研究项目中，我首先提出了一种新的曲线图--条件内潜伏期-准确度曲线。这条曲线将允许我们观察阈值的变化及其对正确和错误反应的速度的后续影响。其次，我考察了阈值的可变性是否对学习有影响。一种可能性是阈值在波动，因此系统可以找到最佳阈值。第三，我考察了阈值变异性对响应时间分布的影响。如果判决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，从而产生左尾。最后，由于扩散模型和线性弹道累加器模型不能适应快速误差、高精度和响应时间分布的适度偏斜，提出了一种新的基于内部校正度的简单决策模型。词汇判断任务是一项常用的任务，参与者必须尽快判断由字母组成的刺激是单词还是非单词。我还探索了两个任务，在这些任务中，刺激属性被正式操纵。在一个任务中，必须同时对属性进行加权(所有属性对决策都很重要，因为在相同-不同的任务中，Bamber，1969)；在另一个任务中，属性必须进行分离加权(只有一个属性足以进行决策，就像Miller，1982年的冗余属性目标检测任务一样)。*在文献中，快速错误通常被视为由外部机制引起的快速猜测。这里，我假设快速误差在响应速度和阈值变化性的连续体上。这一统一的观点应有助于理解快速错误的原因和作用，更好地明确决策模式，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.**