Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of great importance to develop frameworks that allow us to evaluate sequence learning and processing in a domain agnostic fashion, whilst simultaneously providing a link to formal theories of computation and computability. To address this need, we introduce two complementary software tools: SymSeq, designed to rigorously generate and analyze structured symbolic sequences, and SeqBench, a comprehensive benchmark suite of rule-based sequence processing tasks to evaluate the performance of artificial learning systems in cognitively relevant domains. In combination, SymSeqBench offers versatility in investigating sequential structure across diverse knowledge domains, including experimental psycholinguistics, cognitive psychology, behavioral analysis, neuromorphic computing and artificial intelligence. Due to its basis in Formal Language Theory (FLT), SymSeqBench provides researchers in multiple domains with a convenient and practical way to apply the concepts of FLT to conceptualize and standardize their experiments, thus advancing our understanding of cognition and behavior through shared computational frameworks and formalisms. The tool is modular, openly available and accessible to the research community.

翻译：序列结构是自然认知与行为的多个领域（如语言、运动与决策）的关键特征。同样，它也是我们希望应用人工智能的任务的核心属性。因此，开发能够在领域无关的方式下评估序列学习与处理，同时提供与计算及可计算性形式理论联系的框架至关重要。为满足这一需求，我们引入了两个互补的软件工具：SymSeq，旨在严格生成和分析结构化符号序列；以及SeqBench，一个全面的基于规则的序列处理任务基准测试套件，用于评估人工学习系统在认知相关领域中的性能。两者结合，SymSeqBench为跨多个知识领域（包括实验心理语言学、认知心理学、行为分析、神经形态计算与人工智能）研究序列结构提供了多功能性。基于形式语言理论（FLT），SymSeqBench为多个领域的研究者提供了一种便捷实用的方式，以应用FLT概念来概念化和标准化其实验，从而通过共享的计算框架与形式化方法推进我们对认知与行为的理解。该工具采用模块化设计，对研究社区开放且易于获取。