paper:doi-10-3389-fnhum-2014-00160A formal model of interpersonal inference
Original abstract (expand)
INTRODUCTION: We propose that active Bayesian inference-a general framework for decision-making-can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. METHOD: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. RESULTS: (1) Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to "mentalizing" in the psychological literature, is based upon the outcomes of interpersonal exchanges. (2) We show how some well-known social-psychological phenomena (e.g., self-serving biases) can be explained in terms of active interpersonal inference. (3) Mentalizing naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one's own qualities and preferences. CONCLUSION: We inaugurate a Bayes optimal framework for modeling intersubject variability in mentalizing during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalizing is distorted.
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- ≈ 75%
- SocialNLI: A Dialogue-Centric Social Inference DatasetKate Sanders, Benjamin Van Durme Akhil Deo2025≈ 74%
- Interactive inference: a multi-agent model of cooperative joint actionsFrancesco Donnarumma, Giovanni Pezzulo Domenico Maisto2024≈ 72%
- Relations World: A Possibilistic Graphical ModelErin Renshaw, and Andrzej Pastusiak Christopher J.C. Burges2014≈ 72%
- Modeling Human Behavior Part I -- Learning and Belief ApproachesAndrew Fuchs and Andrea Passarella and Marco Conti2022≈ 72%
- Bayesian Inference of Social Norms as Shared Constraints on BehaviorZhi-Xuan Tan and Desmond C. Ong2019≈ 72%
- ≈ 72%
- Interactive Inference: A Neuromorphic Theory of Human-Computer InteractionTimothy Merritt, Saul Greenberg, Aneesh P. Tarun, Zhen Li, Zafeirios Fountas Roel Vertegaal2026≈ 72%
- Perceptions of Linguistic Uncertainty by Language Models and HumansMarkelle Kelly, Mark Steyvers, Sameer Singh, Padhraic Smyth Catarina G Belem2024≈ 71%
- Active Inference and Human--Computer InteractionJohn H. Williamson, Sebastian Stein Roderick Murray-Smith2024≈ 71%
- Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and CreativityTakato Horii Kentaro Nomura2026≈ 71%
- ≈ 71%
- A Computational Model of Crowds for Collective IntelligencePiper Jackson, and Thai Nguyen John Prpic2014≈ 71%
- Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others in Conversation ForecastingAnthony Sicilia and Malihe Alikhani2024≈ 71%
- Language and Experience: A Computational Model of Social Learning in Complex TasksTracey Mills, Ben Prystawski, Michael Henry Tessler, Noah Goodman, Jacob Andreas, Joshua Tenenbaum C\'edric Colas2026≈ 71%
- ≈ 67%
- ≈ 66%
- ≈ 66%
- Active Inference, Curiosity and Insightin corpus2017≈ 65%
- ≈ 65%
- ≈ 65%
- Cognitive glues are shared models of relative scarcities: the economics of collective intelligencein corpus2026≈ 65%
- Quantitative Introspection in Language Models: Tracking Emotive States Across Conversationin corpus2026≈ 65%
- ≈ 64%
- ≈ 64%
- Evaluating Language Model Character Traitsin corpus2024≈ 64%
- The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetsin corpus2023≈ 63%
- When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Modelsin corpus2025≈ 63%
Similar preprints — Semantic Scholar
Cited by (4)
- Active Inference, Curiosity and Insight
Minimizing expected variational free energy under a discrete-state Markov decision process generative model is sufficient to produce curiosity, epistemic learning, and insight without any additional m
- Active inference: demystified and compared
Active inference agents operating under expected free energy minimization achieve 98.90 [98.00, 99.79] average score in a non-stationary FrozenLake OpenAI gym environment, compared to 64.39 [60.33, 68
- Active Inference: A Process Theory
A single variational principle—minimizing variational free energy via gradient descent on a Markov decision process (MDP) generative model—is sufficient to derive neuronal dynamics that reproduce, wit
- Active inference on discrete state-spaces: a synthesis
Active inference on discrete state-spaces, formalized as partially observable Markov decision processes (POMDPs) with likelihood matrix A, transition matrix B, and prior D, unifies perception, plannin