Prezenski Sabine, Brechmann André, Wolff Susann, Russwinkel Nele
Cognitive Modeling in Dynamic Human-Machine Systems, Department of Psychology and Ergonomics, Technical University BerlinBerlin, Germany.
Special Lab Non-Invasive Brain Imaging, Leibniz Institute for NeurobiologyMagdeburg, Germany.
Front Psychol. 2017 Aug 4;8:1335. doi: 10.3389/fpsyg.2017.01335. eCollection 2017.
Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
决策是一种基于感知、注意力和记忆等认知过程的高级认知过程。现实生活中的情况需要做出一系列决策，每个决策都取决于来自潜在变化环境的先前反馈。为了更好地理解动态决策的潜在过程，我们在一个基于复杂规则的类别学习任务中应用了认知建模方法。在这里，参与者首先需要识别定义目标类别的两条规则的结合，然后适应反馈偶然性的反转。我们为这个动态决策任务的核心方面开发了一个ACT-R模型。我们模型的一个重要目标是，它提供了一个关于如何解决此类任务的一般性说明，并且只需进行微小更改，就适用于其他刺激材料。该模型是作为一种基于范例和基于规则的方法的混合体实现的，其中还纳入了感知运动和元认知方面。该模型通过首先尝试单特征策略，然后由于反复的负面反馈而切换到双特征策略来解决分类任务。总体而言，该模型以与参与者相似的方式解决任务，包括通常成功的初始学习以及反馈偶然性变化后的反转学习。此外，并非所有参与者在两个学习阶段都成功这一事实也反映在建模数据中。然而，我们发现与人类数据相比，建模数据的方差更大且整体性能更低，这可能与参与者的感知偏好或应用的额外知识和规则有关。在下一步中，可以在模型中实现这些方面，以实现更好的整体拟合。鉴于参与者之间决策性能存在较大的个体差异，来自行为、心理生物学和神经生理学数据的关于潜在认知过程的额外信息可能有助于优化该模型的未来应用，使其能够转移到其他可比动态决策任务的领域。
