Feature-Rich Artificial Intelligence Models and Applications of Cognition

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 September 2024 | Viewed by 10410

Special Issue Editors


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Guest Editor
Department of Psychology and Cognitive Science, University of Trento, Corso Bettini 33, 38068 Rovereto, Italy
Interests: cognitive data science; complex networks; knowledge modelling; multiplex networks; natural language processing
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Guest Editor
Knowledge Discovery and Data mining Laboratory, Information Science and Technologies Institute, Italian National Research Council, 56124 Pisa, Italy
Interests: dynamic networks; community detection; diffusion processes; feature-rich networks; human mobility
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Computer Science, University of Pisa, 56126 Pisa, Italy
Interests: feature-rich networks; cognitive network science; community detection; natural language processing; dynamic networks

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a rapidly growing trend within cognitive sciences, with fields such as natural language processing, data mining and cognitive network science quickly revolutionizing how we build models of knowledge processing and understanding. To sustain this growth, there is an urgent need for next-generation models capable of managing multiple structural, associative, vectorial and qualitative patterns at once.

Feature-rich models are particularly promising, because they can simultaneously merge networks and non-network information among attributes on nodes, categories of connections or dynamic features. Feature-rich cognitive mining can result in the extraction of new knowledge that a classic network, data mining or natural language approaches alone could not highlight.

This Special Issue hopes to attract innovative publications regarding AI-based models grounded in feature-rich representations and of relevance for the investigation or simulation of cognition. Such methods can be inspired by the cognitive processing of knowledge, or display significant performance in cognition-related tasks, such as natural language understanding, text classification or word prediction tasks. In this Special Issue, we wish to include modeling approaches where feature-rich representations of data achieve significant performance boosts that would otherwise not be viable with other approaches or could not be easily interpreted within other modeling paradigms.

Potential topics include, but are not limited to, the following:

  • Mining of feature-rich datasets and AI;
  • Network science and AI for understanding cognitive representations;
  • Network psychometrics and soft computing for understanding mental health;
  • AI for exploration and exploitation processes in semantic search;
  • Complex system approaches to knowledge/information modeling;
  • AI for feature-rich stance detection;
  • AI applications to classification over textual corpora in clinical settings;
  • AI applications to classification over textual corpora in social media settings;
  • Opinion dynamics modeling;
  • Higher-order interactions and AI;
  • Methods for feature-rich complex networks;
  • Feature-rich community detection for cognitive networks;
  • Spreading activation and semantic diffusion in feature-rich networks;
  • Feature-rich word embedding representations;
  • Bias, polarization and ideology identification from social debates;
  • Psychometric features for automatic assessments of personality traits with AI.

Dr. Massimo Stella
Dr. Giulio Rossetti
Guest Editors
Salvatore Citraro
Guest Editor Assistant

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. Big Data and Cognitive Computing 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 1800 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

  • feature-rich models
  • artificial intelligence
  • cognitive computing
  • cognitive representations
  • knowledge modelling
  • feature-rich multivariate analysis
  • measurements of psychological constructs

Published Papers (2 papers)

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Research

24 pages, 4621 KiB  
Article
Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students
by Katherine Abramski, Salvatore Citraro, Luigi Lombardi, Giulio Rossetti and Massimo Stella
Big Data Cogn. Comput. 2023, 7(3), 124; https://doi.org/10.3390/bdcc7030124 - 27 Jun 2023
Cited by 11 | Viewed by 4667
Abstract
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing [...] Read more.
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them. Full article
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12 pages, 1753 KiB  
Article
A Mirror to Human Question Asking: Analyzing the Akinator Online Question Game
by Gal Sasson and Yoed N. Kenett
Big Data Cogn. Comput. 2023, 7(1), 26; https://doi.org/10.3390/bdcc7010026 - 29 Jan 2023
Cited by 3 | Viewed by 4416
Abstract
Question-asking is a critical aspect of human communications. Yet, little is known about the reasons that lead people to ask questions, which questions are considered better than others, or what cognitive mechanisms allow the ability to ask informative questions. Here, we take a [...] Read more.
Question-asking is a critical aspect of human communications. Yet, little is known about the reasons that lead people to ask questions, which questions are considered better than others, or what cognitive mechanisms allow the ability to ask informative questions. Here, we take a first step towards investigating human question-asking. We do so by an exploratory data-driven analysis of the questions asked by Akinator, a popular online game of a genie who asks questions to guess the character that the user is thinking of. We propose that the Akinator’s question-asking process may be viewed as a reflection of how humans ask questions. We conduct an exploratory data analysis to examine different strategies for the Akinator’s question-asking process, ranging from mathematical algorithms to gamification-based considerations, by analyzing complete games and individual questions. Furthermore, we use topic-modelling techniques to explore the topics of the Akinator’s inquiries and map similar questions into clusters. Overall, we find surprising aspects of the specificity and types of questions generated by the Akinator game, that may be driven by the gamification characteristics of the game. In addition, we find coherent topics that the Akinator retrieves from when generating questions. Our results highlight commonalities in the strategies for question-asking used by people and by the Akinator. Full article
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