Internal redundancy in simple response time tasks

简单响应时间任务中的内部冗余

• 批准号: RGPIN-2016-03906
• 负责人: Cousineau, Denis
• 金额: $ 2.13万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683318
• 关键词: Internal; redundancy; simple; response; time

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.**

许多简单的决策模型都基于两个主要的机制，决策阈值和感知信息的积累率。决策阈值受上下文信息，如指令，响应频率等的影响，而利率的影响，如对比度，信噪比等的物理特性的刺激，高度引用的模型包括拉特克利夫的扩散模型和布朗和希思科特的线性弹道累加器模型。近年来，这些模型已经纳入了一个额外的组成部分：在不同试验的决策阈值的变异性。添加此组件是为了解决快速错误，即，比预期更快的错误响应。*一个模型的一个新的组成部分改善了拟合的事实并不是增加它的充分理由。阈值的可变性应是可观察的，以便可以直接评估其存在及其对性能的影响。在这个研究项目中，我首先提出了一种新的绘图类型，条件内延迟-准确度曲线。这条曲线将使我们能够观察阈值的变化及其对正确和错误反应速度的后续影响。第二，我检查阈值的变化是否在学习中起作用。一种可能性是阈值是波动的，使得系统可以找到最佳阈值。第三，我研究的影响阈值变化的响应时间分布。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，每个分布具有略微不同的偏移，从而导致左尾。最后，由于扩散和线性弹道累积模型不能适应快速错误，高精度和适度的响应时间分布的偏斜，提出了一种新的基于内部冗余的简单决策模型。作为项目的一部分，人类实验探索简单的决策任务。词汇判断任务是一种常用的任务，在该任务中，被试必须尽可能快地判断由字母组成的刺激是单词还是非单词。我还探讨了两个任务的刺激属性被正式操纵。在一个任务中，属性必须联合权衡（所有属性对决策都很重要，如在相同-不同任务中，Bamber，1969）;在另一个任务中，属性必须分开权衡（只有一个属性足以做出决策，如在冗余属性目标检测任务中，米勒，1982）。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。在这里，我假设快速错误是响应速度和阈值变化的连续体。这种统一的观点应该有助于理解快速错误的原因和作用，更好地确定决策模型，并对培训方案产生影响。