CoCoG-2：用于理解人类概念表征的可控视觉刺激生成
CoCoG-2: Controllable generation of visual stimuli for understanding human concept representation
AI总结：
提出CoCoG-2框架，将可控视觉刺激生成融入相似性判断任务，利用无需训练的引导算法增强灵活性，以验证实验假设并推进概念表征与行为因果关系的理解。
摘要：
人类使用抽象概念来解释复杂的视觉刺激，这些概念有助于诸如食物选择和风险规避等决策任务。相似性判断任务是探索这些概念的有效方法。然而，在概念空间中进行可控图像生成的方法尚不完善。在本研究中，我们提出了一个名为CoCoG-2的新框架，该框架将生成的视觉刺激整合到相似性判断任务中。CoCoG-2利用一种无需训练的引导算法来增强生成的灵活性。CoCoG-2框架适用于基于人类概念创建实验刺激，支持多种引导视觉刺激生成的策略，并展示了这些刺激如何验证各种实验假设。通过生成视觉刺激，CoCoG-2将推进我们对概念表征与行为之间因果关系的理解。代码可在\newline https://github.com/ncclab-sustech/CoCoG-2获取。
英文摘要：
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 \url{https://github.com/ncclab-sustech/CoCoG-2}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
