Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density
This article presents Affinity, a visual analytics tool that enhances the simulation of the emergence of derived relations between stimuli in humans. Built on the foundations of a reinforcement learning model called Enhanced Equivalence Projective Simulation, Affinity provides both real-time visuali...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1618678/full |
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| author | James O'Sullivan Freddy Jackson Brown Oliver Ray |
| author_facet | James O'Sullivan Freddy Jackson Brown Oliver Ray |
| author_sort | James O'Sullivan |
| collection | DOAJ |
| description | This article presents Affinity, a visual analytics tool that enhances the simulation of the emergence of derived relations between stimuli in humans. Built on the foundations of a reinforcement learning model called Enhanced Equivalence Projective Simulation, Affinity provides both real-time visualizations of the agent's relational memory and enables the simulation of Relational Density Theory, a novel approach to understanding relational responding through the modeling of higher-order properties of density, volume, and mass. We demonstrate these features in a simulation of a recent study into the quantification of relational volume. We also use this as an opportunity to examine the effect of the underlying model's consolidation mechanism, Network Enhancement, on the agent's relational network. Our results highlight Affinity's innovation as an explainable modeling interface for relational formation and a testbed for new experiments. We discuss the limitations of Affinity in its current state, underline future work on the software and computational modeling of Stimulus Equivalence and locate this contribution in the broader scope of integrations of Contextual Behavioral Science and Artificial Intelligence. |
| format | Article |
| id | doaj-art-bafdafa93fef41db83f9a31eefa6a771 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-bafdafa93fef41db83f9a31eefa6a7712025-08-20T03:44:04ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.16186781618678Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational densityJames O'Sullivan0Freddy Jackson Brown1Oliver Ray2Department of Computer Science, University of Bristol, Bristol, United KingdomCentre for Research in Intellectual and Developmental Disabilities, University of Warwick, Coventry, United KingdomDepartment of Computer Science, University of Bristol, Bristol, United KingdomThis article presents Affinity, a visual analytics tool that enhances the simulation of the emergence of derived relations between stimuli in humans. Built on the foundations of a reinforcement learning model called Enhanced Equivalence Projective Simulation, Affinity provides both real-time visualizations of the agent's relational memory and enables the simulation of Relational Density Theory, a novel approach to understanding relational responding through the modeling of higher-order properties of density, volume, and mass. We demonstrate these features in a simulation of a recent study into the quantification of relational volume. We also use this as an opportunity to examine the effect of the underlying model's consolidation mechanism, Network Enhancement, on the agent's relational network. Our results highlight Affinity's innovation as an explainable modeling interface for relational formation and a testbed for new experiments. We discuss the limitations of Affinity in its current state, underline future work on the software and computational modeling of Stimulus Equivalence and locate this contribution in the broader scope of integrations of Contextual Behavioral Science and Artificial Intelligence.https://www.frontiersin.org/articles/10.3389/frai.2025.1618678/fullStimulus Equivalencecomputational modelingexplainable AIreinforcement learningrelational density theoryrelational frame theory |
| spellingShingle | James O'Sullivan Freddy Jackson Brown Oliver Ray Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density Frontiers in Artificial Intelligence Stimulus Equivalence computational modeling explainable AI reinforcement learning relational density theory relational frame theory |
| title | Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| title_full | Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| title_fullStr | Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| title_full_unstemmed | Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| title_short | Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| title_sort | elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density |
| topic | Stimulus Equivalence computational modeling explainable AI reinforcement learning relational density theory relational frame theory |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1618678/full |
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