Department of Brain and Cognition & Leuven Brain Institute, Leuven, Belgium.
Department of Neurobiology, Harvard Medical School, Boston, United States.
Elife. 2023 Dec 11;12:RP87719. doi: 10.7554/eLife.87719.
Many species are able to recognize objects, but it has been proven difficult to pinpoint and compare how different species solve this task. Recent research suggested to combine computational and animal modelling in order to obtain a more systematic understanding of task complexity and compare strategies between species. In this study, we created a large multidimensional stimulus set and designed a visual discrimination task partially based upon modelling with a convolutional deep neural network (CNN). Experiments included rats (N = 11; 1115 daily sessions in total for all rats together) and humans (N = 45). Each species was able to master the task and generalize to a variety of new images. Nevertheless, rats and humans showed very little convergence in terms of which object pairs were associated with high and low performance, suggesting the use of different strategies. There was an interaction between species and whether stimulus pairs favoured early or late processing in a CNN. A direct comparison with CNN representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. These findings highlight the additional value of using a computational approach for the design of object recognition tasks. Overall, this computationally informed investigation of object recognition behaviour reveals a strong discrepancy in strategies between rodent and human vision.
许多物种都能够识别物体，但要准确确定并比较不同物种如何解决这个问题一直很困难。最近的研究建议将计算和动物模型相结合，以便更系统地了解任务的复杂性，并比较物种之间的策略。在这项研究中，我们创建了一个大型多维刺激集，并设计了一个视觉辨别任务，部分基于卷积深度神经网络（CNN）的建模。实验包括大鼠（N=11；所有大鼠总共进行了 1115 个日常训练）和人类（N=45）。两个物种都能够掌握任务并推广到各种新的图像。然而，大鼠和人类在哪些物体对与高绩效和低绩效相关方面几乎没有趋同，这表明它们使用了不同的策略。在 CNN 中，物种和刺激对是否有利于早期或晚期处理之间存在相互作用。与 CNN 表示和视觉特征分析的直接比较表明，大鼠的性能最好由晚期卷积层以及亮度和像素级相似性等视觉特征来捕获，而人类的性能与更高的全连接层更相关。这些发现突出了使用计算方法设计物体识别任务的额外价值。总的来说，这种对物体识别行为的计算启发式研究揭示了啮齿动物和人类视觉之间策略的强烈差异。
