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Entropy
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Information-Theoretic Principles in Cognitive Systems
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Information-Theoretic Principles in Cognitive Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 964

Special Issue Editors

Prof. Dr. Giovanni Petri

E-Mail Website
Guest Editor
Network Science Institute, Northeastern University London, London E1W 1LP, UK
Interests: topology and predictability of complex systems; computational neuroscience; cognitive control
Prof. Dr. Jonathan D. Cohen

E-Mail Website
Guest Editor
Department of Psychology, Princeton University, Princeton, NJ 08544, USA
Interests: neurobiological mechanisms; cognitive control; psychiatric disorder

Special Issue Information

Dear Colleagues,

Information theory has become a crucial tool in understanding and modeling the complexities of cognitive systems. These systems, which include the human brain, artificial neural networks, and other complex adaptive systems, process vast amounts of information in ways that are still being unraveled. Shannon's information theory offers a robust framework for analyzing the efficiency and capacity of these systems to encode, transmit, and store information.

The application of information theory to cognitive systems has led to significant advancements, such as optimizing neural coding strategies, understanding perception and decision-making processes, and modeling brain connectivity. This Special Issue seeks to highlight recent research and developments in this interdisciplinary field, addressing both theoretical and experimental perspectives.

Key areas of interest include, but are not limited to, the following:

  • The use of entropy and mutual information to quantify neural efficiency and redundancy.
  • Applications of the information bottleneck method to understand neural representation and processing.
  • Information-theoretic approaches to modeling learning and memory.
  • Bayesian inference and predictive coding in cognitive neuroscience.
  • The role of information theory in brain network dynamics and connectivity.
  • Novel statistical techniques based on Shannon’s information theory for non-stationary and complex data.
  • Empirical studies applying information theory to real-world cognitive systems.

This Issue aims to foster a deeper understanding of cognitive systems by integrating theoretical insights with empirical data, promoting the development of new techniques and the refinement of existing methods in information theory. Contributions that push the boundaries of how we interpret and utilize information theory in the context of cognitive neuroscience are highly encouraged.

Prof. Dr. Giovanni Petri
Prof. Dr. Jonathan D. Cohen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information theory
  • cognitive neuroscience
  • statistical mechanics
  • learning systems
  • artificial intelligence
  • neurobiological mechanisms
  • computational neuroscience
  • geometrical and topological information

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Published Papers (2 papers)

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Research

13 pages, 1239 KiB  
Article
Information Theory in Perception of Form: From Gestalt to Algorithmic Complexity
by Daniel Algom and Daniel Fitousi
Entropy 2025, 27(4), 434; https://doi.org/10.3390/e27040434 - 17 Apr 2025
Viewed by 223
Abstract
In 1948, Claude Shannon published a revolutionary paper on communication and information in engineering, one that made its way into the psychology of perception and changed it for good. However, the path to truly successful applications to psychology has been slow and bumpy. [...] Read more.
In 1948, Claude Shannon published a revolutionary paper on communication and information in engineering, one that made its way into the psychology of perception and changed it for good. However, the path to truly successful applications to psychology has been slow and bumpy. In this article, we present a readable account of that path, explaining the early difficulties as well as the creative solutions offered. The latter include Garner’s theory of sets and redundancy as well as mathematical group theory. These solutions, in turn, enabled rigorous objective definitions to the hitherto subjective Gestalt concepts of figural goodness, order, randomness, and predictability. More recent developments enabled the definition of, in an exact mathematical sense, the key notion of complexity. In this article, we demonstrate, for the first time, the presence of the association between people’s subjective impression of figural goodness and the pattern’s objective complexity. The more attractive the pattern appears to perception, the less complex it is and the smaller the set of subjectively similar patterns. Full article
(This article belongs to the Special Issue Information-Theoretic Principles in Cognitive Systems)
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20 pages, 1931 KiB  
Article
Information-Theoretic Measures of Metacognitive Efficiency: Empirical Validation with the Face Matching Task
by Daniel Fitousi
Entropy 2025, 27(4), 353; https://doi.org/10.3390/e27040353 - 28 Mar 2025
Cited by 1 | Viewed by 347
Abstract
The ability of participants to monitor the correctness of their own decisions by rating their confidence is a form of metacognition. This introspective act is crucial for many aspects of cognition, including perception, memory, learning, emotion regulation, and social interaction. Researchers assess the [...] Read more.
The ability of participants to monitor the correctness of their own decisions by rating their confidence is a form of metacognition. This introspective act is crucial for many aspects of cognition, including perception, memory, learning, emotion regulation, and social interaction. Researchers assess the quality of confidence ratings according to bias, sensitivity, and efficiency. To do so, they deploy quantities such as meta−d’-d′ or the M−ratio These measures compute the expected accuracy level of performance in the primary task (Type 1) from the secondary confidence rating task (Type 2). However, these measures have several limitations. For example, they are based on unwarranted parametric assumptions, and they fall short of accommodating the granularity of confidence ratings. Two recent papers by Dayan and by Fitousi have proposed information-theoretic measures of metacognitive efficiency that can address some of these problems. Dayan suggested meta−I and Fitousi proposed meta−U, meta−KL, and meta−J. These authors demonstrated the convergence of their measures on the notion of metacognitive efficiency using simulations, but did not apply their measures to real empirical data. The present study set to test the construct validity of these measures in a concrete behavioral task—the face-matching task. The results supported the viability of these novel indexes of metacognitive efficiency, and provide substantial empirical evidence for their convergence. The results also adduce considerable evidence that participants in the face-matching task acquire valuable metaknowledge about the correctness of their own decisions in the task. Full article
(This article belongs to the Special Issue Information-Theoretic Principles in Cognitive Systems)
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