2024/07/20 by Chen Wei, Jiachen Zou, Wei, Chen +5 · 3 citations
Computer Science · Engineering · Psychology · #Advanced Text Analysis Techniques #Color perception and design #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Neurons and Cognition (q-bio.NC) #Robotics and Automated Systems
paper · pdf · doi:10.48550/arxiv.2407.14949
openalex publication_date 2024/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Humans interpret complex visual stimuli using abstract concepts that facilitate decision-making tasks such as food selection and risk avoidance. Similarity judgment tasks are effective for exploring these concepts. However, methods for controllable image generation in concept space are underdeveloped. In this study, we present a novel framework called CoCoG-2, which integrates generated visual stimuli into similarity judgment tasks. CoCoG-2 utilizes a training-free guidance algorithm to enhance generation flexibility. CoCoG-2 framework is versatile for creating experimental stimuli based on human concepts, supporting various strategies for guiding visual stimuli generation, and demonstrating how these stimuli can validate various experimental hypotheses. CoCoG-2 will advance our understanding of the causal relationship between concept representations and behaviors by generating visual stimuli. The code is available at \urlhttps://github.com/ncclab-sustech/CoCoG-2.