Attention to task structure for cognitive flexibility

2026-04-14

AI Summary

This study investigates the mechanisms underlying cognitive flexibility in dynamic multitask environments—specifically, how agents can effectively generalize to novel tasks while preserving the stability of previously acquired knowledge. To this end, the authors construct a multitask learning environment based on dual cue dimensions and introduce, for the first time, graph-theoretic measures to characterize the connectivity structure among tasks. They propose an attention-based model integrating gating and concatenation mechanisms that decomposes task structure and allocates attention sequentially, enabling a systematic analysis of how environmental structure and model architecture jointly shape cognitive flexibility. Experimental results demonstrate that environmental richness and task connectivity significantly modulate the trade-off between stability and generalization, with the proposed attention model exhibiting particularly pronounced advantages in highly connected task environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Multimodal Learning
• Multiagent Systems: Mechanism Design

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Humans and artificial agents must often learn and switch between multiple tasks in dynamic environments. Success in such settings requires cognitive flexibility: the ability to retain prior knowledge (cognitive stability) while also transferring it to novel tasks (cognitive generalization). Cognitive flexibility research has largely focused on the role of model architecture to achieve these complementary goals. However, it is less well understood how the structure of the environment itself influences cognitive flexibility, and how it interacts with model architecture. To address this gap, we design a multi-task learning environment in which tasks are defined by a combination of two cue dimensions, allowing us to characterize the environment with graph-theory methods. We also introduce gating-based (multiplicative) and concatenation-based attention models that can decompose tasks into components and can sequentially allocate attention to them. We compare the attention-based models' performance in the multi-task learning environment to multilayer perceptrons. Generalization and stability are systematically evaluated across environments that vary in richness and task connectivity. We observe that richer environments improve both generalization and stability. In addition, a critical novel observation is that (graph theory based) connectivity between the tasks in the environment strongly modulates both stability and generalization, with especially pronounced benefits for attention-based models. These findings underscore the importance of considering not only cognitive architectures but also environmental structure and their interaction in shaping multi-task learning, generalization, and stability.

Problem

• cognitive flexibility
• environmental structure
• multi-task learning
• task connectivity
• generalization

Innovation

• cognitive flexibility
• task structure
• graph-theoretic connectivity
• attention mechanisms
• multi-task learning