Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia
- URL: http://arxiv.org/abs/2605.30708v1
- Date: Fri, 29 May 2026 01:00:38 GMT
- Title: Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia
- Abstract summary: We evaluate the behavior of patterns in Lenia by introducing regions into their environment with no sensory information available.<n>We find that creatures tend to avoid these regions, a behavior we term agnosiophobia.<n>This work illustrates the beginning of an important roadmap to understand how emergent agents' behavioral propensities interact with the informational, not only tangible, topography of their world.
- Score: 1.7499351967216341
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: All embodied agents are fundamentally patterns in physiological or other excitable media, blurring the distinction between objects and processes. Emergent patterns with complex behaviors, such as Gliders in the Game of Life and virtual patterns in Lenia, are powerful model systems in which to understand the properties and origins of behavioral traits in novel agents. To evaluate the behavior of patterns in Lenia, we introduce regions into their environment from which no sensory information is available - in effect, making creatures blind to parts of their surroundings. Complementing the conventional concept of infotaxis, we find that creatures tend to avoid these regions, a behavior we term agnosiophobia. To explain this behavior, we map each test creature's sensitivity to targeted occlusions and interpret the results in the language of dynamical systems. We observe Lenia creatures taking advantage of their freedom to change heading in order to achieve what appears to be a more fundamental goal: the preservation of their morphology. This work illustrates the beginning of an important roadmap to understand how emergent agents' behavioral propensities interact with the informational, not only tangible, topography of their world.
Related papers
- Agent-Centric Animal Pose Forecasting [0.1494633072572191]
We introduce a framework for training agent-centric autoregressive models of animal behavior.<n>We show that trained models capture the distribution of social behavior in groups of courting Drosophila.<n>We release a general-purpose library focused on the composable sequences of operations that translate between these representations.
arXiv Detail & Related papers (2026-07-21T19:48:21Z) - NeuroCogMap Reveals Cognitive Organization of Large Language Models [82.70079471723963]
We present NeuroCogMap, a cognitive neuroscience-inspired framework that organizes internal features of large language models into functional parcels.<n>These parcels form a stable and semantically coherent organization that is partly conserved across models.<n>These findings establish NeuroCogMap as a system-level framework for mapping functional organization in artificial systems.
arXiv Detail & Related papers (2026-07-01T03:48:49Z) - A Mechanistic Model for Collective Motion from Sensorimotor Regularities [6.805575417034369]
We present a model based on a modeling framework from robotics, extended here to collective motion.<n>This simple model produces diverse collective behaviors including polarized motion, milling, ring formations, and fragmentation.<n>Collective behavior can therefore be understood as the emergent outcome of interacting sensorimotor regularities.
arXiv Detail & Related papers (2026-05-15T18:17:41Z) - The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
arXiv Detail & Related papers (2025-09-03T21:27:10Z) - Grounding Intelligence in Movement [0.9895793818721335]
Recent advances in machine learning have dramatically improved our ability to model language, vision, and other high-dimensional data.<n>Across neuroscience, medicine, robotics, and ethology, movement is essential for interpreting behavior, predicting intent, and enabling interaction.<n>We argue that movement should be treated as a primary modeling target for AI.
arXiv Detail & Related papers (2025-07-03T16:34:34Z) - Avoiding Death through Fear Intrinsic Conditioning [48.07595141865156]
We introduce an intrinsic reward function inspired by early amygdala development and produce this intrinsic reward through a novel memory-augmented neural network architecture.<n>We show how this intrinsic motivation serves to deter exploration of terminal states and results in avoidance behavior similar to fear conditioning observed in animals.
arXiv Detail & Related papers (2025-06-05T19:24:51Z) - Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning [69.71072181304066]
We introduce Perceptive Dexterous Control (PDC), a framework for vision-driven whole-body control with simulated humanoids.<n>PDC operates solely on egocentric vision for task specification, enabling object search, target placement, and skill selection through visual cues.<n>We show that training from scratch with reinforcement learning can produce emergent behaviors such as active search.
arXiv Detail & Related papers (2025-05-18T07:33:31Z) - The world seems different in a social context: a neural network analysis
of human experimental data [57.729312306803955]
We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals.
An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
arXiv Detail & Related papers (2022-03-03T17:19:12Z) - Inference of Affordances and Active Motor Control in Simulated Agents [0.5161531917413706]
We introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture.
We show that our architecture develops latent states that can be interpreted as affordance maps.
In combination with active inference, we show that flexible, goal-directed behavior can be invoked.
arXiv Detail & Related papers (2022-02-23T14:13:04Z) - How to build a cognitive map: insights from models of the hippocampal
formation [0.45880283710344055]
The concept of a cognitive map has emerged as one of the leading metaphors for these capacities.
unravelling the learning and neural representation of such a map has become a central focus of neuroscience.
arXiv Detail & Related papers (2022-02-03T16:49:37Z) - Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
arXiv Detail & Related papers (2021-12-07T18:50:42Z) - Overcoming the Domain Gap in Contrastive Learning of Neural Action
Representations [60.47807856873544]
A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior.
We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies.
This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
arXiv Detail & Related papers (2021-11-29T15:27:51Z) - Curiosity-driven Intuitive Physics Learning [0.0]
We propose a model for curiosity-driven learning and inference for real-world AI agents.
This model is based on the arousal of curiosity, deriving from observations along discontinuities in the fundamental macroscopic solid-body physics parameters.
The model aims to support the emulation of learning from scratch followed by substantiation through experience, irrespective of domain.
arXiv Detail & Related papers (2021-05-16T12:58:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.