We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more or less memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more or less memorable. The resulting "visual definitions" surface image properties (like "object size") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. Visit the GANalyze website at http://ganalyze.csail.mit.edu/.

听力损失是人类的重大健康问题和心理负担。小鼠模型提供了阐明参与潜在发育和病理生理机制的基因的可能性。为此，大规模的鼠标表型计划包括单基因敲除小鼠线的听觉表型。使用听觉脑干响应（ABR）程序，德国鼠标诊所和全球类似设施已经产生了大型均匀的突变体和野生型小鼠的ABR原料数据。在标准ABR分析过程中，听力阈值通过训练有素的工作人员从增加声压水平的信号曲线进行视觉评估。这是令人耗时的，并且容易被读者偏向，以及图形显示质量和规模。为了减少工作量并提高质量和再现性，我们开发并比较了两种方法，用于从平均ABR原始数据中实现自动听力阈值识别：一个受监督方法，涉及在人生成的标签和自我监督方法上训练的两个组合神经网络，利用信号功率谱利用信号功率谱并将随机森林声级估计与转换曲线拟合算法结合起来进行阈值查找。我们表明，两种型号都很好地，胜过人类阈值检测，并且适用于快速，可靠和无偏见的听力阈值检测和质量控制。在高通量鼠标表型环境中，两种方法都以自动端到端筛选管道的一部分表现良好，以检测用于听力参与的候选基因。两种模型的代码以及用于此工作的数据都可以自由使用。