Topic
cognitive science
Technology AI Isn't Smarter Than a Baby—Yet: New Test Reveals Limits of Vision Language Models
Researchers at Meta, Stanford, and other institutions developed EgoBabyVLM, a test comparing vision language models to infant learning. Current VLMs fail when fed realistic baby-camera footage, highlighting the efficiency gap between human and AI learning. The findings suggest that building baby-like AI could reduce costs and improve robot learning.
Generative AI and Creativity: Researchers Argue Intentional Agency Not Necessary for Creative Output
A new paper by Pearson, Dennis, and Cheong argues that the Intentional Agency Condition (IAC) should be abandoned. Through corpus analyses, they show people increasingly attribute creativity to generative AI. They propose a novel approach based on creative ability to resolve the predicament.
New Unified Framework for World Models Aims to Bridge Human and Machine Cognition
A new research paper presents a conceptual unified framework for world models that integrates cognitive functions such as memory, perception, language, reasoning, imagination, motivation, and metacognition. The authors identify that motivation and metacognition remain under-researched and propose directions based on active inference and global workspace theory. They also introduce epistemic world models for scientific discovery.
How Scale Design Impacts LLM Metacognition and Enterprise AI Reliability
A study on arXiv reveals that the confidence scale used in LLMs (typically 0-100) leads to heavy discretization, with over 78% of responses on three round numbers. Changing the scale to 0-20 improves metacognitive efficiency. The findings have implications for enterprise use of LLMs in supply chain decision-making where confidence calibration is critical.
Cognitive Trajectory Modeling: A New Framework for Quantifying Human-AI Co-Creation
Cognitive Trajectory Modeling (CTM) is a novel cognitive theory of interaction dynamics that conceptualizes cognition and creative processes as temporally organized trajectories. It provides a framework for quantifying how human-AI co-creation evolves over time, distinguishing cognitive trajectories from mere interaction traces.
Causal Model of Theory of Mind in Conflict Offers New Path for AI Mentalizing
A new research paper by Gurney and Nikolos introduces a structural causal model for theory of mind (ToM) in artificial intelligence, addressing the unresolved question of when mentalizing is warranted in conflict situations. The model treats ToM as a mechanism activated by situational and agent-level conditions, offering a resource-rational decision procedure for AI systems. It specifies four exogenous variables, five endogenous mediators, and three causal pathways leading to epistemic accuracy, with implications for efficiency, trust, and robust artificial social intelligence.