# A Lexical T‑Maze for Active Inference: A Proof‑of‑Concept Model of Context Seeking Under Ambiguity Presenter: Ahmad Nazzal Contribution: Pre-recorded Video presentation Public ID: talk-d68bd36185ba7570 ## Submitted abstract Lexical‑disambiguation tasks with supplied context do not reveal whether readers would choose to seek evidence before deciding. Therefore, we adapted the spatial T‑maze in the active‑inference package pymdp to show how agents seek and use contextual information under lexical uncertainty. Agents could choose between two readings or request a contextual hint. We performed two main sets of simulations. First, deterministic simulations used fully informative hints to reproduce context seeking followed by lexical commitment. A hint shifted belief over two readings from equal probabilities (0.50, 0.50) to certainty (1.00, 0.00), reducing entropy by one bit. Second, lexical decision trials were repeatedwith informativeanduninformative hints across 32 paired simulationruns. Agents learned the relationship between the intended reading and the observed hint. Informative experience increased both the probability and simulated frequency of requesting context. This increase persisted when trials ended after one response and feedback. A hint information‑gain ablation, in which policies were rescored without crediting the information expected from hints, eliminated the learning‑related increase in hint‑request probability. These simulations demonstrate how experience changes the expected informational value of context within a lexical task, providing a computational proof of concept for subsequent behavioral experiments. Source: [submitted source]() Abstract SHA256: cba3606cc6e2fe01a7544b42973a27aa0950f042661362b119bfee12223bc968 ## Organizer review checklist - [ ] Confirm the submitted title, presenter, and abstract with the presenter. - [ ] Confirm contribution format and scheduling needs. - [ ] Confirm public permissions for any additional material before publication. - [ ] Prepare discussion questions grounded in the submitted abstract. ## Public learning resources These approved event resources are starting points; topic relevance and author identity have not been inferred. - Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology - Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations - GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER - Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=A%20Lexical%20T%E2%80%91Maze%20for%20Active%20Inference%3A%20A%20Proof%E2%80%91of%E2%80%91Concept%20Model%20of%20Context%20Seeking%20Under%20Ambiguity