ArticlePerspectives on behavior science2020
Relational Density Theory: Nonlinearity of Equivalence Relating Examined through Higher-Order Volumetric-Mass-Density.
Article in Perspectives on behavior science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- A Rapid Review of Training Future Behavior Analysts Through Computer-Generated Simulations: Challenges and Opportunities in Using Artificial Intelligence and Virtual Reality.Behavior analysis in practice · 2026Review
- Flexible Teachers, Thriving Classrooms: A Unified Flexibility and Mindfulness (UFM) Model of Classroom Dynamics, Teaching Practices, and Teacher Burnout.Behavioral sciences (Basel, Switzerland) · 2026Article
- Editorial: AI and neuroscience: integrating knowledge, reasoning, and theory of mind.Frontiers in computational neuroscience · 2026Article
- On the Disruptive Effects of Behavior Analysis on Behavior Analysis: The High Cost of Keeping Out Acceptance and Commitment Therapy and Training.Behavior analysis in practice · 2025Article
- Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density.Frontiers in artificial intelligence · 2025Article
- The Shape of Relations to Come: Multidimensional Analyses of Complex Human Behavior.The Psychological record · 2024Article
- Analyzing the Functional Interdependence of Verbal Behavior with Multiaxial Radar Charts.Perspectives on behavior science · 2024Article
- A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind.Frontiers in computational neuroscience · 2024Article
- Top-Down Attentional Processing and Relational Density Evident in Word Search Performance of Children.Behavior analysis in practice · 2022Article
- Derived Relations and Meaning in Responding to Art.Perspectives on behavior science · 2022Article
- Relational Behavior and ACT: A Dynamic Relationship.Behavior analysis in practice · 2022Article
- Relational Verbal Behavior and Eco-Friendly Purchasing: A Preliminary Translational Analysis and Implications.Behavior and social issues · 2022Article
- Selection by consequence: A response to Hayes and Fryling (2019).Journal of contextual behavioral science · 2021Review
- Model Dependent Realism and the Rule-Governed Behavior of Behavior Analysts: Applications to Derived Relational Responding.Perspectives on behavior science · 2020Article
- Derived Relational Responding and Transformations of Function in Children: A Review of Applied Behavior-Analytic Journals.The Analysis of verbal behavior · 2020Article
- Editorial: What Sidman Did -- Historical and Contemporary Significance of Research on Derived Stimulus Relations.Perspectives on behavior science · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose relational density theory, as an integration of stimulus equivalence and behavioral momentum theory, to predict the nonlinearity of equivalence responding of verbal humans. Consistent with Newtonian classical mechanics, the theory posits that equivalence networks will demonstrate the higher order properties of density, volume, and mass. That is, networks containing more relations (volume) that are stronger (density) will be more resistant to change (i.e., contain greater mass; mass = volume * density). Data from several equivalence experiments that are not easily interpreted through existing accounts are described in terms of the theory, generating predictable results in most cases. In addition, we put forward the higher-order properties of relational acceleration and gravity, which follow directly from the theory and may inspire future researchers to evaluate the seemingly self-organizing nature of human cognition. Finally, we conclude by describing avenues for real-world translation, considering past research interpreted through relational density theory, and call for basic experimental research to validate and extend core theoretical assumptions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.