A central question for cognitive science is to understand how humans process
visual objects, i.e, to uncover human low-dimensional concept representation
space from high-dimensional visual stimuli. Generating visual stimuli with
controlling concepts is the key. However, there are currently no generative
models in AI to solve this problem. Here, we present the Concept based
Controllable Generation (CoCoG) framework. CoCoG consists of two components, a
simple yet efficient AI agent for extracting interpretable concept and
predicting human decision-making in visual similarity judgment tasks, and a
conditional generation model for generating visual stimuli given the concepts.
We quantify the performance of CoCoG from two aspects, the human behavior
prediction accuracy and the controllable generation ability. The experiments
with CoCoG indicate that 1) the reliable concept embeddings in CoCoG allows to
predict human behavior with 64.07\% accuracy in the THINGS-similarity dataset;
2) CoCoG can generate diverse objects through the control of concepts; 3) CoCoG
can manipulate human similarity judgment behavior by intervening key concepts.
CoCoG offers visual objects with controlling concepts to advance our
understanding of causality in human cognition. The code of CoCoG is available
at https://github.com/ncclab-sustech/CoCoG.

理解人类是如何处理视觉对象的是认知科学的一个核心问题，这篇研究利用 Concept based Controllable Generation (CoCoG) 框架，通过两个组成部分 —— 提取可解释概念并预测人类在视觉相似性判断任务中的决策行为的 AI 代理以及给定概念生成视觉刺激的条件生成模型，评估了 CoCoG 的性能，结果显示 CoCoG 能够准确预测人类行为、生成多样的对象以及通过干预关键概念操纵人类相似性判断行为，为深入理解人类认知中的因果关系提供了具有控制概念的视觉对象。

基于人类概念代表的可控视觉刺激生成

CoCoG: Controllable Visual Stimuli Generation based on Human Concept  Representations

In traditional machine teaching, a teacher wants to teach a concept to a
learner, by means of a finite set of examples, the witness set. But concepts
can have many equivalent representations. This redundancy strongly affects the
search space, to the extent that teacher and learner may not be able to easily
determine the equivalence class of each representation. In this common
situation, instead of teaching concepts, we explore the idea of teaching
representations. We work with several teaching schemas that exploit
representation and witness size (Eager, Greedy and Optimal) and analyze the
gains in teaching effectiveness for some representational languages (DNF
expressions and Turing-complete P3 programs). Our theoretical and experimental
results indicate that there are various types of redundancy, handled better by
the Greedy schema introduced here than by the Eager schema, although both can
be arbitrarily far away from the Optimal. For P3 programs we found that witness
sets are usually smaller than the programs they identify, which is an
illuminating justification of why machine teaching from examples makes sense at
all.

传统机器教学中，概念的教学受到其多种等价表示的影响。本文通过研究各种教学方案，分析了表达方式和证据集大小对教学效果的影响，结果显示 Greedy 方案比 Eager 方案更有效，但两者都无法达到最优。对于 P3 程序而言，证据集通常比其识别的程序更小，这为从示例进行机器教学提供了有意义的解释。

冗余的重要性：表示机器教学

When Redundancy Matters: Machine Teaching of Representations

This paper analyses a visual archive of drawings produced by an interactive
robotic art installation where audience members narrated their dreams into a
system powered by CLIPdraw deep learning (DL) model that interpreted and
transformed their dreams into images. The resulting archive of prompt-image
pairs were examined and clustered based on concept representation accuracy. As
a result of the analysis, the paper proposes four groupings for describing and
explaining CLIP-generated results: clear concept, text-to-text as image,
indeterminacy and confusion, and lost in translation. This article offers a
glimpse into a collection of dreams interpreted, mediated and given form by
Artificial Intelligence (AI), showcasing oftentimes unexpected, visually
compelling or, indeed, the dream-like output of the system, with the emphasis
on processes and results of translations between languages, sign-systems and
various modules of the installation. In the end, the paper argues that proposed
clusters support better understanding of the neural model.

本文分析了一个视觉档案，其中互动机器人艺术装置根据观众讲述的梦境生成图像，通过 CLIPdraw 深度学习模型解释和转换。结果表明四个概念分组描述和解释了 CLIP 生成的结果：清晰的概念，文本转图像，不确定和混乱以及翻译问题。最终，文章认为所提出的分组支持对神经模型的更好理解。