Mathematical and Computational Models of Cognition, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 187

Special Issue Editor


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Guest Editor
Consortium for the Advancement of Cognitive Science, Psychology Department, College of Arts and Sciences, Ohio University, Athens, OH 45701, USA
Interests: mathematical modeling; applied mathematics; cognitive science; computational modeling; psychophysics; mathematical psychology; artificial intelligence
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Special Issue Information

Dear Colleagues,

One of the ultimate goals of Cognitive Science is to discover the mathematical laws and computational processes that govern human behavior and the human mind, as well as to achieve this with the systematicity and rigor that are found in the physical sciences. Mathematical and computational modeling are key tools in accomplishing this lofty goal. Indeed, the development of such models is crucial for rigorous theory development, measurement, and testing in Cognitive Science and Psychology. Fortunately, with the advancement of computing technologies and an unprecedented increase in computing resources, there has never been a more fertile period in human history for the successful formulation, application, and testing of mathematical and computational models of human cognitive phenomena and related processes. This Special Issue has two aims. The first is to collate papers that propose, apply, and/or test mathematical and computational models of any of the following cognitive capacities: perception, similarity assessment, attention, memory, concept learning, categorization, language, problem solving, reasoning, and decision-making. The second aim is to inform and motivate mathematicians from different fields of mathematics to engage in cognitive modeling. Based on this, new mathematical approaches to long-standing problems may emerge, and more accurate and tenable models may be discovered. Contributions may involve any style of mathematical and computational modeling, whether deterministic or probabilistic, providing that the approach is accompanied by a plausible cognitive mechanism and adequate theory development. However, given the prevalence of probabilistic models in the field, deterministic models are especially welcomed. In addition, contributions may focus on methodological (or metamodeling theory) techniques in the form of new model-testing methods or programming tools that are designed to facilitate model construction and/or testing if these are grounded in mathematical theory.

Dr. Ronaldo Vigo
Guest Editor

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Keywords

  • mathematical cognitive science
  • cognitive modeling
  • mathematical modeling
  • cognitive science
  • computational modeling
  • metamodeling
  • psychophysics
  • mathematical psychology
  • cognition
  • perception
  • concept learning
  • categorization
  • memory
  • reasoning
  • problem solving
  • similarity assessment
  • decision-making
  • attention

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Published Papers (1 paper)

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Research

34 pages, 2542 KiB  
Article
Memory Constraints in Uncertainty Misestimation: A Computational Model of Working Memory and Environmental Change Detection
by Li Xin Lim, Rei Akaishi and Sébastien Hélie
Mathematics 2025, 13(15), 2431; https://doi.org/10.3390/math13152431 - 28 Jul 2025
Abstract
Reinforcement learning models often rely on uncertainty estimation to guide decision-making in dynamic environments. However, the role of memory limitations in representing statistical regularities in the environment is less understood. This study investigated how limited memory capacity influence uncertainty estimation, potentially leading to [...] Read more.
Reinforcement learning models often rely on uncertainty estimation to guide decision-making in dynamic environments. However, the role of memory limitations in representing statistical regularities in the environment is less understood. This study investigated how limited memory capacity influence uncertainty estimation, potentially leading to misestimations of outcomes and environmental statistics. We developed a computational model incorporating active working memory processes and lateral inhibition to demonstrate how relevant information is selected, stored, and used to estimate uncertainty. The model allows for the detection of contextual changes by estimating expected uncertainty and perceived volatility. Two experiments were conducted to investigate limitations in information availability and uncertainty estimation. The first experiment explored the effect of cognitive load on memory reliance for uncertainty estimation. The results show that cognitive load diminished reliance on memory, lowered expected uncertainty, and increased perceptions of environmental volatility. The second experiment assessed how outcome exposure conditions affect the ability to detect environmental changes, revealing differences in the mechanisms used for environmental change detection. The findings emphasize the importance of memory constraints in uncertainty estimation, highlighting how misestimation of uncertainties is influenced by individual experiences and the capacity of working memory (WM) to store relevant information. These insights contribute to understanding the role of WM in decision-making under uncertainty and provide a framework for exploring the dynamics of reinforcement learning in memory-limited systems. Full article
(This article belongs to the Special Issue Mathematical and Computational Models of Cognition, 2nd Edition)
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