The complexity of the socioeconomic environment means that it is challenging to make decisions that rely on cognitive information. Decision makers normally cannot obtain a precise or sufficient level of knowledge about the problem domain and hence must provide multiple answers with interval values to depict them. This makes cognizing and decision making very difficult. To address this issue, this paper proposes a novel cognitive information-based decision-making algorithm with interval-valued q-rung picture fuzzy (IVq-RPtF) numbers. We first define the concept of the IVq-RPtF set, including the basic definition, operational laws, a score function, and an accuracy function. Considering the interrelationship between attributes, we then present the IVq-RPtF Heronian mean (IVq-RPtFHM) operators using the new operational laws. Moreover, we discuss the properties of IVq-RPtFHM operators, such as monotonicity, commutativity, and idempotency. Finally, we use a numerical example to verify the viability of the proposed method. The results show that the proposed method effectively expresses multiple types of interval cognitive information. The sensitivity analysis of the parameters shows that the ranking results are susceptible to parameter changes, but regardless of how the parameters change, the score values of the four alternatives in our example are in the range of [1.27, 1.66], within the basic scoring range of [1.352–1.472] for the four alternatives. Therefore, our proposed method based on IVq-RPtFHM operators has a stronger information aggregation ability than other methods. Compared with other methods, the proposed cognitive information-based decision-making algorithm is more widely applicable, avoids loss of cognitive information, and conducts a reasonable decision-making process.

社会经济环境的复杂性意味着做出依赖认知信息的决策具有挑战性。决策者通常无法获得有关问题域的准确或足够的知识水平，因此必须提供带有区间值的多个答案来描述它们。这使得认知和决策非常困难。为了解决这个问题，本文提出了一种新颖的基于认知信息的具有间隔值的q-梯级图片模糊（IVq-RPtF）数的决策算法。我们首先定义IVq-RPtF集的概念，包括基本定义，操作律，得分函数和准确性函数。考虑到属性之间的相互关系，然后使用新的运算法则介绍IVq-RPtF Heronian均值（IVq-RPtFHM）运算符。而且，我们讨论IVq-RPtFHM算子的属性，例如单调性，可交换性和幂等性。最后，我们通过一个数值例子验证了该方法的可行性。结果表明，该方法有效地表达了多种类型的区间认知信息。对参数的敏感性分析表明，排名结果易受参数变化的影响，但是无论参数如何变化，我们示例中四个替代方法的得分值均在基本评分范围内[1.27，1.66]四个替代方案的范围为[1.352–1.472]。因此，我们提出的基于IVq-RPtFHM算子的方法具有比其他方法更强的信息聚合能力。与其他方法相比，