Summary
This paper describes a new mathematical model that helps artificial systems behave like a motivated and emotional mind. The model shows how such systems can learn from internal needs, feelings, and memories to make choices that keep their inner balance. It combines signals from the outside world, internal body states, and memories to form linked representations called semblions, which then decide what the system should focus on next. The authors’ approach goes beyond usual learning methods by including how needs change, how goals shift, and how uncertainty affects decisions. This work aims to provide a clearer way to study and build artificial intelligence that acts more like living beings.
What this means in practice
- •For robotics engineers: Design embodied robots that adaptively regulate internal states and make decisions based on needs and emotions.
- •For video game developers: Create non-player characters with more human-like motivated behaviors influenced by internal needs and emotions.
A theory result. No direct application yet.
Abstract
This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems. Such a system learns to maintain its homeostasis through a generalized form of reinforcement learning based on its internal motivations, termed motivated learning (ML). The principal contribution of this article is a rigorous formalization of the re-entrant loop integrating feedforward processing, lateral interactions, and feedback pathways, together with the representational selection mechanisms that govern adaptive system responses. The model specifies how ongoing exteroceptive and interoceptive signals, bodily-motivational context, and memory traces are bound into associative memory structures termed semblions, which compete for access to further processing and top-down reconstruction. The formalization encompasses secondary perception, representational competition, curiosity, procedural gaps, and action selection directed toward limiting allostatic violations. Within this framework, motivated learning is tailored to embodied systems whose dynamics are shaped by needs, affect, and the current regulatory state. Unlike standard reinforcement-learning models, the proposed approach incorporates need thresholds, goal generation and shifting goals, bodily state, resource constraints, and action uncertainty, thereby providing a more adequate account of response selection under regulatory pressure. Global affect functions as a central control signal, modulating the learning rate, representational valence, and the balance between exploration and exploitation. The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.