A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs

• URL: http://arxiv.org/abs/2501.13620v3
• Date: Mon, 21 Apr 2025 11:03:43 GMT
• Title: A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs
• Authors: Mohit Vaishnav, Tanel Tammet,
• Abstract summary: This paper introduces a structured evaluation framework using Bongard Problems (BPs) to dissect the perception-reasoning interface in Vision-Language Models (VLMs)<n>We propose three distinct evaluation paradigms, mirroring human problem-solving strategies.<n>Our framework provides a valuable diagnostic tool, highlighting the need to enhance visual processing fidelity for achieving more robust and human-like visual intelligence in AI.
• Score: 3.2228025627337864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A fundamental challenge in artificial intelligence involves understanding the cognitive processes underlying visual reasoning in sophisticated models like Vision-Language Models (VLMs). How do these models integrate visual perception with abstract thought, especially when reasoning across multiple images? Drawing inspiration from cognitive science, this paper introduces a structured evaluation framework using Bongard Problems (BPs) - a classic test of visual abstraction to dissect the perception-reasoning interface in VLMs. We propose three distinct evaluation paradigms, mirroring human problem-solving strategies: Direct Visual Rule Learning (DVRL; holistic processing), Deductive Rule Learning (DRL; rule extraction and application), and Componential Analysis (CA; analytical decomposition via textual descriptions). These paradigms allow us to systematically vary the cognitive load and probe specific processing stages. Notably, the CA paradigm enables the evaluation of multi-image reasoning even in VLMs architecturally limited to single images and facilitates the isolation of reasoning capabilities from perceptual limitations by controlling the descriptive input. Ablation studies further confirm that reasoning abilities improve significantly when perceptual challenges are mitigated. Our framework provides a valuable diagnostic tool, highlighting the need to enhance visual processing fidelity for achieving more robust and human-like visual intelligence in AI.

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