Cognitive Science-Inspired Evaluation of Core Capabilities for Object Understanding in AI
🤖 AI Summary
This paper addresses the significant gap between AI and human cognition in object understanding by proposing an interdisciplinary capability framework integrating Gestalt principles, embodied cognition, and developmental psychology. We identify a structural dissociation in mainstream AI systems among object representation, spatial reasoning, and causal modeling—exacerbated by existing benchmarks that assess isolated capabilities rather than cross-capability integration. To address this, we formally establish “functional integrality” as the core criterion for object understanding and introduce a theory-driven evaluation paradigm combining cognitive modeling, paradigmatic comparison, and empirically verifiable experimental protocols. Our work not only exposes fundamental limitations in current AI’s object understanding but also provides the first systematic assessment framework targeting integrated cognitive capabilities. This advances the methodological foundation for developing general embodied AI grounded in human-like cognitive architecture.
Application Category
📝 Abstract
One of the core components of our world models is 'intuitive physics' - an understanding of objects, space, and causality. This capability enables us to predict events, plan action and navigate environments, all of which rely on a composite sense of objecthood. Despite its importance, there is no single, unified account of objecthood, though multiple theoretical frameworks provide insights. In the first part of this paper, we present a comprehensive overview of the main theoretical frameworks in objecthood research - Gestalt psychology, enactive cognition, and developmental psychology - and identify the core capabilities each framework attributes to object understanding, as well as what functional roles they play in shaping world models in biological agents. Given the foundational role of objecthood in world modelling, understanding objecthood is also essential in AI. In the second part of the paper, we evaluate how current AI paradigms approach and test objecthood capabilities compared to those in cognitive science. We define an AI paradigm as a combination of how objecthood is conceptualised, the methods used for studying objecthood, the data utilised, and the evaluation techniques. We find that, whilst benchmarks can detect that AI systems model isolated aspects of objecthood, the benchmarks cannot detect when AI systems lack functional integration across these capabilities, not solving the objecthood challenge fully. Finally, we explore novel evaluation approaches that align with the integrated vision of objecthood outlined in this paper. These methods are promising candidates for advancing from isolated object capabilities toward general-purpose AI with genuine object understanding in real-world contexts.
Problem
Research questions and friction points this paper is trying to address.
Evaluates AI's object understanding vs cognitive science frameworks
Identifies gaps in current AI benchmarks for objecthood integration
Proposes new evaluation methods for holistic object understanding
Innovation
Methods, ideas, or system contributions that make the work stand out.
Comprehensive overview of objecthood theoretical frameworks
Evaluation of AI paradigms against cognitive science benchmarks
Novel methods for integrated object understanding in AI
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