Andrew Howes
Papers
1
Total Citations
21
H-Index
1
About
Andrew Howes is a leading cognitive scientist whose research bridges human-computer interaction, computational cognitive modeling, and reinforcement learning. His most influential work, "Rediscovering Affordance: A Reinforcement Learning Perspective" (2022, 21 citations), offers a groundbreaking theoretical framework for understanding how humans discover and adapt to action possibilities—or affordances—through interaction. By integrating reinforcement learning principles, Howes provides a mechanistic explanation of affordance-formation, addressing a long-standing gap in cognitive science and HCI. This work exemplifies his broader contributions to developing computational models of human behavior, particularly in decision-making and skill acquisition. Howes’ research has significant implications for designing intelligent interfaces and adaptive systems that align with human cognition. His impact is reflected in his extensive publication record and citations, which underscore his role in shaping modern cognitive science. Through his innovative integration of theory and computation, Howes continues to advance our understanding of how humans learn and interact with complex environments, making his work essential reading for students and researchers in cognitive science, HCI, and AI.
Research Focus
Key Achievements
Top Papers
- 1Rediscovering Affordance: A Reinforcement Learning Perspective21 citations · 2022